Anomaly detection of network-initiated LTE signaling traffic in wireless sensor and actuator networks based on a Hidden semi-Markov Model

Bang, June-ho; Cho, Young-Jong; Kang, Kyungran

doi:10.1016/j.cose.2016.11.008

Cited by 37 publications

(20 citation statements)

References 17 publications

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“…In past several studies (see [7], [8], [9] and [10]), researchers have employed classical machine learning mechanism such as SVM, K-Nearest Neighbour (KNN), ANN, Random Forest etc. for developing an IDS.…”

Section: Related Workmentioning

confidence: 99%

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Tang

Mhamdi

McLernon

et al. 2018

2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)

242

129

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Section: Related Workmentioning

confidence: 99%

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Tang

Mhamdi

McLernon

et al. 2018

2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)

242

129

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“…The hidden semi-markov model (Bang, Cho, & Kang, 2017) is used to detect anomaly in wireless sensor actuator network, based on the Long-Term Evolution (LTE) signalling traffic. This model improves the detection sensitivity result and shows more attack alarms with false positive and true negative ration.…”

Section: Current Anomalies Detection Techniquesmentioning

confidence: 99%

Real-time big data processing for anomaly detection: A Survey

Habeeb¹,

Nasaruddin²,

Gani³

et al. 2019

International Journal of Information Management

340

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The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.

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“…Before the development of DNN variants, classical ML algorithms, such as random forest (RF), SVM, ANN, and k-nearest neighbors (KNN) were used by various researchers to develop IDSs [9][10][11][12]. However, these methods have inherent limitations.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

Albahar

2019

Security and Communication Networks

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Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.

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Anomaly detection of network-initiated LTE signaling traffic in wireless sensor and actuator networks based on a Hidden semi-Markov Model

Cited by 37 publications

References 17 publications

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Real-time big data processing for anomaly detection: A Survey

Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

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