A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD -a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

This thesis proposes a Distributed Intrusion Detection System for Smart Grids by developing and deploying intelligent modules in multiple layers of the smart grid in order to handle cyber security threats. Multiple Analyzing Modules are embedded at different levels of the smart grid-the Home Area Network, Neighborhood Area Network, and Wide Area Network. These intelligent modules employ Support Vector Machines and Artificial Immune System to detect and classify malicious data and possible cyber attacks. Analyzing Modules at different levels are trained using data that are relevant to their levels and will also be able to communicate with each other in order to improve the detection performance. Simulation results demonstrate that this is a promising methodology for improving system security through the identification of malicious network traffic, and the detection efficiency is improved by applying the optimal communication routing.

Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.

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