109,313 research outputs found
Intrusion Detection System for Platooning Connected Autonomous Vehicles
The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
Efficient Database Generation for Data-driven Security Assessment of Power Systems
Power system security assessment methods require large datasets of operating
points to train or test their performance. As historical data often contain
limited number of abnormal situations, simulation data are necessary to
accurately determine the security boundary. Generating such a database is an
extremely demanding task, which becomes intractable even for small system
sizes. This paper proposes a modular and highly scalable algorithm for
computationally efficient database generation. Using convex relaxation
techniques and complex network theory, we discard large infeasible regions and
drastically reduce the search space. We explore the remaining space by a highly
parallelizable algorithm and substantially decrease computation time. Our
method accommodates numerous definitions of power system security. Here we
focus on the combination of N-k security and small-signal stability.
Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show
how it outperforms existing approaches requiring less than 10% of the time
other methods require.Comment: Database publicly available at:
https://github.com/johnnyDEDK/OPs_Nesta162Bus - Paper accepted for
publication at IEEE Transactions on Power System
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector
Machine Applications
- …