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Defense-In-Depth Against Computer Viruses

Fred Cohen
Computers and Security, Volume 11, Issue 6, pp. 563-579
ISSN 0167-4048
October 1992

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Index terms: Coding theory, cryptography, fault tolerant computing, error detection codes, error correction codes, computer viruses, testing, computer security, computer integrity, operating systems, integrity shells, software based fault tolerance, recovery blocks, information theory, data compression.

Abstract

In this paper, we discuss software based fault tolerant computing techniques used in defense against computer viruses and other integrity corruptions in modern computer systems. We begin with a summary of research on computer viruses, their potential for harm, and the extent to which this potential has been realized to date. We then examine major results on the application of fault tolerant software techniques for virus defense, including; the problems with conventional coding schemes in detecting intentional corruptions and the use of high performance cryptographic checksums for reliable detection; an optimal method for detecting viruses and preventing their further spread in untrusted computing environments; the use of redundancy and automated decision making for automatic and transparent repair of corruption and continuity of operation; and the use of fault avoidance techniques for limiting viral spread. Next we discuss the state-of-the-art in virus defense, its use of redundancy for defense-in-depth, the impact of this on the reliability of the mechanism, the implications of these results to other computing environments, and architectural issues in implementing hardware assisted virus defense based on the software fault tolerance techniques already in widespread use. Finally we summarize results, draw conclusions, and discuss further work.

Copyright © 1991, Fred Cohen, ALL RIGHTS RESERVED, This research was funded by ASP, PO Box 81270, Pittsburgh, PA 15217, USA

1 Background

The “Computer Virus” problem was first described in 1984 [1], when the results of several experiments and substantial theoretical work showed that viruses could spread, essentially unhindered, even in the most ‘secure’ computer systems; that they could cause widespread and essentially unlimited damage with little effort on the part of the virus writer; that detection of viruses was undecidable; that many of the defenses that could be devised in relatively short order were ineffective against a serious attacker; and that the best defenses were limited transitivity of information flow, limited function so that Turing capability [2] is unavailable, and limited sharing.

In subsequent papers; it was shown that limited sharing, in the most general case, would cause the information flow in a system to form a partially ordered set of information domains [3]; it was proven that limiting of transitivity, functionality, and sharing were the only ‘perfect’ defenses [4]; and it was suggested that a complexity based defense against viruses might be practical [5]. It was also shown [4] that viruses could ‘evolve’ into any result that a Turing machine could compute, thus introducing a severe problem in detection and correction, tightening the connection between computer viruses and artificial life, and introducing the possibility that viruses could be a very powerful tool in parallel computing.

While initial laboratory results showed that viruses could attain all access to all information in a typical timesharing computer system with properly operating access controls in only 30 minutes on average [1] and that network spread would be very rapid and successful [7], experiments and analytical results were severely limited by the unwillingness of the research community to even allow statistical data to be used in assessing the potential risks. [1] Although substantial theoretical results indicated how quickly viruses might be expected to spread given an accurate characterization of an environment [6], and an experiment at the University of Texas at El Passo showed that in a standard IBM PC network, a virus could spread to 60 computers in 30 seconds [7], actual spread rates could not be determined accurately until real-world attacks took place.

Real-world viruses started to appear in large numbers in 1987, when viruses apparently created in Pakistan, Israel, and Germany all independently spread throughout the world, causing thousands of computer systems to become unusable for short periods of time, hundreds of thousands of computers to display spurious messages, tens of thousands of users to experience denial of services, and several international networks to experience denial of services for short periods [7,8]. By 1988, there were about 20 well known and widely spread computer viruses, in early 1990, the IBM high integrity research laboratory reported over 125 unique viruses detected in the environment [9], and by March of 1991, between 200 and 600 unique real-world viruses were known in the research community [10], and over one new virus introduced into the global computing environment per day.1

In the period before viral attacks became widespread, there was little interest from the broader research community, and research results were considered of relatively little interest to funding agencies. Even though early results predicted many of the widespread implications we now see, very few organizations took any measures to defend themselves [11]. In the following years however, interest sprung up throughout the world research community, and there are now international computer virus conferences more than once a month, hundreds of university researchers, and tens of books on the subject. For more complete summaries of the field, the reader is referred to summary reports [19,20] and books [7,8] on the subject.

Most of the useful techniques for virus defense are based on basic results from fault-tolerant computing, with special consideration required to deal with defense against intentional attackers rather than random noise. In the remainder of this paper we will look at how software based fault-tolerant computing techniques have been used to deal with the computer virus problem.

2 A Multitude of Broken Defenses

Many defensive ideas have been examined for their viability in virus defense. The vast majority of them have failed to pan out because there are generic attacks against them, they produce infinite numbers of false positives and false negatives, or they are too costly to be effectively applied [7]. We now examine several of the well known ideas that are in widespread use even though we have known for some time about their vulnerabilities.

