Biblio
The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.
A Mobile ad hoc network (MANET) is a set of nodes that communicate together in a cooperative way using the wireless medium, and without any central administration. Due to its inherent open nature and the lack of infrastructure, security is a complicated issue compared to other networks. That is, these networks are vulnerable to a a wide range of attacks at different network layers. At the network level, malicious nodes can perform several attacks ranging from passive eavesdropping to active interfering. Wormhole is an example of severe attack that has attracted much attention recently. It involves the redirection of traffic between two end-nodes through a Wormhole tunnel, and manipulates the routing algorithm to give illusion that nodes located far from each other are neighbors. To handle with this issue, we propose a novel detection model to allow a node to check whether a presumed shortest path contains a Wormhole tunnel or not. Our approach is based on the fact that the Wormhole tunnel reduces significantly the length of the paths passing through it.
Flooding attacks are well-known security threats that can lead to a denial of service (DoS) in computer networks. These attacks consist of an excessive traffic generation, by which an attacker aim to disrupt or interrupt some services in the network. The impact of flooding attacks is not just about some nodes, it can be also the whole network. Many routing protocols are vulnerable to these attacks, especially those using reactive mechanism of route discovery, like AODV. In this paper, we propose a statistical approach to defense against RREQ flooding attacks in MANETs. Our detection mechanism can be applied on AODV-based ad hoc networks. Simulation results prove that these attacks can be detected with a low rate of false alerts.
Wireless mesh networks (WMNs) are attracting more and more real time applications. This kind of applications is constrained in terms of Quality of Service (QoS). Existing works in this area are mostly designed for mobile ad hoc networks, which, unlike WMNs, are mainly sensitive to energy and mobility. However, WMNs have their specific characteristics (e.g. static routers and heavy traffic load), which require dedicated QoS protocols. This paper proposes a novel traffic regulation scheme for multimedia support in WMNs. The proposed scheme aims to regulate the traffic sending rate according to the network state, based on the buffer evolution at mesh routers and on the priority of each traffic type. By monitoring the buffer evolution at mesh routers, our scheme is able to predict possible congestion, or QoS violation, early enough before their occurrence; each flow is then regulated according to its priority and to its QoS requirements. The idea behind the proposed scheme is to maintain lightly loaded buffers in order to minimize the queuing delays, as well as, to avoid congestion. Moreover, the regulation process is made smoothly in order to ensure the continuity of real time and interactive services. We use the interval type-2 fuzzy logic system (IT2 FLS), known by its adequacy to uncertain environments, to make suitable regulation decisions. The performance of our scheme is proved through extensive simulations in different network and traffic load scales.