Security information management with frame-based attack presentation and first-order reasoning

Internet has grown by several orders of magnitude in recent years, and this growth has escalated the importance of computer security. Intrusion Detection System (IDS) is used to protect computer networks. However, the overwhelming flow of log data generated by IDS hamper security administrators from uncovering new insights and hidden attack scenarios. Security Information Management (SIM) is a new growing area of interest for intrusion detection. The research work in this dissertation explores the semantics of attack behaviors and designs Frame-based Attack Representation and First-order logic Automatic Reasoning (FAR-FAR) using linguistics and First-order Logic (FOL) based approaches. Techniques based on linguistics can provide efficient solutions to acquire semantic information from alert contexts, while FOL can tackle a wide variety of problems in attack scenario reasoning and querying. In FAR-FAR, the modified case grammar PCTCG is used to convert raw alerts into frame-structured alert streams and the alert semantic network 2-AASN is used to generate the attack scenarios, which can then inform the security administrator. Based on the alert contexts and attack ontology, Space Vector Model (SVM) is applied to categorize the intrusion stages. Furthermore, a robust Variant Packet Sending-interval Link Padding algorithm (VPSLP) is proposed to prevent links between the IDS sensors and the FAR-FAR agents from traffic analysis attacks. Recent measurements and studies demonstrated that real network traffic exhibits statistical self-similarity over several time scales. The bursty traffic anomaly detection method, Multi-Time scaling Detection (MTD), is proposed to statistically analyze network traffic\u27s Histogram Feature Vector to detect traffic anomalies

R-CAD: Rare Cyber Alert Signature Relationship Extraction Through Temporal Based Learning

The large number of streaming intrusion alerts make it challenging for security analysts to quickly identify attack patterns. This is especially difficult since critical alerts often occur too rarely for traditional pattern mining algorithms to be effective. Recognizing the attack speed as an inherent indicator of differing cyber attacks, this work aggregates alerts into attack episodes that have distinct attack speeds, and finds attack actions regularly co-occurring within the same episode. This enables a novel use of the constrained SPADE temporal pattern mining algorithm to extract consistent co-occurrences of alert signatures that are indicative of attack actions that follow each other. The proposed Rare yet Co-occurring Attack action Discovery (R-CAD) system extracts not only the co-occurring patterns but also the temporal characteristics of the co-occurrences, giving the `strong rules\u27 indicative of critical and repeated attack behaviors. Through the use of a real-world dataset, we demonstrate that R-CAD helps reduce the overwhelming volume and variety of intrusion alerts to a manageable set of co-occurring strong rules. We show specific rules that reveal how critical attack actions follow one another and in what attack speed

A Novel Authentication and Validation Mechanism for Analyzing Syslogs Forensically

This research proposes a novel technique for authenticating and validating syslogs for forensic analysis. This technique uses a modification of the Needham Schroeder protocol, which uses nonces (numbers used only once) and public keys. Syslogs, which were developed from an event-logging perspective and not from an evidence-sustaining one, are system treasure maps that chart out and pinpoint attacks and attack attempts. Over the past few years, research on securing syslogs has yielded enhanced syslog protocols that focus on tamper prevention and detection. However, many of these protocols, though efficient from a security perspective, are inadequate when forensics comes into play. From a legal perspective, any kind of evidence found at a crime scene needs to be validated. In addition, any digital forensic evidence when presented in court needs to be admissible, authentic, believable, and reliable. Currently, a patchy log on the server side and client side cannot be considered as formal authentication of a wrongdoer. This work presents a method that ties together, authenticates, and validates all the entities involved in the crime scene--the user using the application, the system that is being used, and the application being used on the system by the user. This means that instead of merely transmitting the header and the message, which is the standard syslog protocol format, the syslog entry along with the user fingerprint, application fingerprint, and system fingerprint are transmitted to the logging server. The assignment of digital fingerprints and the addition of a challenge response mechanism to the underlying syslogging mechanism aim to validate generated syslogs forensically

Statistical methods used for intrusion detection

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 58-64)Text in English; Abstract: Turkish and Englishx, 71 leavesComputer networks are being attacked everyday. Intrusion detection systems are used to detect and reduce effects of these attacks. Signature based intrusion detection systems can only identify known attacks and are ineffective against novel and unknown attacks. Intrusion detection using anomaly detection aims to detect unknown attacks and there exist algorithms developed for this goal. In this study, performance of five anomaly detection algorithms and a signature based intrusion detection system is demonstrated on synthetic and real data sets. A portion of attacks are detected using Snort and SPADE algorithms. PHAD and other algorithms could not detect considerable portion of the attacks in tests due to lack of sufficiently long enough training data