Networking and Communication Engineering

B. Muthulakshmi ¹

Affiliations
1 Department of CSE, A.V.C. College of Engineering, IN

Abstract

The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols [1], which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) [2] are often employed to monitor network traffic and host[3] activities that may lead to unauthorized accesses and attacks against vulnerable services. To combat against attacks on encrypted protocols [4], we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs) [5].. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions.

Keywords

SSL, TLS, IDS, MS, SSH, VPN.

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B. Muthulakshmi ¹

Affiliations
1 Department of CSE, A.V.C. College of Engineering, IN

Abstract

A new Internet traffic data mining technique presented for generating frequent episode rules (FER)[1]. Adaptive base-support threshold is applied to different axis attributes in these rules. We use the rules to build anomaly-based, network intrusion detection systems (NIDS)[2]. The episode rules detect anomalous sequences of TCP [3], UDP [4], or ICMP [5] connections. Three new pruning techniques are devised to reduce the rule search space by 70% in our bench mark experiments. Testing our scheme over real-life Internet trace data collected at USC mixed with 10 days of MIT/LL attack data, we encountered 20 or less false alarms over 200 network attacks. We detect with a success rate of 47% of all unknown network attacks. These results show a 51%improvement over the NIDS built with association rules, exclusively.

Keywords

Network Security, Intrusion Detection Systems, Anomaly Detection, Internet Traffic Analysis, Frequent Episode Rules, False Alarms and Adaptive Data Mining.

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