Abstract.A major concern for computer systems security is the threat from malicious insiders who execute perfectly legitimate operations to compromise system security. Unfortunately, most currently available intrusion detection systems (which include anomaly and misuse detection systems) fail to address this problem in a comprehensive manner. In this work we propose a framework that uses an attack tree to identify malicious activities from authorized insiders. We develop algorithms to generate minimal forms of attack tree customized for each user such that it can be used efficiently to monitor the user's activities. If the user's activities progress sufficiently up along the branches of the attack tree towards the goal of system compromise, we generate an alarm. Our system is not intended to replace existing intrusion detection and prevention technology, but rather is intended to complement current and future technology.
System log files contain valuable evidence pertaining to computer attacks. However, the log files are often massive, and much of the information they contain is not relevant to the investigation. Furthermore, the files almost always have a flat structure, which limits the ability to query them. Thus, digital forensic investigators find it extremely difficult and time consuming to extract and analyze evidence of attacks from log files. This paper describes an automated attack-tree-based approach for filtering irrelevant information from system log files and conducting systematic investigations of computer attacks.
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