Non-intrusive anomaly detection for encrypted networks

The use of encryption is steadily increasing. Packet payloads that are encrypted are becoming increasingly difficult to analyze using IDSs. This investigation uses a new non-intrusive IDS approach to detect network intrusions using a K-Means clustering methodology. It was found that this approach was able to detect many intrusions for these datasets while maintaining the encrypted confidentiality of packet information. This work utilized the KDD \u2799 and NSL-KDD evaluation datasets for testing

Towards intrusion detection for encrypted networks

Traditionally, network-based Intrusion Detection Systems (NIDS) monitor network traffic for signs of malicious activities. However, with the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer analyse the encrypted data. This essentially negates any protection offered by the NIDS. Although the encrypted traffic can be decrypted at a network gateway for analysis, this compromises on data confidentiality. In this paper, we propose a detection framework which allows a traditional NIDS to continue functioning, without compromising the confidentiality afforded by the VPN. Our approach uses Shamir's secret-sharing scheme and randomised network proxies to enable detection of malicious activities in encrypted channels. Additionally, this approach is able to detect any malicious attempts to forge network traffic with the intention of evading detection. Our experiments show that the probability of a successful evasion is low, at about 0.98\% in the worst case. We implement our approach in a prototype and present some preliminary results. Overall, the proposed approach is able to consistently detect intrusions and does not introduce any additional false positives