![]() Sudharsan, Sundaram D, Patel P, Breslin JG, Ali MI (2021) Edge2Guard: Botnet attacks detecting offline models for resource constrained IoT devices. Int J Electr Comput Eng (IJECE) 10(2):2182 Electron 9(10):1565Īl-Duwairi, Al-Kahla W, AlRefai MA, Abedalqader Y, Rawash A, Fahmawi R (2020) SIEM-based detection and mitigation of IoT-botnet DDoS attacks. Lawal, Shaikh RA, Hassan SR (2020) An anomaly mitigation framework for IoT using fog computing. Haji, Ameen SY (2021) ‘Attack and anomaly detection in IoT networks using machine learning techniques: A review. Yamashita, Nishio M, Do RKG, Togashi K (2018) ‘Convolutional neural networks: An overview and application in radiology. Am Acad Sci Res J Eng Technol Sci 77(1):76–89 ![]() 837–842Ĭheng, Regedzai GR (2021) A survey on botnet attacks. Ji LY, Liu S, Yao H, Ye Q, Wang R (2018) The study on the botnet and its prevention policies in the Internet of Things. Ngo H-TN, Le V-H, Nguyen D-H (2020) A survey of IoT malware and detection methods based on static features. Xiao Y, Xing C, Zhang T, Zhao Z (2019) An intrusion detection model based on feature reduction and convolutional neural networks. Ībdallah NALK, Jahromi H, Jurcut AD (2021) A hybrid CNN LSTM based approach for anomaly detection systems in SDNs. Expert Syst 39(5):e12753įraccaroli QD (2020) Engineering IoT networks. IOP Conf Ser Mater Sci Eng 928(3):032027īhushan Haque B, Dhiman G (2022) Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends. Jabbar AF, Mohammed IJ (2020) Development of an optimized botnet detection framework based on filters of features and machine learning classifiers using CICIDS2017 dataset. The proposed model gives 99.3%, 99.5%, 99.5%, 99.6%, 99%, 98.9%, 99% accuracy with normal attack detection, botnet attack detection, Brute force attack detection, DoS attack detection, Infiltration attack detection, Portscan attack detection and web attack detection respectively.Īnand YS, Selway A, Alazab M, Tanwar S, Kumar N (2020) IoT vulnerability assessment for sustainable computing: Threats, current solutions, and open challenges. The proposed model gives better trade-off compared to existing approaches like Deep Belief Networks (DBN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Supervised Neural Networks (SNN) and Feed Forward Neural Networks (FNN). Recent tests and simulations show how effective the security control strategy is active. The addressed CPS can be asymptotically stable against DoS assaults under the security controller's active security control technique without sacrificing control performance. Using the CICIDS dataset for attack detection, we examined the effectiveness of the Deep Convolutional Network Model (DCNM), a suggested deep learning model. A proactive security control method is then developed to combat two-channel DoS attacks, depending on a method for identifying IoT intrusions. ![]() Due to attack cost restrictions, the linked channels are subject to a limit on the number of continuous DoS attacks. This study develops an active security control strategy for Cyber-Physical Systems (CPSs) that are subject to attacks known as Denial-of-Service (DoS), which can target both channels from the controller to the actuator and from the controller to the sensor.
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