Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Axis 2 : Attack detection

Intrusion detection in sequential control systems.

Sophisticated process-aware attacks targeting industrial control systems require adequate detection measures taking into account the physical process. In [20], we propose an approach relying on automatically mined process specifications to detect attacks on sequential control systems. The specifications are synthesized as monitors that read the execution traces and report violations to the operator. In contrast to other approaches, a central aspect of our method consists in reducing the number of mined specifications suffering from redundancies. We evaluate our approach on a hardware-in-the-loop testbed with a complex physical process model and discuss the mining efficiency and attack detection capabilities of our approach.

Hardware-based Information Flow Tracking

The HardBlare project proposes a software/hardware co-design methodology to ensure that security properties are preserved all along the execution of the system but also during files storage. It is based on the Dynamic Information Flow Tracking (DIFT) that generally consists in attaching tags to denote the type of information that are saved or generated within the system. These tags are then propagated when the system evolves and information flow control is performed in order to guarantee the safe execution and storage within the system monitored by security policies.

Existing hardware DIFT approaches have not been widely used neither by research community nor by hardware vendors. It is due to two major reasons: current hardware DIFT solutions lack support for multi-threaded applications and implementations for hardcore processors. In [10] we addresse both issues by introducing an approach with some unique features: DIFT for multi-threaded software, virtual memory protection (rather than physical memory as in related works) and Linux kernel support using an information flow monitor called RFBlare. These goals are accomplished by taking advantage of a notable feature of ARM CoreSight components (context ID) combined with a custom DIFT coprocessor and RFBlare. The communication time overhead, major source of slowdown in total DIFT time overhead, is divided by a factor 3.8 compared to existing solutions with similar software constraints as in this work. The area overhead of this work is lower than 1% and power overhead is 16.2% on a middle-class Xilinx Zynq SoC.

Most of hardware-assisted solutions for software security, program monitoring, and event-checking approaches require instrumentation of the target software, an operation which can be performed using an SBI (Static Binary Instrumentation) or a DBI (Dynamic Binary Instrumentation) framework. Hardware-assisted instrumentation can use one of these two solutions to instrument data to a memory-mapped register. Both these approaches require an in-depth knowledge of frameworks and an important amount of software modifications in order to instrument a whole application. In [11] we propose a novel way to instrument an application, at the source code level, taking advantage of underlying hardware debug components such as CS (CoreSight) components available on Xilinx Zynq SoCs. As an example, the instrumentation approach proposed in this work is used to detect a double free security attack. Furthermore, it is evaluated in terms of runtime and area overhead.

Alert correlation in intrusion detection.

In distributed systems and in particular in industrial SCADA environments, alert correlation systems are necessary to identify complex multi-step attacks within the huge amount of alerts and events. In [22] we describe an automata-based correlation engine developed in the context of a European project where the main stakeholder was an energy distribution company. The behavior of the engine is extended to fit new requirements. In the proposed solution, a fully automated process generates thousands of correlation rules. Despite this major scalability challenge, the designed correlation engine exhibits good performance. Expected rates of incoming low level alerts approaching several hundreds of elements per second are tolerated. Moreover, the data structures chosen allow to quickly handle dynamic changes of the set of correlation rules. As some attack steps are not observed, the correlation engine can be tuned to raise an alert when all the attack steps except k of them have been detected. To be able to react to an ongoing attack by taking countermeasures, alerts must also be raised as soon as a significant prefix of an attack scenario is recognized. Fulfilling these additional requirements leads to an increase in the memory consumption. Therefore purge mechanisms are also proposed and analyzed. An evaluation of the tool is conducted in the context of a SCADA environment.

Most recent and frequent items in distributed streams for DDoS detection.

The need to analyze in real time large-scale and distributed data streams has recently became tremendously important to detect attacks (DDoS), anomalies or performance issues. In particular the identification of recent heavy-hitters (or hot items) is essential but highly challenging. Actually, this problem has been heavily studied during the last decades with both exact and probabilistic solutions. While simple to state and fundamental for advanced analysis, answering this issue over a sliding time window and among distributed nodes is still an active research field. The distributed detection of frequent items over a sliding time window presents two extra challenging aspects with respect to the centralized detection of frequent items since the inception of the stream: (i) Treat time decaying items as they enter and exit the sliding window; (ii) Produce mergeable local stream summaries in order to obtain a system-wide summary. In [12], we propose a sliding window-based solution of the top k most frequent items based on a deterministic counting of the most over-represented items in the data streams, which are themselves probabilistically identified using a dynamically defined threshold. Performance of our new algorithm are astonishingly good, despite any items order manipulation or distributed execution.

Propagation of information.

Together with Yves Mocquard and Bruno Sericola, we have worked on the well studied dissemination of information in large scale distributed networks through pairwise interactions. The information to be propagated can simply be a bit of information to any code, including viruses. This problem, originally called rumor mongering, and then rumor spreading has mainly been investigated in the synchronous model. This model relies on the assumption that all the nodes of the network act in synchrony, that is, at each round of the protocol, each node is allowed to contact a random neighbor. In this paper, we drop this assumption under the argument that it is not realistic in large scale systems. We thus consider the asynchronous variant, where at random times, nodes successively interact by pairs exchanging their information on the rumor. In a previous paper, we performed a study of the total number of interactions needed for all the nodes of the network to discover the rumor. While most of the existing results involve huge constants that do not allow us to compare different protocols, we provided a thorough analysis of the distribution of this total number of interactions together with its asymptotic behavior [4]. In addition to this study, we have proposed an algorithm that allows, through simple pairwise interactions, each node of the large scale and dynamic system to build a global clock which allows any node to maintain with high probability a common temporal referential [25]. By combining this global clock together with the rumor spreading algorithm, we have proposed a mechanism that allows each node to locally detect that the system has converged to a sought configuration with high probability. We have also shown the applicability of our convergence detection mechanism to many other pairwise interaction-based protocols. For instance, our construction can be applied to a leader election protocol provided that its convergence time is known with high probability [26].