Abstract. Over the past decade many anomaly-detection techniques have been proposed and/or deployed to provide early warnings of cyberattacks, particularly of those attacks involving masqueraders and novel methods. To date, however, there appears to be no study which has identified a systematic method that could be used by an attacker to undermine an anomaly-based intrusion detection system. This paper shows how an adversary can craft an offensive mechanism that renders an anomaly-based intrusion detector blind to the presence of on-going, common attacks. It presents a method that identifies the weaknesses of an anomaly-based intrusion detector, and shows how an attacker can manipulate common attacks to exploit those weaknesses. The paper explores the implications of this threat, and suggests possible improvements for existing and future anomaly-based intrusion detection systems.
Anomaly detection is a key element of intrusiondetection and other detection systems in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. Because most anomaly detectors are based on probabilistic algorithms that exploit the intrinsic structure, or regularity, embedded in data logs, a fundamental question is whether or not such structure influences detection performance. If detector performance is indeed a function of environmental regularity, it would be critical to match detectors to environmental characteristics. In intrusion-detection settings, however, this is not done, possibly because such characteristics are not easily ascertained. This paper introduces a metric for characterizing structure in data environments, and tests the hypothesis that intrinsic structure influences probabilistic detection. In a series of experiments, an anomaly-detection algorithm was applied to a benchmark suite of 165 carefully calibrated, anomalyinjected datasets of varying structure. Results showed performance differences of as much as an order of magnitude, indicating that current approaches to anomaly detection may not be universally dependable.
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Practical software security measurements and metrics are critical to the improvement of software security. We propose a metric to determine whether one software system is more secure than another similar system with respect to their attack surface. We use a system's attack surface measurement as an indicator of the system's security; the larger the attack surface, the more insecure the system. We measure a system's attack surface in terms of three kinds of resources used in attacks on the system: methods, channels, and data. We demonstrate the use of our attack surface metric by measuring the attack surfaces of two open source IMAP servers and two FTP daemons. We validated the attack surface metric by conducting an expert user survey and by performing statistical analysis of Microsoft Security Bulletins. Our metric can be used as a tool by software developers in the software development process and by software consumers in their decision making process. AbstractPractical software security measurements and metrics are critical to the improvement of software security. We propose a metric to determine whether one software system is more secure than another similar system with respect to their attack surface. We use a system's attack surface measurement as an indicator of the system's security; the larger the attack surface, the more insecure the system. We measure a system's attack surface in terms of three kinds of resources used in attacks on the system: methods, channels, and data. We demonstrate the use of our attack surface metric by measuring the attack surfaces of two open source IMAP servers and two FTP daemons. We validated the attack surface metric by conducting an expert user survey and by performing statistical analysis of Microsoft Security Bulletins. Our metric can be used as a tool by software developers in the software development process and by software consumers in their decision making process.
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