Visible to the public Biblio

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Hammi, B., Khatoun, R., Doyen, G..  2014.  A Factorial Space for a System-Based Detection of Botcloud Activity. New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on. :1-5.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Hammi, B., Khatoun, R., Doyen, G..  2014.  A Factorial Space for a System-Based Detection of Botcloud Activity. New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on. :1-5.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Rmayti, M., Begriche, Y., Khatoun, R., Khoukhi, L., Gaiti, D..  2015.  Flooding attacks detection in MANETs. 2015 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications (SSIC). :1–6.

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.

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Rmayti, M., Begriche, Y., Khatoun, R., Khoukhi, L., Mammeri, A..  2018.  Graph-based wormhole attack detection in mobile ad hoc networks (MANETs). 2018 Fourth International Conference on Mobile and Secure Services (MobiSecServ). :1–6.

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.

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Gu, P., Khatoun, R., Begriche, Y., Serhrouchni, A..  2017.  k-Nearest Neighbours classification based Sybil attack detection in Vehicular networks. 2017 Third International Conference on Mobile and Secure Services (MobiSecServ). :1–6.

In Vehicular networks, privacy, especially the vehicles' location privacy is highly concerned. Several pseudonymous based privacy protection mechanisms have been established and standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to Sybil attack. In this paper, a Sybil attack detection method based on k-Nearest Neighbours (kNN) classification algorithm is proposed. In this method, vehicles are classified based on the similarity in their driving patterns. Furthermore, the kNN methods' high runtime complexity issue is also optimized. The simulation results show that our detection method can reach a high detection rate while keeping error rate low.

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Gu, P., Khatoun, R., Begriche, Y., Serhrouchni, A..  2017.  Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks. 2017 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.

Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.

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Badis, H., Doyen, G., Khatoun, R..  2014.  Understanding botclouds from a system perspective: A principal component analysis. Network Operations and Management Symposium (NOMS), 2014 IEEE. :1-9.

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.

Badis, H., Doyen, G., Khatoun, R..  2014.  Understanding botclouds from a system perspective: A principal component analysis. Network Operations and Management Symposium (NOMS), 2014 IEEE. :1-9.

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.