Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

Ferrag, Mohamed Amine; Μαγλαράς, Λέανδρος; Moschoyiannis, Sotiris; Janicke, Helge

doi:10.1016/j.jisa.2019.102419

Cited by 469 publications

(254 citation statements)

References 102 publications

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“…Both DeepCoin and DeliveryCoin frameworks demonstrated the efficiency of the deep learning techniques in cyber security intrusion detection. For more detail about the deep learning techniques in cyber security intrusion detection, we refer the reader to our recent studies in [26,27].…”

Section: Relevant Workmentioning

confidence: 99%

RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

et al. 2020

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This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.

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Section: Relevant Workmentioning

confidence: 99%

RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

et al. 2020

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“…Moreover, when such datasets are available, permission to experimentally run attacks is not easily obtained, the process is costly, and there are complex security and privacy issues. Consequently, most of the work reported in the literature is based on artificially generated datasets [36], [45]. In this paper, we have the advantage of having access to a real dataset.…”

Section: Experimental Evaluation a Synthetic Replay Attack Datasetmentioning

confidence: 99%

Replay Attack Detection in Smart Cities Using Deep Learning

et al. 2020

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Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research. INDEX TERMS Smart cities, intrusion detection, replay attack, deep learning, convolutional neural networks.

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“…In [51], the current deep learning approaches have been surveyed for intrusion detection. Because dataset play a major role in detecting intruders, 35 of the most popular cyber-datasets are utilized, which are classified into seven categories.…”

Section: The Role Of Artificial Intelligence In Sustainable Cybersecumentioning

confidence: 99%

Toward a Sustainable Cybersecurity Ecosystem

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Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community.

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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

Cited by 469 publications

References 102 publications

RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

Replay Attack Detection in Smart Cities Using Deep Learning

Toward a Sustainable Cybersecurity Ecosystem

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