The analysis performance of an Intrusion Detection Systems based on Neural Network

Farissi, Ilhame El; Saber, M.; Chadli, Sara; Emharraf, Mohamed; Belkasmi, Mohammed Ghaouth

doi:10.1109/cist.2016.7805032

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Thus, the performance of an IDS can be analyzed using these four important key factors as shown in Table IV. Thus, from these four terms performance of an IDS can be easily calculated using the following equations [40]: P recision, P = T P (T P + F P )…”

Section: Discussionmentioning

confidence: 99%

G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

Shahriar¹,

Haque²,

Rahman³

et al. 2020

Preprint

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The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS technologies suffer from imbalanced and missing sample data, which makes the training of IDS difficult. In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones. G-IDS also fixes the difficulties of imbalanced or missing data problems. We model a network security dataset for an emerging CPS technologies using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. We find that our proposed G-IDS model performs much better in attack detection and model stabilization during the training process than a standalone IDS.

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Section: Discussionmentioning

confidence: 99%

G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

Shahriar¹,

Haque²,

Rahman³

et al. 2020

Preprint

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“…The measures used to assess IDS performance depend on four factors, namely, the numbers of true positives (α), true negatives (δ), false positives (γ ) and false negatives (β), as described in Table 2. Following [85,86], these factors and the performance metrics for IDSs are described below.…”

Section: Idss: Performance Evaluationmentioning

confidence: 99%

Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

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