Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Zhang, Bin; Waqas, Muhammad; Tu, Shanshan; Hussain, Syed Mudassir; Rehman, Sadaqat Ur

doi:10.32604/cmc.2021.016553

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…IDS systems are classified into two categories depending on their signature-matching capabilities: signature-based intrusion detection systems (SIDS) and anomaly-based intrusion detection systems (AIDS) [11]. The term "signature-based intrusion detection" is often referred to as misuse detection or rule-based detection [12].…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Yang¹,

Tu²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a twolayered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Yang¹,

Tu²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The communication between Physical Wireless components and Cyber components of industrial CPWC systems is vulnerable to many threats, like MITM, eavesdropping, spoofing, etc. Physical-Layer Authentication is used to handle such attacks [27], which works better in stationary scenarios, but its performance degrades when the wireless components are mobile. Supervised ML classification algorithms predict whether the received signal is legitimate or illegitimate.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The proposed method in [26] detects spoofer in the Fog network using State Action Reward State Action (SARSA) and Qlearning techniques. Some previously proposed approaches include extracting channel difference using difference equations [27] and then identifying an optimal threshold value using the hit and trial method. This model achieved the best accuracy of 49.7% when the receiver and transmitter were stationary.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Authentication Using Ensemble Learning Technique in燱ireless Communications

Waqas¹,

Bano²,

Hassan³

et al. 2022

Computers, Materials &Amp; Continua

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Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks. Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target. These methods perform well but only in stationary scenarios. We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios. The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data. The authentication of the received signal is considered a binary classification problem. The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information. Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks. It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile. The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

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Blockchain Enable IoT Using Deep Reinforcement Learning: A Novel Architecture to Ensure Security of Data Sharing and Storage

Bai

Waqas

et al. 2022

Lecture Notes in Computer Science

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Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Cited by 4 publications

References 22 publications

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

Physical Layer Authentication Using Ensemble Learning Technique in燱ireless Communications

Blockchain Enable IoT Using Deep Reinforcement Learning: A Novel Architecture to Ensure Security of Data Sharing and Storage

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