The most common virus defense is the so-called ‘scanner’, which examines computer files to detect known viruses. Scanners have several important problems that have a serious impact on their current and future viability as a defense, most notably: [7,16]

There are a number of variations on scanners, most notably the so-called ‘monitor’ [28], which is a variation on the ‘integrity shell’ technique described later in this paper [12,13], and which dramatically reduces the costs associated with detecting known viruses. [7,16]

Another interesting idea was the use of built-in self-test for detecting and possibly correcting viruses in interpreted information [5]. It turns out that all such mechanisms are vulnerable to a generic attack which is independent of the particular mechanism [7], but the concept of using complexity to make attack very difficult remains one of the most practical techniques. The generic attack against self-defense operates as follows:

Infection :=
  1. Find a program to infect (P)
  2. Rename P to P'
  3. Copy the virus (V) to P
Execution :=
  1. Rename the current infected program I to V
  2. Rename the original I (currently I') to I
  3. Run (the original) I
  4. Wait for I to complete
  5. Rename I to I'
  6. Rename V to I

In this example, the virus V ‘disinfects’ I for execution and reinfects I after execution. Thus, by the time I gets control for execution, I is the clean original copy of I. This method only requires that the ‘Rename’ operation doesn’t change any file related information, and thus that the program I cannot determine that it has been renamed to I' and back to I prior to execution. Although this attack works on all current systems, a slightly more generic form of this attack is achieved by replacing ‘Run’ in line 6 with ‘Simulate’. By performing an accurate simulation of the legitimate machine environment, we can eliminate all automated detection methods.

A third common technique is to ‘vaccinate’ a program against viruses by modifying the program so that the virus is ‘fooled into thinking’ that the program is already infected. This has several very important drawbacks, primarily that not all viruses have to check for previous infection, vaccinating against large numbers of viruses may require so many changes that the resulting program will not operate, and n-tuples of competing viruses may make vaccination impossible [7]. The later case is easily seen in the following example.


	V_1 := \text{Find a program }P\text{ to infect}\\
	\text{If }P^x \geq 25\text{, Infect}\\
	...\\
	V_2 := \text{Find a program }P\text{ to infect}\\
	\text{If }P^x \lt 25\text{, Infect}\\
	...

where P^x is an abbreviation for the value stored at the xth location in program P, and ‘. . .’ represents the remainder of the virus code.

In this example, we have two viruses V_1 and V_2, each of which performs infection if and only if the same value is in a different range. If we try to prevent V_1 from infecting P by setting P^x to a value under 25, then V_2 will be able to infect P, while setting P^x to a value of 25 or greater will allow V_1 to infect P. By vaccinating against V_1 we only provide more ‘food’ for V_2, and as V_2 ‘eats’ more of the available food, it in turn provides more food for V_1. Thus two viruses of this sort can result in a stable population ratio which returns to its stable state even when disturbed by widespread vaccination against one of the members. It is simple to create n-tuples of viruses with similar techniques, or even evolutionary viruses that vary through a potentially infinite number of variations. By varying the relative sizes of the ‘food space’, we can also control long term average population ratios.

Multiversion programming has also been suggested as a solution to the virus problem [1], and recent improvements in this technique have made this more and more feasible from an operational standpoint, [26] but the costs associated with these techniques make them tolerable only in a very limited number of environments [7], and it is unclear whether they will be useful in avoiding the effects of computer viruses because they don’t address the ability to discern between legitimate and illegitimate changes. An n-version virus could presumably infect (n + 1)/2 copies of the legitimate program, thus causing the voting technique to ‘kick out’ the legitimate program in favor of the virus [1].

3 Coding Techniques

Although precise virus detection is undecidable [4], we may be willing to suffer an infinite number of false positives and a very low probability of false negatives in order to have an effective defense. This can be achieved through the use of coding techniques which reliably detect changes. For example, a simple checksum or CRC code could be used to detect changes in files. The problem with many coding techniques is that they are easily forged, so that an attacker can easily make modifications which leave the code space unchanged [1]. The reason for this vulnerability is that many coding techniques are designed to detect corruptions due to random noise with particular characteristics, but they are not designed to detect malicious changes by intentional agents intent on bypassing them.

In the case of the simple checksum, forgery is trivial as follows:

mathematically:


V = \{v_0, \dots, v_j\}, j \in N\\
F = (b_1, \dots, b_n), n \in N, \forall kb_k \in V\\
S = \sum_{i=1}^n b_i | m, m \in N\\
F'= (c_1, \dots, c_p), p \in N, \forall kc_k \in V\\
S'= \sum^p_{i=1} c_i | m\\
F''=(c_1, \dots, c_p, x), x : (S' + x) | m = S

since addition in a modulus is associative, x is simply calculated as x=(S-S')|m.

With a checksum which incorporates the size of a file, forgery is still usually straight forward because the file can be compressed before attack, leaving additional space that can be filled with null information or information selected to forge a valid checksum. A compression virus of this sort has been demonstrated as a method of trading time for space in a computer system, and several current products exploit this technique for automatic in-memory decompression of executable files at runtime [1].

In the case of a CRC coded checksum, attack is quite similar:

If you don’t know the order of the equation ahead of time, assume a large number of variables (as many as you have sample CRC codes and files for), and solve the equation. If there is enough data, all irrelevant coefficients will be determined as 0. If not, there is insufficient data for a unique solution. Several techniques have been devised to use multiple CRC codes with pseudo-randomly generated or user provided coefficients, but these appear to be simple to attack as well.

Other major problems with CRC codes are that; selected plaintext attack is trivial; for small files, deriving CRC coefficients is trivial; and for ‘empty’ files, CRC codes may show the CRC coefficients directly. Some defenders have tried to use multiple CRC codes with different coefficients, but this provides no computational advantage to the defender, and further, may actually reduce the complexity of attack because common coefficients are trivially revealed.

