4,143 research outputs found
A Real-Time Remote IDS Testbed for Connected Vehicles
Connected vehicles are becoming commonplace. A constant connection between
vehicles and a central server enables new features and services. This added
connectivity raises the likelihood of exposure to attackers and risks
unauthorized access. A possible countermeasure to this issue are intrusion
detection systems (IDS), which aim at detecting these intrusions during or
after their occurrence. The problem with IDS is the large variety of possible
approaches with no sensible option for comparing them. Our contribution to this
problem comprises the conceptualization and implementation of a testbed for an
automotive real-world scenario. That amounts to a server-side IDS detecting
intrusions into vehicles remotely. To verify the validity of our approach, we
evaluate the testbed from multiple perspectives, including its fitness for
purpose and the quality of the data it generates. Our evaluation shows that the
testbed makes the effective assessment of various IDS possible. It solves
multiple problems of existing approaches, including class imbalance.
Additionally, it enables reproducibility and generating data of varying
detection difficulties. This allows for comprehensive evaluation of real-time,
remote IDS.Comment: Peer-reviewed version accepted for publication in the proceedings of
the 34th ACM/SIGAPP Symposium On Applied Computing (SAC'19
DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments
With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST
Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms
In this paper we present the design and evaluation of intrusion detection
models for MANETs using supervised classification algorithms. Specifically, we
evaluate the performance of the MultiLayer Perceptron (MLP), the Linear
classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and
the Support Vector Machine (SVM). The performance of the classification
algorithms is evaluated under different traffic conditions and mobility
patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks.
The results indicate that Support Vector Machines exhibit high accuracy for
almost all simulated attacks and that Packet Dropping is the hardest attack to
detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200
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