ANN for Misuse Detection
Published on Jan 07, 2020
Because of the increasing dependence which companies and government agencies have on their computer networks the importance of protecting these systems from attack is critical. A single intrusion of a computer network can result in the loss or unauthorized utilization or modification of large amounts of data and cause users to question the reliability of all of the information on the network.
There are numerous methods of responding to a network intrusion, but they all require the accurate and timely identification of the attack.
Intrusion Detection Systems
The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. The individual creativity of attackers, the wide range of computer hardware and operating systems, and the ever changing nature of the overall threat to target systems have contributed to the difficulty in effectively identifying intrusions. While the complexities of host computers already made intrusion detection a difficult endeavor, the increasing prevalence of distributed network-based systems and insecure networks such as the Internet has greatly increased the need for intrusion detection.
There are two general categories of attacks which intrusion detection technologies attempt to identify - anomaly detection and misuse detection .Anomaly detection identifies activities that vary from established patterns for users, or groups of users. Anomaly detection typically involves the creation of knowledge bases that contain the profiles of the monitored activities.
The second general approach to intrusion detection is misuse detection. This technique involves the comparison of a user's activities with the known behaviors of attackers attempting to penetrate a system. While anomaly detection typically utilizes threshold monitoring to indicate when a certain established metric has been reached, misuse detection techniques frequently utilize a rule-based approach.
When applied to misuse detection, the rules become scenarios for network attacks. The intrusion detection mechanism identifies a potential attack if a user's activities are found to be consistent with the established rules. The use of comprehensive rules is critical in the application of expert systems for intrusion detection.
Current approaches to intrusion detection systems
Most current approaches to the process of detecting intrusions utilize some form of rule-based analysis. Rule-Based analysis relies on sets of predefined rules that are provided by an administrator, automatically created by the system, or both. Expert systems are the most common form of rule-based intrusion detection approaches. The early intrusion detection research efforts realized the inefficiency of any approach that required a manual review of a system audit trail. While the information necessary to identify attacks was believed to be present within the voluminous audit data, an effective review of the material required the use of an automated system.
The use of expert system techniques in intrusion detection mechanisms was a significant milestone in the development of effective and practical detection-based information security systems.
An expert system consists of a set of rules that encode the knowledge of a human "expert". These rules are used by the system to make conclusions about the security-related data from the intrusion detection system. Expert systems permit the incorporation of an extensive amount of human experience into a computer application that then utilizes that knowledge to identify activities that match the defined characteristics of misuse and attack.
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