Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions

Aleesa, Ahmed Marwan; Zaidan, B. B.; Zaidan, A. A.; Sahar, Nan Mad

doi:10.1007/s00521-019-04557-3

Cited by 68 publications

(36 citation statements)

References 74 publications

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“…The researchers of [91] review deep learning for the detection of cyber security intrusion. In [92], the authors review deep learning-based intrusion detection systems. The authors of [93] conducted a study of cybersecurity deep learning methods.…”

Section: Cybersecurity Data and Systemsmentioning

confidence: 99%

“…The strongest aspect of deep learning techniques is effectively learning feature hierarchies based on the patterns in the data [92]. Several unsupervised techniques such as autoencoder (AE), deep belief network (DBN), restricted Boltzmann machines (RBMs), generative adversarial network (GAN) etc., can also be used in the domain of cybersecurity [90,92]. Hybrid techniques can also be used for significant outcomes in several cases [92].…”

Section: Machine Learning-based Modelingmentioning

confidence: 99%

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AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

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Section: Cybersecurity Data and Systemsmentioning

confidence: 99%

Section: Machine Learning-based Modelingmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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“…The search strategy used in this work followed the method employed by Aleesa et al [52], as outlined in Figure 1. This includes the following four (4) stages:…”

Section: Review Research Methodsmentioning

confidence: 99%

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

Silva

Neto

et al. 2020

Sensors

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The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.

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“…An efficient way to detect network scanning attacks is the data mining of network traffic based on a neural network approach [14][15][16][17]. This approach is based on the creation of a neural network trained with the use of data that include the signs of a network scanning attack.…”

Section: Methodsmentioning

confidence: 99%

Neural Network Model for Detecting Network Scanning Attacks

Panischev¹,

Makridin²,

Katasev³

et al. 2020

International Journal of Engineering Research and Technology

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Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions

Cited by 68 publications

References 74 publications

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

Neural Network Model for Detecting Network Scanning Attacks

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