Another possibility is the use of information content measures. In this method, we calculate the information content of a data set using Shannon’s method [30]. Unfortunately, for monograph content, it is trivial to switch two symbols without affecting the total content:


\begin{eqnarray}
	S	&=& \{s_1, \dots, s_n\}, n \in N 			& \text{Symbol set}\\
	F	&=& (f_1, \dots, f_m), m \in N, \forall i, f_i \in S	& \text{old file}\\
	H(F)	&=& \sum^m_{j=1} h(f_j)					& \text{content of old file}\\
	F'	&=& (f_1', \dots f_m'), m \in N, \forall i, f_i' \in S	& \text{new file}\\
		& & \forall x, y \leq m \exist M(x) : F(x) \Leftrightarrow F'(y) & \text{one-to-one onto}\\
	H(F')	&=& \sum ^m_{j=1} h(f_j')				& \text{content of new file}
\end{eqnarray}

where h is Shannon’s monograph information content. [30] Since h is strictly a function of its argument and the statistical model of content, \Sigma is order independent, and M is one-to-one onto, transformation under M does not effect H, and thus H(F') = H(F).

Bigraph, trigraph, and higher order content can also be considered, but these do not appear to be significantly more difficult to forge. More generally, compression techniques can be used to decrease the overall content of a data set by flattening the probability distributions associated with symbols. [32] Once this has been done, symbols can be added to adjust the content and size of the data set. Computers also have finite precision, and a forgery needn’t be exact, but only close enough for the precision being applied. Higher precision requires more time, and computing information content takes a substantial amount of time even at nominal precision.

The fundamental problem with all of these techniques is that they are designed to cover specific classes of changes, whereas an intentional attacker need not make changes in those classes in order to infect a file.

An alternative method designed to withstand substantial attack by knowledgeable attackers is the cryptographic checksum. The basic principle is to use a secret key and a good but fast cryptosystem to encrypt a file and then perform a checksum on the encrypted contents. If the cryptosystem is good enough, the key is kept secret, and the process meets performance requirements, the result is a usable hard-to-forge cryptographic checksum [5,14,15]. In this case, we can store the checksums on-line and unprotected, and still have a high degree of assurance that an attacker will be unable to make a change to the stored information and/or the associated cryptographic checksum such that they match under the unknown key when transformed under the hard-to-forge cryptographic checksum. More specifically, the properties of a hard-to-forge cryptographic checksum for this application are:


\begin{eqnarray}
C : \{f, k, S : f \times k \to N\} &:&\\
&&	\bigtriangleup f \to \bigtriangleup S\\
&&	\bigtriangleup k \to \bigtriangleup S\\
&&	\bigtriangleup S \to \bigtriangleup for \bigtriangleup k\\
&&	\{f, S\} \not\to k\\
&&	\{f, S\} \not\to \{f', S'\}
\end{eqnarray}

where f is a file, k is the cryptosystem key, S is the cryptographic transform which yields a natural number N, and f' and S' are a modified file and checksum such that f \not= f'.

Fairly secure cryptographic checksums have been implemented with performance comparable to CRC codings. [14,15] In addition, the use of cryptographic checksums introduces the principle of the protection vs. performance tradeoff. In general, we can increase the difficulty of attack by increasing the cryptosystem key size, reducing the content per symbol (e.g. Huffman compression [32]), or improving our computational advantage through the use of a different cryptosystem [31]. Each of these normally involves more time for increased difficulty of attack, although changing cryptosystems may improve both protection and performance.

It is now apparent that we can use cryptographic checksums to reliably detect the changes associated with a computer virus even in the presence of a knowledgeable attacker, but we still have the problem of finding a way to efficiently apply the cryptographic checksum for virus detection, and the problem that we can only detect change with cryptographic checksums and not the presence or absence of viruses. As we pointed out earlier, the latter problem is of undecidable in general [1,4], and as we will point out next, the former problem has been solved.

A special problem for integrity shells in operating systems without basic protection comes from the so called ‘stealth’ viruses. These classes of viruses alter the operating system calls used by integrity shells to examine file contents so that they report original file contents even though the same file when ‘loaded’ through a different system call returns different data. In order for integrity shells to maintain integrity, they must in turn rely on some other integrity maintenance mechanism. This is true in general, in that all protection ultimately depends on some other protection mechanism, eventually leading down to the level of hardware and then physics.

4 Optimal Detection and Infection Limitation

We mentioned earlier that all built-in self-test techniques are vulnerable to a generic attack. The basis of this attack is that the virus could activate before the program being attacked, and forge an operating environment that, to the self defense technique, shows the altered information to be unaltered [7]. Since the introduction of this concept [27], several so-called ‘stealth’ viruses have appeared in the environment with the ability to forge unmodified files when the DOS operating system is used to read files, thus making detection by self-examination fail.

An alternative to built-in self-test is the use of a system-wide test capability that uses cryptographic checksums to detect changes in information. The question remains of how to apply this technique in an efficient and reliable manner. It turns out that an optimal technique for applying cryptographic checksums called an ‘integrity shell’ has been found [12,13].


\begin{align}
&	System : \{P, K, S, C : & P \times K \to S, M, V, k\}\text{ where:} & \\
			&& P = \{p_1, \dots, p_n\}, n \in I 		&&& \text{(A set of programs)}\\
			&& K = \{k_1, \dots, k_m\}, m \in I		&&& \text{(A set of keys)}\\
			&& S = \{s_1, \dots, s_o\}, o \in I		&&& \text{(A set of checksums)}\\
			&& C : P \times K \mapsto S			&&& \text{(A transform)}\\
			&& M = \{m_1, m_2, m_3, m_4\}			&&& \text{(A set of moves)}\\
			&& V = \{v_1, \dots, v_n\}			&&& \text{(A set of values)}
\end{align}

\forall v_i \in V, \exist s_i \in S : v_i = s_i, and each user has a secret key k \in K.

