References of "Lagraa, Sofiane 50028660"
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See detailROS-Defender: SDN-based Security Policy Enforcement for Robotic Applications
Rivera, Sean UL; Lagraa, Sofiane UL; State, Radu UL et al

in IEEE Workshop on the Internet of Safe Things, Co-located with IEEE Security and Privacy 2019 (2019, May)

Abstract—In this paper we propose ROS-Defender, a holistic approach to secure robotics systems, which integrates a Security Event Management System (SIEM), an intrusion prevention system (IPS) and a ... [more ▼]

Abstract—In this paper we propose ROS-Defender, a holistic approach to secure robotics systems, which integrates a Security Event Management System (SIEM), an intrusion prevention system (IPS) and a firewall for a robotic system. ROS-Defender combines anomaly detection systems at application (ROS) level and network level, with dynamic policy enforcement points using software defined networking (SDN) to provide protection against a large class of attacks. Although SIEMs, IPS, and firewall have been previously used to secure computer networks, ROSDefender is applying them for the specific use case of robotic systems, where security is in many cases an afterthought. [less ▲]

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See detailReal-time attack detection on robot cameras: A self-driving car application
Lagraa, Sofiane UL; Cailac, Maxime; Rivera, Sean UL et al

in International Conference on Robotic Computing (2019, February)

The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected ... [more ▼]

The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car. [less ▲]

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See detailDeep mining port scans from darknet
Lagraa, Sofiane UL; Chen, Yutian; François, Jérôme

in International Journal of Network Management (2019)

TCP/UDP port scanning or sweeping is one of the most common technique used 3 by attackers to discover accessible and potentially vulnerable hosts and applications. Although extracting and distinguishing ... [more ▼]

TCP/UDP port scanning or sweeping is one of the most common technique used 3 by attackers to discover accessible and potentially vulnerable hosts and applications. Although extracting and distinguishing different port scanning strategies is a challenging task, the identification of dependencies among probed ports is primordial for profiling attacker behaviors, with a final goal of better mitigating them. In this paper, we propose an approach that allows to track port scanning behavior patterns among multiple probed ports and identify intrinsic properties of observed group of orts. Our method is fully automated based on graph modeling and data mining techniques, including text mining. It provides to security analysts and operators relevant information about services that are jointly targeted by attackers. This is helpful to assess the strategy of the attacker by understanding the types of applications or environment he or she targets. We applied our method to data collected through a large Internet telescope (or darknet). [less ▲]

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See detailROSploit: Cybersecurity tool for ROS
Rivera, Sean UL; Lagraa, Sofiane UL; State, Radu UL

in International Conference on Robotic Computing (2019, February)

Abstract—Robotic Operating System(ROS) security research is currently in a preliminary state, with limited research in tools or models. Considering the trend of digitization of robotic systems, this lack ... [more ▼]

Abstract—Robotic Operating System(ROS) security research is currently in a preliminary state, with limited research in tools or models. Considering the trend of digitization of robotic systems, this lack of foundational knowledge increases the potential threat posed by security vulnerabilities in ROS. In this article, we present a new tool to assist further security research in ROS, ROSploit. ROSploit is a modular two-pronged offensive tool covering both reconnaissance and exploitation of ROS systems, designed to assist researchers in testing exploits for ROS. [less ▲]

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See detailUne nouvelle approche pour la détection d’anomalies dans les flux de graphes hétérogènes
Kiouche, Abd Errahmane; Amrouche, Karima; Seba, Hamida et al

in EGC (2019)

In this work, we propose a new approach to detect anomalous graphs in a stream of di- rected and labeled heterogeneous graphs. Our approach uses a new representation of graphs by vectors. This ... [more ▼]

In this work, we propose a new approach to detect anomalous graphs in a stream of di- rected and labeled heterogeneous graphs. Our approach uses a new representation of graphs by vectors. This representation is flexible and allows to update the graph vectors as soon as a new edge arrives. In addition, it is applicable to any type of graph and optimizes memory space. Moreover, it allows the detection of anomalies in real-time. [less ▲]

