Biblio

With the emergence of the Internet of Things (IoT) and the increasing number of resource-constrained interconnected smart devices, there is a noticeable increase in the number of cyber security crimes. In the face of the possible attacks on IoT networks such as network intrusion, denial of service, spoofing and so on, there is a need to develop efficient methods to locate vulnerabilities and mitigate attacks in IoT networks. Without loss of generality, we consider only intrusion-related threats to IoT. A honeypot is a system used to understand the potential dynamic threats and act as a proactive measure to detect any intrusion into the network. It is used as a trap for intruders to control unauthorized access to the network by analyzing malicious traffic. However, a sophisticated attacker can detect the presence of a honeypot and abort the intrusion mission. Therefore it is essential for honeypots to be undetectable. In this paper, we study and analyze possible techniques for SSH and telnet honeypot detection. Moreover, we propose a new methodology for probabilistic estimation of honeypot detection and an automated software implemented this methodology.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.