At a set of times T = \{t_1, \dots, t_n\}, we generate initial values V = \{v_1, \dots, v_n\}\forall p_i \in P, where \forall v_i \in V,\ v_i = C(p_i, k) at time t_i.

We now define: t_j : \forall t_i \in T,\ t_j > t_i

We then have an ‘operating system’ which operates as follows:


\begin{align}
	1 && \text{get a program '}p_i\text{' to be interpreted (time=}t_j\text{)}\\
	2 && \text{if }C(p_i, k) = v_i,\text{ interpret }p_i\text{; goto 1}\\
	3 && \text{ask user for a move '}m\text{' where:}\\
m = m1: && \text{goto 1}\\
m = m2: && \text{\{interpret pi; goto 1\}}\\
m = m3: && \text{\{set }v_i = C(p_i, k)\text{; interpret }p_i\text{; goto 1\}}\\
m = m4: && \text{\{restore }p_i\text{ to where }C(p_i, k) = v_i\text{; interpret }p_i\text{; goto 1\}}
\end{align}

This testing method performs tests just before interpretation. It is optimal 2 in that it detects all primary infection, 3 prevents all secondary infection, 4 performs no unnecessary checks, and we can do no better in an untrusted computing environment. [12]

Early experiments with integrity shells showed that they detected Trojan horses under Unix and gained rapid user acceptance in a programming environment [13]. More recently, cost analysis has also shown that this technique is more cost effective than other techniques in widespread use, including far less reliable methods such as virus scanners [7,16]. A similar cryptographic checksum method has been proposed for multi-level trusted systems [17], and finer grained hardware based detection at load time has also been proposed [18].

5 Automated Repair

Automated repair has been implemented with two techniques; for known viruses, it is sometimes feasible to remove the virus and repair the original data set with a custom repair routine; while general purpose repair is accomplished through on-line backups.

Although custom repair has some appeal, it is possible to write viruses that make this an NP-complete problem or worse through the use of evolution [1,4,7]. In several cases, customized repair has also produced undesired side effects, but this is primarily because of errors in identification of viruses or because certain side effects caused by viruses are not reversible from the information remaining in the data set. A simple example of an irreversible modification is the addition of instructions at a random location in the data space of a program. We can remove the instructions from the virus if we can find them, but we cannot necessarily determine what data they replaced. Similarly, a virus that adds bytes to a program to reach an aligned length and then adds the virus to the end, cannot be restored to the proper length because the proper length is no longer known.

As a general purpose repair scheme, on-line backups are used in an integrity shell to replace a data set with an image of the data stored when it was last trusted. This brute force method succeeds in all but the rarest cases, but has the undesirable side effect of doubling the space requirements for each covered data set. The space problem can be reduced by 50% or more in cases where original data sets have sufficient redundancy for compression to be effective, but the time and space overhead may still be unacceptable in some cases.

We can often implement on-line backups with no space overhead by compressing the original executable files and the on-line backups so that both require only 1/2 of the space that the original executable file required. This then slows processing at every program execution as well as at backup recovery time, and thus implements a slightly different time/space tradeoff.

On-line backups are also vulnerable to arbitrary modification unless they are protected by some other protection mechanism. Two such protection mechanisms have been devised; one cryptographically transforms the name and/or contents of the redundant data set so as to make systematic corruption difficult; and the other protects the on-line backups with special system state dependent access controls so that they can only be modified and/or read when the integrity shell is active in performing updates and/or repairs. Both of these mechanisms have been quite effective, but both are vulnerable in machines which do not provide separate states for operating system and user resident programs (i.e. current personal computers).

A LAN based backup mechanism has also been implemented by placing backup files on the LAN file server. This mechanism has the pleasant side effect of automating many aspects of LAN based PC backup and recovery, which has become a substantial problem. In a typical LAN of only 100 computers, each with a failure rate of one failure per 2 years (i.e. a typical disk mean-time-to-failure for PC based systems), you would expect about 1 failure per week. Some LANs have 10,000 or more computers, yielding an expected 100 failures per week. In these situations, automated LAN based recovery is extremely useful and saves a great deal of time and money.

Unfortunately, in many personal computers, the system bootstrap process cannot even be secured, and thus viruses can and have succeeded in bypassing several quite thorough integrity shell implementations. A recent development taken from fault tolerant computing [30] uses roll back techniques to ‘SnapShot’ system memory at bootup and perform a complete replacement of the system state with the known state from a previous bootstrap [25]. With this system, any memory resident corruptions are automatically removed at bootstrap and initial system testing can continue unhindered. The SnapShot mechanisms must of course be protected in order for this to be effective against serious attackers, but this dramatically reduces the protection problem and makes it far more manageable. In practice, this technique has been effective against all PC based bootstrap modifying viruses available for testing, and when combined with subsequent integrity checking and repair with on-line backups, results in a formidable barrier against attack.

6 Fault Avoidance Techniques

In almost all cases where viruses modify files, they exploit the operating system calls for file access rather than attempting to perform direct disk access. In systems with operating system protection, this is necessary in order to make viruses operate, while in unprotected systems, it is often too complex to implement the necessary portions of all versions of the operating system inside the virus, and it makes the virus less portable to hinge its operation on non-standard interface details that may not apply to all device types or configurations. An effective fault avoidance technique is to use enhanced operating system protection to prevent viruses from modifying some portion of the system’s data sets.

