Classification of Cyber-Attack using Adaboost Regression Classifier and Securing the Network

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Bdah Mohammed Mubarak AlShahrani, et. al.


In recent years, with adverse development of technology leads to several security breaches. To withstand those security threats and breaches especially for cyber attacks resources are optimized with improved network lifetime. Those security challenges are lead to confidentiality, privacy, integrity and availability. To prevent cyberattacks artificial intelligence-based technology is evolved. To adopt appropriate cybersecurity wireless communication systems are intended to withstand threats and challenges. This paper, presented a deep learning-based classification technique for cyber attack detection. Deep learning structure involved in attack detection with proposed AdaBoost Regression Classifier (ABRC). The proposed ABRC with deep learning involved in estimation of attacks in the network security with deep learning structure.  The proposed classifier model is involved in estimation of threats. The developed algorithm integrates AdaBoost and Regression classifier for threat detection and classification. The performance analysis expressed that proposed ABRC exhibits significant performance for cyber-attack detection than the existing deep learning technique.

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