Taxonomy of Malicious Software and Detection Tools
The amount of malicious software is growing at an exorbitant rate and overwhelming security vendors. Identifying and disinfecting systems of that software remains in the realm of antivirus software. Maintaining the virus signatures and activity descriptions necessary for the proper operation of antivirus software may be compounded by the lack of a standard classification system for malicious software. The author-proposed taxonomy of malicious software and detection tools to aid the understanding of practitioners and the public.
By the end of 1992, Daoud, Jebril, and Zaqaibeh (2008) estimated that between 1,000 and 2,300 viruses existed; viruses were the only types of infection in the early years of malicious software. By 2002, Trojans and worms were added to the mix, and the number of known variants of malicious software grew to around 60,000. In the modern age, there are more than 100,000 classified virus strains. Note that many non-practitioners classify all types of malware as viruses.
The growing amount of malicious software appearing on a daily basis is overwhelming security companies. An article in Security and the Internet (2008) claimed that some security vendors receive a “daily average 15,000 files from product users and CSlRTs (Computer Security Incident Response Teams), and sometimes as many as 70,000” (p. 10) sample files per day to analyze. Daoud, et al. (2008) cited studies that demonstrated that connecting a computer to the Internet would subject that computer to attack in 39-second intervals.
The Need for a Taxonomy
Early malicious software programs were classified as viruses even before any similarity between malware and biological organisms were considered. Li and Knickerbocker (2007) recognized the differences between organic viral infections and computer viral infections but also noted certain similarities. “Although one uses genetic material and the other uses a series of computer instructions, the two follow similar patterns in the way in which infections are transmitted and in their behavior inside infected hosts” (p. 339).
Classifying organisms using taxonomies help scientists understand common physical and behavioral characteristics and such approaches can be helpful with regards to computer viruses. Biology-online.org (2008) offered one definition of taxonomy as a hierarchical method of classifying organisms according to ranks, such as the method using the division, class, genus, and species that an organism’s characteristics fit. Specialized taxonomies have been developed to fit specific needs, for instance, the United States Department of Commerce (2011) described a taxonomy developed in 1972 to classify organisms in the Chesapeake Bay.
Classifications of entities in areas other than biology are now common. Given the similarities proposed by Li and Knickerbocker (2007) between biological and computer viral infections, the author proposes that a Taxonomy of Malicious Software and Detection Tools may promote understanding of computer viral infections to both the security professional and the public. “Moreover, many countries have their own terminology and criteria for classifying malware, which is hardly ideal given the global nature of the Internet and crime” (Security and the Internet, 2008, p.10). The foundation for such a taxonomy is offered in this paper.
Drupal.org (2005), a well-known Content Management System publisher, prescribed that one of the most important elements to creating a taxonomy is developing the vocabulary. The vocabularies for the proposed Taxonomy of Malicious Software and Detection Tools would contain the terms to describe both the detection tools and the software detected by those tools.
These proposed vocabularies could be as follows:
Malicious Software Vocabulary
Software that contains advertisements downloaded to the user’s system without the user’s knowledge or permission, often resulting in browser redirection, pop-up advertisements, or pop-under advertisements is adware.
A mechanism typically inserted into a program by a developer to bypass normal security controls for testing purposes is a backdoor. Programmers often neglect to remove backdoors when testing is complete.
Boot Sector Infector
A virus that employs an encryption algorithm along with an encryption key to obscure the contents of viral package is an encrypted virus. The infected system decrypts the package upon receipt using the received encryption key and changes the key before retransmission of the virus to another victim’s system so the virus does not present the same signature to anti-virus scanners.
A virus that totally rewrites the virus code with each infection is a metamorphic virus. Metamorphic viruses differ from polymorphic viruses in that not only the appearance of the code changes but the viruses actually change their own program code.
A virus that changes the appearance of the virus’ code presented to antivirus software with every infection to eliminate the likelihood of a signature match is a polymorphic virus.
Named after the story of the Trojan Horse, a Trojan Horse program entices a victim by appearing to be a useful program but the true function of the program may be malicious in nature. A Trojan horse program acquires the authorization level of the user who unknowingly installs the malware so the software often acquires unlimited access to the system.
A software package that merges into legitimate executable code for transport to another system is a virus. The original executable code is said to be infected when the virus code successfully merges into that executable. Running the infected code also runs the virus.
Unlike a virus, a worm does not need to merge into other executable code for transport to other systems. A worm can self-replicate over a network to locate and infect other hosts and run on arrival. (Stallings and Brown, 2008)
A Malicious payload designed to initiate a Denial of Service attack by generating massive quantities of network traffic is a flooder.
A malicious payload that transmits captured keystrokes to another system is a keylogger.
