Biblio

Intrusion Detection Systems (IDS) are the systems that detect and block any potential threats (e.g. DDoS attacks) in the network. In this project, we explore the performance of several machine learning techniques when used as parts of an IDS. We experiment with the CICIDS2017 dataset, one of the biggest and most complete IDS datasets in terms of having a realistic background traffic and incorporating a variety of cyber attacks. The techniques we present are applicable to any IDS dataset and can be used as a basis for deploying a real time IDS in complex environments.

The future of ambient assisted living (AAL) especially eHealthcare almost depends on the smart objects that are part of the Internet of things (IoT). In our AAL scenario, these objects collect and transfer real-time information about the patients to the hospital server with the help of Wireless Mesh Network (WMN). Due to the multi-hop nature of mesh networks, it is possible for an adversary to reroute the network traffic via many denial of service (DoS) attacks, and hence affect the correct functionality of the mesh routing protocol. In this paper, based on a comparative study, we choose the most suitable secure mesh routing protocol for IoT-based AAL applications. Then, we analyze the resilience of this protocol against DoS attacks. Focusing on the hello flooding attack, the protocol is simulated and analyzed in terms of data packet delivery ratio, delay, and throughput. Simulation results show that the chosen protocol is totally resilient against DoS attack and can be one of the best candidates for secure routing in IoT-based AAL applications.

In this paper, we inspire from two analogies: the warfare kill zone and the airport check-in system, to tackle the issue of spam botnet detection. We add a new line of defense to the defense-in-depth model called the third line. This line is represented by a security framework, named the Spam Trapping System (STS) and adopts the prevent-then-detect approach to fight against spam botnets. The framework exploits the application sandboxing principle to prevent the spam from going out of the host and detect the corresponding malware bot. We show that the proposed framework can ensure better security against malware bots. In addition, an analytical study demonstrates that the framework offers optimal performance in terms of detection time and computational cost in comparison to intrusion detection systems based on static and dynamic analysis.