Lunt T., Tamaru A., Gilham F., Jagannathan R., Jalali C., Neumann, P. G., Javitz, H. S., Valdes A., Garvey T. D. (1992) "A real time Intrusion Detection Expert System (IDES) - Final Report, "SRI International, Menlo Park, CA.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Distinguishing deliberate actions on the part of the user from implicit operations due to application behavior or malicious operations resulting from mobile code (see section II B) represents a considerable di#culty. This limits the e#ectiveness of most reactive intrusion detection systems [5] [6]. A related problem occurs when trying to specify permitted application behavior for intrusion detection mechanisms. Full specifications of generic COTS or even reasonably complex custom applications are very hard to obtain. Linking permitted operations on the part of either a user or of the ....

T. F. Lunt, A. Tamaru, F. Gilham, R. Jagannathan, P. G. Neumann, H. S. Javitz, A. Valdes, and T. D. Garvey, "A Real-Time Intrusion Detection Expert System (IDES) -- Final Technical Report," Tech. Rep., SRI Computer Science Laboratory, SRI International, Menlo Park, CA, Feb. 1992.

....audit data, rather than statis tical formulas. In 1985, Denning and Neumann presented a detailed dis cussion of statistical profile based anomaly detection [5] Perhaps the best known statistical profile based anomaly detection system is the Intrusion Detection Expert System (IDES) 17] 19] [20]. The profile based anomaly com ponent of this system identifies expected behavior at the The longer the duration, the more accurate the study. user, group, remote host and target system levels. For an in depth discussion of IDES, the reader is referred to [12] Two example intrusion detection ....

.... IDES, the reader is referred to [12] Two example intrusion detection implementations that employ rule based anomaly detection are Wisdom and Sense (W S) 34] and the Time based Inductive Machine (TIM) approach [3] Neural network based anomaly detection has also been proposed in recent work [4] [20]. Anomaly detection is not without limitations. In many environments, it may be difficult to establish behavior patterns for users. For example, in sporadic user environments establishing profiles of normal user behavior would be difficult. This leads to a potentially large number of false ....

T.F. Lunt, A. Tamaru, F. Gilham, R. Jagannathm, C. Jalali, P.G. Neumann, H.S. Javitz, A. Valdes and T.D. Garvey, "A Real- time Intrusion Detection Expert System (IDES), Final Technical Report," Computer Science Laboratory, SRI International, Menlo Park, CA, February 1992.

....detection is often times associated with detecting insider attacks , while intrusion detection generally refers to outsider attacks . We make no such distinction here. framework.tex; 11 12 2000; 14:59; p.3 4 Lee and Stolfo and Mok 2.1. ANOMALY DETECTION Anomaly detection, for example IDES (Lunt et al. 1992), tries to determine whether deviation from an established normal behavior profile can be flagged as an intrusion. A profile typically consists of a number of statistical measures on system activities, for example, the CPU usage and the frequency of system commands during a user login session. ....

Lunt, T., A. Tamaru, F. Gilham, R. Jagannathan, P. Neumann, H. Javitz, A. Valdes, and T. Garvey: 1992, `A Real-time Intrusion Detection Expert System (IDES) - final technical report'. Technical report, Computer Science Laboratory, SRI International, Menlo Park, California.

....EMERALD eXpert is a generic signature analysis engine based on the expert system shell p BEST [14] p BEST (Production Based Expert System Toolset) was originally written by Alan Whitehurst and employed in MIDAS. It was later enhanced at SRI by Whitehurst and Fred Gilham and was employed in IDES [24] and NIDES [12] pBEST provides a production rule language that allows users to express the inference formula for reasoning and acting upon the facts. The facts may be derived from external sources (events) or from the other production rules (rule triggers) The p BEST allows type declarations and ....

T. Lunt, A. Tamaru, F. Gilham, R. Jagannathan, C. Jalali, P. G. Neumann, H. S. Javitz, A. Valdes, and T. D. Garvey, "A Real Time Intrusion Detection Expert System (IDES)" Final Report, SRI International, Menlo Park, CA, Feb. 1992.