A logic bomb waits for a specific time or event to trigger a malicious activity, such as deleting files.
Tools included in a payload to hide malware or to enable a hacker to regain entry into a compromised system is a rootkit.
Solicitation, pornography, and marketing material sent to an individual in e-mail without consent is known as SPAM. A spammer program is a payload package that sends massive quantities of SPAM.
Spyware collects personal or sensitive information from a user and relays that information to another system. (Stallings and Brown, 2008)
Capturing a unique snippet of computer code and using that snippet to identify malicious software is the detection method that characterizes signature-based scanners.
Employing a method that can learn about the behavior of malware is the characteristic that identifies heuristics-based scanners.
As stated earlier, a taxonomy is a method of classification. A taxonomical classification of malicious software and detection tools could commence once the vocabularies are defined. Taxonomical classification of malicious software and detection tools would classify both the malicious software and the tools used to detect and remove that software.
The top-level classification for malicious software would indicate whether the software is independent. True computer viruses depend on host programs to replicate to other systems, and Stallings and Brown (2008) referred to viruses as being parasitic in nature. These bits of software “are essentially fragments of programs that cannot exist independently of some actual application program, utility, or system program” (p. 216). Other malicious software types are independent programs that exist and run without the aid of a host executable program.
Malicious software could be sub-divided into two categories indicating whether the software delivers a payload or not. If the software delivers a payload, then the type of payload delivered would be added to the classification from the vocabulary. The final level of classification would indicate whether the malicious code alters its appearance.
Daoud, et al. (2008) explained that metamorphic viruses alter their appearance by reprogramming the malicious code. This reprogramming changes the behavior characteristics of program execution, which also changes the look of the virus to signature detection algorithms. “Metamorphic viruses use several metamorphic transformations, including Instruction reordering, data reordering, inlining and outlining, register renaming, code permutation, code expansion, code shrinking, Subroutine interleaving, and garbage code insertion” (p. 127). Polymorphic viruses simply obfuscate the original appearance to avoid detection by signature scanners.
The predominant strategy for detecting malicious software, as explained by Evans-Pugh (2006) scans files for snippets of code known as virus signatures. When a signature from a scan matches a portion of code in a test file, the file is determined to be infected. This type of detection could be classified as a signature based virus scan. When a signature-based scanner identifies an infected file, the scanner attempts to disinfect the file to remove the code snippet added to the scanned file by the malicious software. These scanners often place infected files in a quarantine directory when disinfection fails.
The other type of scanner is more dynamic and employs a heuristic method of learning about the activity of malicious software. Daoud, et al. (2008) explained that heuristic methods of detection run a program’s code and monitors the software’s activity then compares that activity to known methods of virus activity. “For example, most virus activity eventually needs to call some system functionality, like I/O operations - only these actions have to be considered. No matter how obfuscated the I/O calls are statically, the calls will appear clearly (p. 125).
Another type of heuristic malicious software detection compares a program’s activity to the normal learned activity of real users. Behavior blockers run the test code in real-time while analyzing the activity. Processes are then be terminated when abnormal behavior is detected. For instance, “human beings using a computer typically make one or two network connections per second, whereas if a piece of code on a machine tries to make 10,000 connections per second, you know it’s malign”( Evans-Pughe, 2006, p.33).
Both signature-based detection methods and dynamic or heuristic methods have some major drawbacks. Signature-based detection methods can only identify software for which they maintain signature files. These malicious software detection tools will not identify software for which no signature exists, so they require constant updating to remain current with new signatures. They can also require a more lengthy time to run as the signature base grows. Heuristic methods can misclassify what normal behavior is and either permit malicious software to run or block normal activity.
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Drupal.org (2005). Documentation: Vocabularies and terms. Available from http://drupal.org/node/22272
Evans-Pughe, C. (2006). Natural defences [security of data]. Engineering & Technology (17509637), 1(6), 30-33. doi:10.1049/et:20060603
Li, J., & Knickerbocker, P. (2007). Functional similarities between computer worms and biological pathogens. Computer & Security, 26(2007), 338-347. Retrieved July 14, 2011 from http://netsec.cs.uoregon.edu/research/papers/li07bio.pdf
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Stallings, W., & Brown, L. (2008). Chapter 7: Malicious software. Computer Security Principles and Practice. Upper Saddle River, NJ: Pearson Education, Inc.
United States Department of Commerce (2011). History of the NODC taxonomic code. Available from http://www.nodc.noaa.gov/General/CDR-detdesc/taxonomic-v8.html
This article is accurate and true to the best of the author’s knowledge. Content is for informational or entertainment purposes only and does not substitute for personal counsel or professional advice in business, financial, legal, or technical matters.