Network anomaly detection with incomplete audit data

Abstract

(ABSTRACT)With the ever increasing deployment and usage of gigabit networks, traditional network anomaly detection based intrusion detection systems have not scaled accordingly. Most, if not all, systems deployed assume the availability of complete and clean data for the purpose of intrusion detection. We contend that this assumption is not valid. Factors like noise in the audit data, mobility of the nodes, and the large amount of data generated by the network make it difficult to build a normal traffic profile of the network for the purpose of anomaly detection.From this perspective, the leitmotif of the research effort described in this dissertation is the design of a novel intrusion detection system that has the capability to detect intrusions with high accuracy even when complete audit data is not available. In this dissertation, we take a holistic approach to anomaly detection to address the threats posed by network based denial-of-service attacks by proposing improvements in every step of the intrusion detection process. At the data collection phase, we have implemented an adaptive sampling scheme that intelligently samples incoming network data to reduce the volume of traffic sampled, while maintaining the intrinsic characteristics of the network traffic. A Bloom filters based fast flow aggregation scheme is employed at the data pre-processing stage to further reduce the response time of the anomaly detection scheme. Lastly, this dissertation also proposes an expectation-maximization algorithm based anomaly detection scheme that uses the sampled audit data to detect intrusions in the incoming network traffic.ii

Research on Comprehensive Evaluation System of Network Security Management Based on Multi Dimensional Extension Fuzzy Technology

“…The application shows that the model is feasible and effective [12]. Through simple correlation function of extension theory to determine the index weight, through extensive correlation analysis of various dimensions of evaluation value, reflecting the extension evaluation based on data mining the useful information ability, to ensure the objectivity and accuracy of evaluation.…”

Data mining-based intrusion detectors

“…The following is to describe current analysis methods applied in intrusion detection system (Bace, 2002;Lu, Boedihardjo, & Manalwar, 2005;Patcha & Park, 2007;Verwoerd & Hunt, 2002):…”

An efficient network intrusion detection

“…The Stochastic Clustering Algorithm for Network Anomaly Detection (SCAN) method proposed by Patcha and Park [26] detects Denial of Service (DoS) attacks in high speed networks. High speed network traffic may contain noise and incomplete audit data.…”