Grégory Blanc

Mustafizur Rahman Shahid, Gregory Blanc, Zonghua Zhang et al.

Nowadays, IoT devices have been widely deployed for enabling various smart services, such as, smart home or e-healthcare. However, security remains as one of the paramount concern as many IoT devices are vulnerable. Moreover, IoT malware are constantly evolving and getting more sophisticated. IoT devices are intended to perform very specific tasks, so their networking behavior is expected to be reasonably stable and predictable. Any significant behavioral deviation from the normal patterns would indicate anomalous events. In this paper, we present a method to detect anomalous network communications in IoT networks using a set of sparse autoencoders. The proposed approach allows us to differentiate malicious communications from legitimate ones. So that, if a device is compromised only malicious communications can be dropped while the service provided by the device is not totally interrupted. To characterize network behavior, bidirectional TCP flows are extracted and described using statistics on the size of the first N packets sent and received, along with statistics on the corresponding inter-arrival times between packets. A set of sparse autoencoders is then trained to learn the profile of the legitimate communications generated by an experimental smart home network. Depending on the value of N, the developed model achieves attack detection rates ranging from 86.9% to 91.2%, and false positive rates ranging from 0.1% to 0.5%.

Aguessy François-Xavier, Bettan Olivier, Blanc Grégory et al.

Because of the threat of advanced multi-step attacks, it is often difficult for security operators to completely cover all vulnerabilities when deploying remediations. Deploying sensors to monitor attacks exploiting residual vulnerabilities is not sufficient and new tools are needed to assess the risk associated to the security events produced by these sensors. Although attack graphs were proposed to represent known multi-step attacks occurring in an information system, they are not directly suited for dynamic risk assessment. In this paper, we present the Bayesian Attack Model (BAM), a Bayesian network-based extension to topological attack graphs, capable of handling topological cycles, making it fit for any information system. Evaluation is performed on realistic topologies to study the sensitivity of its probabilistic parameters.

Gustavo Gonzalez-Granadillo, Christophe Ponchel, Gregory Blanc et al.

Research in information security has generally focused on providing a comprehensive interpretation of threats, vulnerabilities, and attacks, in particular to evaluate their danger and prioritize responses accordingly. Most of the current approaches propose advanced techniques to detect intrusions and complex attacks but few of these approaches propose well defined methodologies to react against a given attack. In this paper, we propose a novel and systematic method to select security countermeasures from a pool of candidates, by ranking them based on the technical and financial impact associated to each alternative. The method includes industrial evaluation and simulations of the impact associated to a given security measure which allows to compute the return on response investment for different candidates. A simple case study is proposed at the end of the paper to show the applicability of the model.