It turns out that because viruses spread transitively, you have to limit the transitive closure of information flow in order to have an effective access control based defense [4]. In the vast majority of existing computer systems, the access control scheme is based on the subject/object model of protection [21], in which it has been shown undecidable to determine whether or not a given access will be granted over time. In an information system with transitive information flow, sharing, and Turing capability, this problem can only be resolved through the implementation of a partially ordered set [3,4].

To date, only one such system has been implemented [22], and preliminary operating experience shows that it is operationally more efficient and easier to manage than previous protection systems, primarily because it uses coarse grained controls which require far less time and space than the fine grained controls of previous systems, and because it has automated management tools to facilitate protection management. It has also proven effective against the transitive spread of viruses, thus confirming theoretical predictions.

Covert channels [23] still provide a method for attack by users in domains near the INF of the partially ordered set. Bell-LaPadula based systems [24] are vulnerable to the same sort of attack by the least trusted user [1,4,7], but with partially ordered sets, there needn’t be a single INF, and thus even the impact of attacks exploiting covert channels can be effectively limited by this technique.

A ‘BootLock’ mechanism has also been devised to pre-bootstrap the computer with a low-level protection mechanism that masks the hardware I/O mechanism of the PC. BootLock provides low-level remapping of disk areas to prevent bootstrapping mechanisms other than the BootLock mechanism from gaining logical access to the DOS disk, and thus forces an attacker to make physical changes to a disk of unknown format or to unravel the disk remapping process in order to avoid protection. BootLock is also used to prevent disk access when the operating system is not bootstrapped through BootLock (i.e. from a floppy disk). Disk-wide encryption provides a lower performance but higher quality alternative to BootLock protection. Hardware BootLock devices also exist, and are capable of physically preventing this sort of protection.

A wide variety of other fault avoidance techniques have been implemented, including testing of all disks entering an area for known viruses using a scanner [7], physical isolation from external systems [1], in-place modification controls for binary executables [29], special avoidance techniques in specific system states, and sound change control [7]. Although all of these techniques provide limited coverage against many current attacks, they have serious and fundamental cost and effectiveness problems that make them less desirable than more sound and cost effective techniques [7].

7 Defense-in-depth

As of this writing, the most effective protection against computer viruses is based on defense-in-depth. In this approach, we combine many approaches so that when one technique fails, redundant techniques provide added coverage. Combinations of virus monitors, integrity shells, access controls, on-line backups, SnapShots, BootLocks, and ad-hoc techniques are applied to provide barriers against operation, infection, evasion, and damage by known and unknown viruses. To give a clearer picture, we describe one operational system for a PC [29]. This system is implemented through a series of techniques installed and operable at different phases of DOS operation:

PhaseOperations
p_1Hardware + ROM bootstrap
p_2Disk bootstrap
p_3Operating system load
p_4Driver loads
p_5Command interpreter load
p_6Instruction processing
p_7Operating system calls

where processing proceeds from phase p_1 to p_5 and then alternates between p_6 and p_7. Phase p_1 can only be altered by hardware changes, whereas all other phases of operation are easily modified by any DOS program under normal DOS operation.

The following table summarizes these descriptions by showing each phase and each protection mechanism’s status in that phase. In this table, ‘+’ indicates the facility is optionally active, and ‘-’ indicates the facility is inactive.

Facility\Phase12345677.17.27.37.4
BootLock-++++-+++++
SnapShot---+-------
Scanner---+-------
System Test---+-------
Passwords---+-------
Access Controls----+-+++--
Special Controls---------++
Self-defense---+--++---
Virus Monitor----+-+----
Integrity Shell----+-+----
Virus Traps----+-+++++

8 Experimental and Real-World Experience

In the laboratory and in operational experience, numerous experimental and real-world viruses have been tested against the above described defense mechanism. Although most experiments indicate very little because their results are easily predicted, occasionally we find a surprising result and have to improve our models of what has to be covered and how to effectively cover it. The good news is that the technique of defense-in-depth tends to provide ample redundancy to withstand new and unanticipated attack mechanisms. This is a vital point because with such a mechanism, we are now in a proactive posture, where defenders are not ‘chasing’ attackers, but rather attackers are ‘chasing’ defenders.

For example, the virus monitor is only effective against known viruses, and is thus quite weak. To avoid it, we only have to write a new virus or modify an existing virus in a non-trivial manner. This is done at a high rate, 6 so there is little realistic hope or desire for such constant updating. Since the time required for monitor operation increases with the number of different viruses tested for, we decrease performance as we increase the known attack list. Based on experience, we select the most likely viruses and encode enough to cover over 90% of current attacks. 7

The integrity shell detects all viruses which modify files unless they also modify the operating system mechanisms which the integrity shell uses to examine the files (A.K.A. a ‘stealth virus’) or circumvent the cryptographic mechanism. This covers over 99% of current known viruses, and in less vulnerable operating systems might be adequate on its own.

Access control has the effect of limiting the scope of the attack by preventing modification of non-writable files by the attack. To avoid this mechanism, it is necessary to either bypass its operation in memory or avoid the use of operating system calls entirely and perform purely physical disk access. This becomes quite complex as the number of different versions of the DOS operating system are quite high and hardware platforms vary considerably.

In practice, only a few of the known viruses have bypassed the access control mechanism (less than 1% of known viruses), and they do so by tracing operating system calls to locate internal addresses which they then directly access. Another similar technique exploits undocumented system calls or implementation errors to bypass protection. In the case of implementation errors, repairs are made as errors are found, and testing programs help locate these errors. Unfortunately, a complete test is infeasible for such a complex system, so instead of getting a verified implementation these systems get stronger with time. In the case of undocumented system calls, we cannot anticipate the number or extent of the problem, and cannot get adequate documentation from the manufacturers in order to provide proper protection.