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See detailGraph-based malicious login events investigation
Amrouche, Faouzi UL; Lagraa, Sofiane UL; Kaiafas, Georgios UL et al

in Amrouche, Faouzi; Lagraa, Sofiane; Kaiafas, Georgios (Eds.) et al 16th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2019) (2019)

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See detailAn Experimental Analysis of Fraud Detection Methods in Enterprise Telecommunication Data using Unsupervised Outlier Ensembles
Kaiafas, Georgios UL; Hammerschmidt, Christian UL; Lagraa, Sofiane UL et al

in Kaiafas, Georgios; Hammerschmidt, Christian; State, Radu (Eds.) 16th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2019) (2019)

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See detailKnowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock Exchanges
Lagraa, Sofiane UL; Charlier, Jérémy Henri J. UL; State, Radu UL

Poster (2018, August 20)

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one ... [more ▼]

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one being Bitcoin. However, these crypto-currencies are so far relatively poorly analyzed and investigated. Recently, many solutions, mostly based on ad-hoc engineered solutions, are being developed to discover relevant analysis from crypto-currencies, but are not sufficient to understand behind crypto-currencies. In this paper, we provide a deep analysis of crypto-currencies by proposing a new knowledge discovery approach for each crypto-currency, across crypto-currencies, blockchains, and financial stocks. The novel approach is based on a conjoint use of data mining algorithms on imbalanced time series. It automatically reports co-variation dependency patterns of the time series. The experiments on the public crypto-currencies and financial stocks markets data also demonstrate the usefulness of the approach by discovering the different relationships across multiple time series sources and insights correlations behind crypto-currencies. [less ▲]

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See detailDetecting Malicious Authentication Events Trustfully
Kaiafas, Georgios UL; Varisteas, Georgios UL; Lagraa, Sofiane UL et al

in Kaiafas, Georgios; Varisteas, Georgios; Lagraa, Sofiane (Eds.) et al IEEE/IFIP Network Operations and Management Symposium, 23-27 April 2018, Taipei, Taiwan Cognitive Management in a Cyber World (2018)

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See detailProfiling Smart Contracts Interactions Tensor Decomposition and Graph Mining.
Charlier, Jérémy Henri J. UL; Lagraa, Sofiane UL; State, Radu UL et al

in Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017. (2017, September)

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and ... [more ▼]

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and allow economy of scale through the automated process. Smart contracts provides inherent benefits for financial institutions such as investment banking, retail banking, and insurance. This technology is widely used within Ethereum, an open source block-chain platform, from which the data has been extracted to conduct the experiments. In this work, we propose an multi-dimensional approach to find and predict smart contracts interactions only based on their crypto-currency exchanges. This approach relies on tensor modeling combined with stochastic processes. It underlines actual exchanges between smart contracts and targets the predictions of future interactions among the community. The tensor analysis is also challenged with the latest graph algorithms to assess its strengths and weaknesses in comparison to a more standard approach. [less ▲]

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See detailBotGM: Unsupervised Graph Mining to Detect Botnets in Traffic Flows
Lagraa, Sofiane UL; François, Jérôme; Lahmadi, Abdelkader et al

in CSNet 2017 Conference Proceedings (2017)

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks ... [more ▼]

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks, trojans, spams, etc. A large body of research has been accomplished on botnet detection, but recent security incidents show that there are still several challenges remaining to be addressed, such as the ability to develop detectors which can cope with new types of botnets. In this paper, we propose BotGM, a new approach to detect botnet activities based on behavioral analysis of network traffic flow. BotGM identifies network traffic behavior using graph-based mining techniques to detect botnets behaviors and model the dependencies among flows to traceback the root causes then. We applied BotGM on a publicly available large dataset of Botnet network flows, where it detects various botnet behaviors with a high accuracy without any prior knowledge of them. [less ▲]

Detailed reference viewed: 70 (2 UL)