Survey on Wireless Network Security

Nazir, Rashid; Laghari, Asif Ali; Kumar, Kamlesh; David, Shibin; Ali, Murtaza

doi:10.1007/s11831-021-09631-5

Cited by 29 publications

(20 citation statements)

References 86 publications

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“…In a more general look, the studies powered on AI have been proposed for various fields of security, including but not limited to, Internet of Things (IoT), [24][25][26] network, [27][28][29] and vehicle and street crime detection. 30,31 An overview of the comparison of the related work in terms of employed techniques, classification modes, and their evaluation performance in terms of obtained accuracy is given in Table 1.…”

Section: Related Workmentioning

confidence: 99%

A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

Kabakuş

2022

Concurrency and Computation

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Summary Uniform resource locator (URL)‐based cyber‐attacks form a major part of security threats in cyberspace. Even though the experience and awareness of the end‐users help them protect themselves from these attacks, a software‐based solution is necessary for comprehensive protection. To this end, a novel robust URL classification model based on convolutional neural network is proposed in this study. The proposed model classifies given URLs into five classes, namely, (normali$$ \mathrm{i} $$) benign$$ \mathrm{benign} $$, (normalii$$ \mathrm{ii} $$) defacement$$ \mathrm{defacement} $$, (normaliii$$ \mathrm{iii} $$) phishing$$ \mathrm{phishing} $$, (normaliv$$ \mathrm{iv} $$) spam$$ \mathrm{spam} $$, and (normalv$$ \mathrm{v} $$) malware$$ \mathrm{malware} $$. The proposed model was trained and evaluated on a gold standard URL dataset comprising of 36,707$$ \mathrm{36,707} $$ samples. According to the experimental result, the proposed model obtained an accuracy as high as 98.1%$$ 98.1\% $$ which outperformed the state‐of‐the‐art. Based on the same architecture, we proposed another classifier, a binary classifier that detects malicious URLs without dealing with their types. This binary classifier obtained an accuracy as high as 99.3%$$ 99.3\% $$ which outperformed the state‐of‐the‐art as well. The experimental result demonstrates the feasibility of the proposed solution.

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Section: Related Workmentioning

confidence: 99%

A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

Kabakuş

2022

Concurrency and Computation

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“…Such attacks prevent legitimate users from accessing the network [2,3]. It is worth noting that DoS attacks differ from selfish behaviour motivated by possible beneficial outcomes [4,5]. Due to the broadcast nature of wireless networks, DoS attacks can be easily carried out, particularly in the wireless domain.…”

Section: Introductionmentioning

confidence: 99%

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

Abdallah¹,

Hamdan²,

Razak³

et al. 2023

Computers, Materials &Amp; Continua

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IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classificationbased methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today's real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection.

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“…In today's society, the Internet has affected the entire social process. e application for the Internet is becoming diversified, and the scale of netizens continues to show a trend of sustained and rapid development [1,2]. At this stage, China's cyber security situation is becoming more severe and complex, and cyber security incidents are becoming more complex.…”

Section: Introductionmentioning

confidence: 99%

Computer Network Intrusion Anomaly Detection with Recurrent Neural Network

2022

Mobile Information Systems

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Network intrusion anomaly detection technique has been widely employed in computer network environments as a highly effective security prevention method. As network technology and network applications have advanced at a rapid pace, so too has network data traffic, resulting in an increase in virus and attack kinds. In the face of large-scale traffic and characteristic information, traditional intrusion detection will have problems such as low detection accuracy, high false negatives, and reliance on dimensionality reduction algorithms. Therefore, it is particularly important to establish a fast and efficient network intrusion anomaly detection method to deal with the current complex network environment. This work designs a computer network intrusion detection model with a recurrent neural network in order to explore a new intrusion detection method. The main purpose of this article include the following: (1) design a network security emergency response system architecture with the recurrent neural network model. This system consists of a management center module, a knowledge database module, a data acquisition module, a risk detection tool module, a risk analysis and processing module, a data protection module, and a remote connection auxiliary module. The modules cooperate with each other to complete system functions. (2) Aiming at the risk analysis and processing module, a network intrusion detection model combining bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) is designed. In view of the lack of consideration of the before-and-after relevance of intrusion data features and the multifeature problem in existing models, the use of BiLSTM to extract the relevance between features and the use of DNN to extract deeper features are proposed. Aiming at the problem that the model lacks consideration of the importance of features, it is proposed to embed an attention mechanism into the network to increase consideration for the importance of features. (3) Massive experiments have verified the reliability and effectiveness of the method proposed in this work.

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Survey on Wireless Network Security

Cited by 29 publications

References 86 publications

A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

Computer Network Intrusion Anomaly Detection with Recurrent Neural Network

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