The series of virus traps which prevent damage by a variety of means are over 99% effective, but a skilled attacker can easily bypass these techniques. The trace trap for example can be easily bypassed by manual inspection and in-place modification of the machine memory resident routines, since it is only a few instructions long. The problem of writing a program for the same task is somewhat more complex. Recently, we have started to exploit evolutionary techniques for making automated bypass of these mechanisms more difficult, and we currently believe that this can be made NP-complete fairly easily.

The remapping of disk areas at bootup prevents all currently known automated physical attacks and the vast majority of manual attacks other than those performed by a skilled and well tooled operator with physical access to the hardware. By convention, we never rate any defense better than 99% effective, so we give that ranking to this one.

Finally, the SnapShot mechanism has never been circumvented, and as far as we can tell, can only be bypassed by intentional attack against the specific defensive mechanism. Thus it receives our 99% rating as well.

A simple 8 calculation, assuming independence of mechanisms, is that the likelihood of successful attack is less than 1 in (90x99x99x99x99x99), or less than 1.2\times 10^{−12}! Unfortunately, protection just doesn’t work that way, because we don’t have random attackers. Most serious attackers will divide and conquer, bypassing each mechanism in turn. This requires a serious attacker to spend a substantial amount of time and effort. Thus, even though our probabilistic assumption is foolish, we have succeeded in ‘raising the bar’ high enough to fend off the vast majority of non-expert virus writers.

Another important issue to be understood in the case of viruses, is that selective survival assures us that as soon as one attacker succeeds in bypassing a defense mechanism, the successful virus will spread and become available to far more attackers, who in turn may find other ways of exploiting similar weaknesses. Experience shows that attackers are intentional and malicious, and spend inordinate amounts of time finding ways to bypass protection mechanisms. Since no such defense is or can be perfect for the PC, we will perhaps always struggle with the problem of viruses, as long as we operate in this completely untrusted mode. The current mix of viruses in the global environment is primarily a reflection of the defenses, not of the written viruses. By using defense-in-depth in an environment where most other defenses have fewer levels of protection, you dramatically increase your chances of succeeding in defense. This is not true in an environment where defense-in-depth is commonplace, since the successful attacks will be those which bypass numerous defense mechanisms.

There are some other advantages and disadvantages of these mechanisms and we would be remiss if we did not point them out. In particular, general purpose mechanisms which are successful against intentional attackers tend to be quite successful against random events. On the other hand, as we provide defense-in-depth, performance suffers, and we may have to consider the extent to which we are willing to reduce performance in trade for coverage.

The integrity shell, automated recovery mechanism, access control mechanisms, SnapShot mechanism, and BootLock mechanism are all amenable to general purpose use in other protection applications, have a sound basis in theory for their effectiveness, and are attractive in other ways. Virus specific traps, monitors, trace prevention mechanisms, and other PC specific defenses are less portable and less applicable in other environments.

Performance can be greatly enhanced through hardware based implementations. To get an idea of the performance implications, the implementation we have been discussing typically operates in less than 3K of resident memory, and except for virus monitor and integrity shell operations requires only a few hundred extra instructions per affected operating system call. In practice, this has negligible performance impact. The monitor and integrity shell functions however take considerably more time because the operations they perform are considerably more complex. A typical virus monitor can check for 60 known viruses on a slow PC in under 1/2 second. If we expand to 600 viruses, this time exceeds one second, and as we start to examine harder to identify viruses, the time can go up by several orders of magnitude, depending on what we are looking for. A typical cryptographic checksum on a 20Mhz PC-AT (16 bit bus) with a 20msec hard-disk operates at 100Kbytes per second. The average DOS program in only about 17Kbytes long [13], so integrity shell operation slows typical program startup by under 0.2 sec.

On-line backup restoration normally requires twice the time of program checking because for every read performed in checking, restoration performs a read and a write. With compression, this problem can be reduced, but only in trade for more computation time in the restoration process. We have never encountered a circumstance where it is preferable to not restore from on-line backups due to the time overhead, and considering that the time for restoration without on-line backups is at least several orders of magnitude longer, only space usage appears to be an impediment to the use of on-line backups.

The strength of this integrated set of redundant protection mechanisms is far stronger than a non-integrated subset because synergistic effects result in increased protection. As an example of synergy, with independent access controls and integrity checking, integrity checking information must be accessible to the attacker in order to be checked, and thus cannot be adequately protected by the access controls. Similarly, on-line backups cannot be protected from modification unless access control is integrated with automated repair. Memory limits of DOS cause the size of resident memory usage to be a critical factor in protection as well. By combining mechanisms we dramatically reduce resident memory requirements, which is another synergistic effect. There are many other synergistic effects too numerous to list here.

Ultimately, a sufficiently motivated, skilled, and tooled attacker with physical access to a system will bypass any protection mechanism, but in the case of computer viruses, highly complex mechanisms are more likely to require large amounts of space and time and be noticed because of their overall system impact. If we can drive the complexity of automated attack high enough without seriously impacting typical system performance, we will have achieved our primary goal. Defense-in-depth appears to succeed in forcing attackers to launch defense-specific attacks in order to succeed in undetected attack, and in this regard, drives the complexity of attack up significantly.

9 Architectural Implications

As we have seen, successful software based virus protection in untrusted computing environments depends heavily on software based fault-tolerant computing. Not only do we require defense-in-depth in order to be effective, but we often have to use redundancy within each method to assure reliability of mechanisms against attackers. Although these software techniques are quite effective at this time, ultimately a hardware supported solution is far preferable.

