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Although the Hardware Keylogger shows no mean shift characteristics, the other ten attacks show many mean shift characteristics, as shown in this chapter The mean shift characteristic results in this chapter can be used not only to detect but also to identify individual attacks Monitoring the variables with the unique attack characteristics of each attack can be considered when detecting and identifying that attack However, it may be more ef cient to consider monitoring the variables with the shared or opposite characteristics among attacks through a unique combination of those variables for each attack in order to reduce the total number of variables that need to be monitored to detect and identify any of these attacks An optimization problem of nding the smallest number of such variables to produce a unique combination of attack data characteristics for each attack is described in 18 This chapter also reveals the relationships among the ten attacks through the hierarchical clustering of the attacks based on their shared or opposite attack characteristics The grouping of the attacks as well as the similarity and difference in data characteristics underlying each attack group is helpful in recognizing the nature of unknown, novel attacks when they show similar attack ata characteristics with one or more groups of known attacks, and in guiding the further investigation of these new attacks to reveal their complete attack characteristics The mean shift characteristics can be used not only for distinguishing attacks from normal use activities by considering two categories of activities attack and normal use but also to identify any individual activity of interest by considering the activity as an individual category and uncovering its unique combination of mean shift characteristics Identifying not necessarily attack activities but other individual activities of interest has applications that go beyond cyber attack detection.

While more detail can be extracted by many of the tools presented in this chapter, these six elements are the core of any session data collection strategy In the context of this chapter, we care about session data for its ability to track intruder activities in a content-neutral way Unlike alert data tools (eg, intrusion detection systems), which rely on triggering a threshold or matching a pattern, session data applications collect everything they see The ability to track everything, or everything up to the limits of your session application, is invaluable for tracking stealthy intruders From a network investigation standpoint, full content data is more valuable than session data Full content data can be sliced and diced in any number of ways by multiple tools But because collecting full content data an be nearly impossible on high-traffic links, we turn to session data as the next best approximation of conversations between networked parties Support for session data collection is built in to many common pieces of networking infrastructure Session data has seen more use for accounting and billing purposes, but many analysts are realizing the security aspects of this important form of network audit data, especially as traffic loads increase Some thoughtful pioneers have been using session data for years to detect intruders Since the mid-1990s the Air Force Computer Emergency Response Team's (AFCERT) Automated Security Incident Measurement (ASIM) sensors have collected session data Analysts have queried or manually perused records of exchanges between Air Force systems and the world, finding novel intrusions not detected by ASIM's signature-matching logic In the late 1990s, Paul Myrick, then an officer in the Air Force Communications Agency, wrote a tool called Profiler to independently collect session data at Air Force bases In 1993, Carter Bullard, then with Carnegie Mellon, began work on the Audit Record Generation and Utilization System (Argus) program There are many ways to use session data on operational networks, but this book focuses on security We take an even more narrow approach when we use session data to track the activities of intruders We care about the intruder's IP address and the protocols he or she uses; we don't care so much about the bandwidth the intruder consumed I present enough information to get the tools running under normal circumstances, and then help decipher their output.

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Code 3/9 In Java Using Barcode maker for .Statistical data is the final form of content-neutral NSM information presented in this book Like full content and session data, statistics are collected to identify and validate intrusions Statistics are the ultimate evolution beyond the granularity of full content data and he vital traffic elements of session data This book discusses descriptive statistics, a way to summarize a collection of data in a clear and coherent manner We may track the amount of traffic on port 53 UDP seen over a two-week period or the amount of bandwidth consumed by the "top talker" at the present moment This is in contrast to inferential statistics, where analysts draw inferences on a population based on examining a sample of that population Because we will work with descriptive statistics, we will not be talking about confidence intervals or hypothesis testing I am more interested in looking at network traffic in a descriptive way, not wondering if my slice of traffic is representative of the Internet as a whole.Related: Generate QR Code Word , Data Matrix Generating Java , Create UPC-E Word

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Abstract excerpt: "The experience of the Lawrence Berkeley Laboratory in tracking an intruder suggests that any operating system is insecure when obvious security rules are ignored How a site should respond to an intrusion, whether it is possible to trace an intruder trying to evade detection, what can be learned from tracking an intruder, what methods the intruder used, and the responsiveness of the law enforcement community are also discussed" Significance: Cliff Stoll may have been the first person to understand the importance of network-based security monitoring He documented his 1986 investigation in this paper and the 1989 book The Cuckoo's Egg The following extracts are eerily prescient It's both amazing and disappointing how little has changed since Stoll commented on the state of computer security while he was monitoring German intruders at Lawrence Berkeley Laboratory Highlights: This paper is only 17 pages long, and it is an absolute must-read I could only include a few quotes here, but wanted to share much more Stoll explains why a passive monitoring system, like a network-based full content collection system, is superior to host-based systems in some respects Host-based systems interfere with the systems they watch and they are visible to intruders: "Off-line monitors are invisible even to an intruder with system privileges Moreover, they gave printouts of the intruder's activities on our local area network (LAN), letting us see his attempts to enter other closely linked computers Besides taking up resources, on-line monitors would have warned the intruder that he was being tracked We used this type of off-line monitor and avoided tampering with our operating systems" The author used full content monitoring o perform threat assessment: "Throughout this time, the printouts showed his interests, techniques, successes, and failures We observed the intruder's familiarity with various operating systems and became familiar with his programming style Buried in this chatter were clues to the intruder's location and persona, but we needed to temper inferences based on traffic analysis Only a complete trace back would identify the culprit" In the years since Stoll wrote these words, nothing has changed: "While connected to MILNET, this intruder attempted to enter about 450 computers, trying to log in using common account names like root, guest, system, or field Taking advantage of well-publicized problems in several operating systems, he was often able to obtain root or system-manager privileges" Network-based monitoring caught activity that disabled host-based monitoring missed Notice how the intruder preferred to appear as though he belonged on his victim systems: "Whenever possible, he disabled accounting and audit trails, so there would be no trace of his presence He planted Trojan horses to passively capture passwords and occasionally created new accounts to guarantee his access into computers Apparently he thought detection less likely if he did not create new accounts, for he seemed to prefer stealing existing, unused accounts".

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