.... without authorization (i.e. crackers) and those who have legitimate access to the system but are exceeding their privileges (i.e. the insider threat) Work is being done elsewhere on Intrusion Detection Systems (IDS s) for a single host [10 11 8] and for several hosts connected by a network [7 6 12]. Our own earlier work on the Network Security Monitor (NSM) concentrated on monitoring a broadcast Local Area Network (LAN) 3] The proliferation of heterogeneous computer networks has serious implications for the intrusion detection problem. Foremost among these implications is the increased ....

T.F. Lunt, A. Tamaru, F. Gilham, R. Jagannathan, C. Jalali, H.S. Javitz, A. Valdes, and P.G. Neumann, "A Real-Time Intrusion-Detection Expert System (IDES)," Interim Progress Report, Project 6784, SRI International, May 1990.

....These tools are characterized by their expert system properties that fire rules when audit information indicates illegal activities. Most of the current intrusion detection tools supplement their anomaly detection components with rule based expert system components. For example, IDES [Lunt92], NADIR, and W S. All these approaches and their corresponding tools are discussed in detail in [Porr91] 2.1.4 Features of STAT In the last section we categorized different intrusion detection systems. STAT falls in the category of rule based penetration identification systems. Current ....

....same scenario several different audit record sequences might exist and those minor variations might slip unnoticed. STAT overcomes this problem by using a higher level audit record independent representation of penetration scenarios and by supporting permutable rule sequences. Garvey and Lunt [Lunt92] also proposed a new intrusion detection approach called Model Based Intrusion Detection. With this approach they address the above problems and provide an audit record independent technique to represent intrusion scenarios. STAT is designed to be a real time system. One of its main features is to ....

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Teresa F. Lunt et al. "A Real-time Intrusion Detection Expert System (IDES)," SRI Technical Report, February 28, 1992.

....identify an unauthorized user by identifying unusual usage of the computer. Usually, for each user a historical profile is built and large deviations from the profile indicate a possible intruder. Therefore it is also referred to as the profile based approach. Intrusion detection systems like IDES (Lunt et al. 1992), NIDES and Emerald (Porras and Neumann 1997) use both approaches, presumably because neither one is uniformly superior to the other. In this paper we only consider the anomaly detection approach, which lends itself to a statistical treatment. Ryan et al. 1998) suggested that each user on a ....

Lunt, T. Tamaru, A., Gilham, F., Jagannathan, R., Neumann , P., Javitz, H., Valdes, A., Garvey, T. (1992). "A Real-Time Intrusion Detection Expert System (IDES) - final technical report. " Computer Science Library, SRI International, Menlo Park, California.

....tables, and network traffic summaries. IDSs have been developed and used at several institutions. Some example IDSs are National Security Agency s Multics Intrusion Detection and Alerting System (MIDAS) 31] AT T s ComputerWatch [9] SRI International s Intrusion Detection Expert System (IDES) [24, 25] and Next Generation Intrusion Detection Expert System (NIDES) 1] UC Santa Barbara s State Transition Analysis Tool for UNIX (USTAT) 15, 16] Los Alamos National Laboratory s (LANL s) Network Anomaly Detection and Intrusion Reporter (NADIR) 14] and UC Davis Network Security Monitor (NSM) ....

.... system (or network) activity, and an intruder (possibly masquerading as a legitimate user) will exhibit a pattern of behavior different from the normal user [8] So, the IDS attempts to characterize each user s normal behavior, often by maintaining statistical profiles of each user s activities [25, 17]. Each profile includes information about the user s computing behavior such as normal login time, duration of login session, CPU usage, disk usage, favorite editor, and so forth. The IDS can then use the profiles to monitor current user activity and compare it with past user activity. Whenever ....

T. F. Lunt et al., "A Real-Time Intrusion Detection Expert System(IDES)," Interim Progress Report, Project 6784, SRI International, May 1990.

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Lunt T., Tamaru A., Gilham F., Jagannathan R., Jalali C., Neumann, P. G., Javitz, H. S., Valdes A., Garvey T. D. (1992) "A real time Intrusion Detection Expert System (IDES) - Final Report, "SRI International, Menlo Park, CA.

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