In the field of computer architecture, information protection has historically been fundamental. Many major advances in information protection have led directly to new architectural structures in computers, and at this point in time, about 25% of the hardware in most modern CPUs is in place for the purpose of protection. At the hardware level, fault tolerant computing has gained legitimacy as a field which studies integrity of hardware structures, but at the systems level, it has failed to protect against software based attacks. Thus information protection at the hardware level has a firm grip in most computer engineering programs, but exists under a name that hides its relationship to other information protection subfields like ‘computer security’ and ‘cryptography’. Computer viruses have demonstrated the extreme lack of integrity in modern systems and networks, and have demonstrated the short sightedness of our research community in ignoring integrity, a major protection issue.

A large portion of the applications for ‘super-computers’ are in the code breaking area. Encryption hardware is a substantial portion of the military budget, and nearly every modern mainframe computer system has either hardware or software encryption capabilities. Every automatic teller machine uses encryption hardware and software, as do most point of sale terminals, and many credit card checking machines. A major complaint against the personal computer has been its lack of protection hardware, which causes crashes and numerous other problems. The major differences between the first generation of personal computers and those coming out now are the addition of protection hardware and improved performance.

Introducing standard hardware-based operating system protection provides dramatic improvements in access control and separation of operating system functions from application program attacks, but this alone does not resolve the virus problem. For example, the first virus experiments were performed on a Unix based system with hardware based operating system protection. They did not exploit any operating system properties other than the sharing and general purpose function, [1] and they demonstrated the ability to attain all rights in under 30 minutes on the average.

For integrity shells and virus monitors, we can save considerable amounts of time by incorporating the checking mechanism in the operating system, since checking a program and then loading it duplicates I/O operations. Hardware based implementation yields several orders of magnitude in performance which can be used to improve performance and/or difficulty of attack. The most advantageous hardware location for generating and testing these codes is in the disk controller, where other checks such as parity and CRC codes are done. The disk controller could easily return these codes to the operating system as a result of DMA transfers, and the operating system could then combine the sequence of codes generated for a file to yield a cryptographic checksum.

Another alternative for highly trusted operating systems is maintaining only the modification date and time of files and a list of the last authorized modification date and time [12]. The operation is essentially the same as an integrity shell, except that we must have an operating system that maintains file dates and times as reliably as cryptography covers changes. In systems without physical access and very good operating system controls, this is feasible, but no current system meets this standard. For example, current systems allow the clock to be changed, which completely invalidates this mechanism. Updating file modification dates would also invalidate this mechanism. Another advantage of the cryptographic checksum is that it operates properly between machines and across networks. Since the cryptographic checksum is a mathematical function of the key and file, no hardware dependencies need be involved.

We must also be careful to assure that the mechanism of updating checksums does not become too automated. If it simply becomes a check for disk errors, it will not fulfill its purpose of controlling the propriety of change. After all, the legitimacy of change is a function of intent [12], and if we automate to the point where people do not specify their intent, we return to the situation where a virus can make a seemingly legitimate change.

Other mechanisms that may be retained for increased integrity even when operating system protection is facilitated by hardware are the BootLock and SnapShot mechanisms. These mechanisms are vital in assuring that the bootstrapping process has not been corrupted.

10 Summary, Conclusions, and Further Work

We have described a substantial set of failures in existing defenses and redundant integrity protection mechanisms used in defending against computer viruses in untrusted computing environments. They include applications of coding theory, cryptography, operating system modifications, redundancy for detection and repair, fault avoidance, synergistic effects, and defense-in-depth.

These protection measures comprise one of the most comprehensive applications of software-based fault-tolerance currently available, and their widespread use represents a major breakthrough in the application of software-based fault-tolerance. They have proven effective against a wide range of corruption mechanisms, including intentional attacks by malicious and intelligent agents. Some of these techniques are used for virus defense in over 100,000 systems, there are something like 10,000 systems currently exploiting all of these techniques in combination, and in the next few years, the total number of systems protected by these mechanisms are expected to exceed 1,000,000.

The protection measures discussed herein are effective to a large degree against computer viruses, but the implications of this work on high integrity computing are far broader than just defense against viruses or even ‘computer security’. The integrity shell, for example, detects large classes of corruptions, including single and multiple bit errors in storage, many sorts of human and programmed errors, accidental deletion and modification, transmission errors in networking environments, and read/write errors in unreliable media such as floppy disks. SnapShot techniques have widespread applications for high integrity bootstrapping, and similar techniques are already used in limited ways for error recovery in other areas.

Improvements based on hardware implementation will provide dramatic performance and reliability enhancement, as will the application of these techniques in systems which already exploit hardware based operating system protection.

In every case we are aware of, attack is feasible given an attacker with physical access to the system, adequate tools for system debugging, and adequate knowledge and persistence. There is no perfect defense.

A number of related research areas are already being pursued. The highest priorities at this time are being given to enhanced performance, improvements in evolutionary codes to make automated bypassing of protection mechanisms very complex, other exploitations of computational advantage in driving the complexity of attack up without seriously impacting the performance of the defenses, improved techniques for exploiting hardware based protection, and the application of these techniques to other operating systems and architectures.

