Flow-based anomaly intrusion detection system using two neural network stages

Abuadlla, Yousef; Kvaščev, Goran; Gajin, Slavko; Jovanović, Zoran

doi:10.2298/csis130415035a

Cited by 39 publications

(18 citation statements)

References 97 publications

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“…Abuadlla et al in his work [1] deals with issues of attack on a large network. Focus was also on the computer systems where MLP network was used to identify time windows during which the attacks were most likely to happen, all on the basis of significant changes in traffic over time.…”

Section: Application Of Multilayered Perceptrons From the Aspect Of Tmentioning

confidence: 99%

A system for deductive prediction and analysis of movement of basketball referees

Pecev

Racković

Ivković

2015

Multimed Tools Appl

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This paper presents us with an automatic prediction and analysis of basketball referees movement which is useful for educational software. Such software would be very beneficial in training the young basketball referees. The paper proposes that the movement prediction of basketball referees can be achieved with a multilayered perceptron neural network. Network will reason on the basis of a ball movement during a play action. Proposed neural network will be trained with a modified Back Propagation algorithm which essentially presents a special algorithm for a multiple dependent Time Series prediction. In this paper, we will also describe initial designs of a neural network structure that, we believe, would better suit the nature of a multiple dependent Time Series prediction problems. The aforementioned educational software is capable of determining whether a referee was moving properly in a certain situation or not. Determination is possible on the basis of numerical values that are calculated by simulating the human visual field. The referee's horizontal field of view simulation is based on the standard set by the American Optometric Association. It is implemented through a modified Sweep and Prune algorithm which is also discussed in this paper.Keywords Multiple dependent time series · MLP neural networks · LTR -MDTS model · Field of vision simulation · Basketball referee movement Predrag Pecev

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Section: Application Of Multilayered Perceptrons From the Aspect Of Tmentioning

confidence: 99%

A system for deductive prediction and analysis of movement of basketball referees

Pecev

Racković

Ivković

2015

Multimed Tools Appl

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“…Abuadlla et al [13] proposed a two-stage intrusion detection and classification method by taking advantage of two separate neural networks for each task. The first stage detects traffic anomalies whereas the next stage classifies the attacks as they occur.…”

Section: Related Workmentioning

confidence: 99%

Towards an efficient anomaly‐based intrusion detection for software‐defined networks

Latah

Toker

2018

IET Networks

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Software-Defined Networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which is responsible for managing the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-Nearest Neighbor approach (kNN); for the second level, we use the Extreme Learning Machine (ELM); and for the remaining levels, we utilize the Hierarchical Extreme Learning Machine (H-ELM) approach. In comparison with conventional supervised machine learning algorithms based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves the highest level of accuracy (84.29%). Therefore, our approach presents an efficient approach for intrusion detection in SDNs.

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“…The natural fastness of neural networks in detecting and recognizing attacks has influenced many researchers [7] [10], [11], [ 12], [13], [14], [15], [16] so as to make them get interested in this universal scheme for intrusion detection. The learning processes used for modifying the network's settings used by the researchers are the following:…”

Section: Related Workon Neural Network-basedidsmentioning

confidence: 99%

“…Yousef Abuadlla && all [16] suggested a behavioral IDS based on traffic networks and two levels of neural networks. The first level makes possible the changes within the traffic and checks whether it is an attack or not.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Network Intrusion Detection Systems based Neural Network: A Comparative Study

Djionang¹,

Tindo²

2017

IJCA

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Neural networks are artificial learning systems. For more than two decades, they have help for detecting hostile behaviors in a computer system. This review describes those systems and theirs limits. It defines and gives neural networks characteristics. It also itemizes neural networks which are used in intrusion detection systems. The state of the art on IDS made from neural networks is reviewed. In this paper, we also make a taxonomy and a comparison of neural networks intrusion detection systems. We end this review with a set of remarks and future works that can be done in order to improve the systems that have been presented. This work is the result of a meticulous scan of the literature.

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Flow-based anomaly intrusion detection system using two neural network stages

Cited by 39 publications

References 97 publications

A system for deductive prediction and analysis of movement of basketball referees

A system for deductive prediction and analysis of movement of basketball referees

Towards an efficient anomaly‐based intrusion detection for software‐defined networks

Network Intrusion Detection Systems based Neural Network: A Comparative Study

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