Detecting Denial of Service Attacks with Bayesian Classifiers and the Random Neural Network

Öke, Gülay; Loukas, George; Gelenbe, Erol

doi:10.1109/fuzzy.2007.4295666

Cited by 52 publications

(33 citation statements)

References 24 publications

(27 reference statements)

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“…The first goal of any protection scheme against DoS attack is the early detection (ideally long before the destructive traffic build-up) of its existence [22,21]. In order to disarm DoS/DDoS attacks and any deviation, not only the detection of the malevolent behavior must be achieved, but the network traffic belonging to the attackers should be also blocked [23,24,25].…”

Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

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Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

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“…Other extensions concern admission control (Gelenbe, Sakellari, and D'arienzo [134]) and distributed denial of service (DDoS) defense (Gelenbe, Gellman, and Loukas [82], Gelenbe and Loukas [117]). In DDoS, network attacks can be detected using CPN as QoS violations, and CPN can be modified to automatically counter-attack by tracing the attacking traffic and using CPN ACK packets to give "drop" orders regarding the attacking traffic to routers upstream (Oke, Loukas, and Gelenbe [170]). Network worms were also considered, and CPN was used to reroute the users' traffic to avoid the infected nodes (Sakellari and Gelenbe [176], Sakellari, Hey, and Gelenbe [177]).…”

Section: Autonomic Systems and Cognitive Packet Network (Cpns)mentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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“…It also interacts with existing network management systems to reduce computational overhead, storage requirements and false alarm rate. The use of a supervised learning RNN is motivated by its capability of classifying known patterns such as signaling storms whose characteristics and root causes are well understood [4,5,21,40], and also its previous success in detecting traditional DoS attacks in the Internet [35,52].…”

Section: Contributions Of the Papermentioning

confidence: 99%

Detecting Network-Unfriendly Mobiles With the Random Neural Network

Abdelrahman

2016

Prob. Eng. Inf. Sci.

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Mobile networks are universally used for personal communications, but also increasingly used in the Internet of Things and machine-to-machine applications in order to access and control critical services. However, they are particularly vulnerable to signaling storms, triggered by malfunctioning applications, malware or malicious behavior, which can cause disruption in the access to the infrastructure. Such storms differ from conventional denial of service attacks, since they overload the control plane rather than the data plane, rendering traditional detection techniques ineffective. Thus, in this paper we describe the manner in which storms happen and their causes, and propose a detection framework that utilizes traffic measurements and key performance indicators to identify in real-time misbehaving mobile devices. The detection algorithm is based on the random neural network which is a probabilistic computational model with efficient learning algorithms. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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Detecting Denial of Service Attacks with Bayesian Classifiers and the Random Neural Network

Cited by 52 publications

References 24 publications

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Detecting Network-Unfriendly Mobiles With the Random Neural Network

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