11 References

  1. F. Cohen, “Computer Viruses - Theory and Experiments”, originally appearing in IFIP-sec 84, also appearing in DOD/NBS 7th Conference on Computer Security, and IFIP-TC11 “Computers and Security”, V6(1987), pp22-35 and other publications in several languages.
  2. A. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem”, London Math Soc Ser 2, 1936.
  3. F. Cohen, “Protection and Administration of Information Networks with Partial Orderings”, IFIP-TC11, “Computers and Security”, V6#2 (April 1987) pp 118-128.
  4. F. Cohen, “Computer Viruses”, Dissertation at the University of Southern California, 1986.
  5. F. Cohen, “A Complexity Based Integrity Maintenance Mechanism”, Conference on Information Sciences and Systems, Princeton University, March 1986.
  6. W. Gleissner, “A Mathematical Theory for the Spread of Computer Viruses”, “Computers and Security”, IFIP TC-11, V8#1, Jan. 1989 pp35-41.
  7. F. Cohen, “A Short Course on Computer Viruses”, ASP Press, PO Box 81270, Pittsburgh, PA 15217, 1990.
  8. H. Highland, “Computer Virus Handbook”, Elsevier, 1990.
  9. S. White, “A Status Report on IBM Computer Virus Research”, Italian Computer Virus Conference, 1990.
  10. K. Brunnstein, “The Computer Virus Catalog”, DPMA, IEEE, ACM 4th Computer Virus and Security Conference, 1991 D. Lefkon ed.
  11. F. Cohen, “Current Trends in Computer Virus Research”, 2nd Annual Invited Symposium on Computer Viruses - Keynote Address, Oct. 10, 1988. New York, NY
  12. F. Cohen, “Models of Practical Defenses Against Computer Viruses”, IFIP-TC11, “Computers and Security”, V7#6, December, 1988.
  13. M. Cohen, “A New Integrity Based Model for Limited Protection Against Computer Viruses”, Masters Thesis, The Pennsylvania State University, College Park, PA 1988.
  14. F. Cohen, “A Cryptographic Checksum for Integrity Protection”, IFIP-TC11 “Computers and Security”, V6#6 (Dec. 1987), pp 505-810.
  15. Y. Huang and F. Cohen, “Some Weak Points of One Fast Cryptographic Checksum Algorithm and its Improvement”, IFIP-TC11 “Computers and Security”, V8#1, February, 1989
  16. F. Cohen, “A Cost Analysis of Typical Computer Viruses and Defenses”, IFIP-TC11 “Computers and Security” 10(1991) pp239-250 (also appearing in 4th DPMA, IEEE, ACM Computer Virus and Security Conference, 1991)
  17. M. Pozzo and T. Gray, “An Approach to Containing Computer Viruses”, Computers and Security V6#4, Aug. 1987, pp 321-331
  18. J. Page, “An Assured Pipeline Integrity Scheme for Virus Protection”, 12th National computer Security conference, Oct. 1989, pp 369-377
  19. M. Bishop, “An Overview of Computer Viruses in a Research Environment”, 4th DPMA, IEEE, ACM Computer Virus and Security Conference, 1991
  20. F. Cohen, “A Summary of Results on Computer Viruses and Defenses”, 1990 NIST/DOD Conference on Computer Security.
  21. M. Harrison, W. Ruzzo, and J. Ullman, “Protection in Operating Systems”, CACM V19#8, Aug 1976, pp461-471.
  22. F. Cohen, “A DOS Based POset Implementation”, IFIP-SEC TC11 “Computers and Security” (accepted, awaiting publication, 1991)
  23. B. W. Lampson, “A note on the Confinement Problem”, Communications of the ACM V16(10) pp613-615, Oct, 1973.
  24. D. E. Bell and L. J. LaPadula, “Secure Computer Systems: Mathematical Foundations and Model”, The Mitre Corporation, 1973
  25. F. Cohen, “A Note On High Integrity PC Bootstrapping”, IFIP-SEC “Computers and Security” (accepted, awaiting publication, 1991)
  26. M. Joseph and A. Avizienis “A Fault Tolerant Approach to Computer Viruses”, Proceedings of the 1988 IEEE Symposium on Security and Privacy, 1989
  27. F. Cohen, “A Note on the use of Pattern Matching in Computer Virus Detection”, Invited Paper, Computer Security Conference, London, England, Oct 11-13, 1989, also appearing in DPMA, IEEE, ACM Computer Virus Clinic, 1990.
  28. J. Herst, “Eliminator” (Users Manual), 1990, PC Security Ltd. London, ENGLAND
  29. F. Cohen, “The ASP Integrity Toolkit - Technical Support Center Manual”, 1991 ASP Press, PO Box 81270, Pittsburgh, PA 15217, USA
  30. C. Shannon, “A Mathematical Theory of Communications”, Bell Systems Technical Journal, 1948.
  31. C. Shannon, “A Communications Theory of Secrecy Systems”, Bell Systems Technical Journal, 1949.
  32. D. Huffman, “A Method for the Construction of Minimum Redundancy Codes”, Proceedings of the I.R.E. V40, pp1098-1101, Sept. 1952
  33. M. Breuer and A. Friedman, “Diagnosis and Reliable Design of Digital Systems”, Computer Science Press, 1967 (see ‘rollback’)

1 Methods for making these counts are not standardized, and many ‘new’ viruses appear to be minor variations on previous viruses.

2 subject to the adequacy of the testing method C

3 infection by information that contains a virus but has been trusted nonetheless

4 infection by information infected through primary infection

5 A new SnapShot is generated if a previous one did not exist, otherwise, the previous startup state is restored. Operation then continues from the PC startup image generated on prior bootstrap.

6 1 or more new viruses per day as discussed above

7 60 out of 600 known viruses currently represent over 90% of the attacks, so we get 90% coverage by testing for only 10% of known viruses.

8 but misguided

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