Guest Editors
Dr. Muhammad Adnan Khan, Gachon University, Korea.
Dr. Muhammad Aamer Saleem, Hamdard University, Pakistan.
Dr. Rizwan Ali Naqvi, Sejong University, Korea.
Summary
Artificial intelligence (AI) and machine learning (ML) have been put thoroughly into practice, with more promotion being given, to enhance continuity, cybersecurity in cloud computing, Internet services, and the Internet-of-Things. Machine learning algorithms, such as AI, are used to track complex cyber threats that cannot be readily identified by conventional detection methods. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost.
By far, the most difficult challenge is how to exploit AI and machine learning algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning as well as the reliability and privacy of data is an important part of today's modern computing. This topic aims to determine the security issues of using AI to protect systems. To be able to enforce them in reality, privacy would have to be held throughout the implementation process.
In this special issue of the journal, we are finding groundbreaking applications and undisclosed work related to artificial intelligence and machine learning for more stable and privacy cleaning computing. By reflecting on the role of machine learning in information security, we are looking to discuss recent developments in the area of machine learning and privacy-preserving strategies. To make our computation more secure and confidential, we aim to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and privacy of information.
Keywords
Suggested topics include, but are not limited to, the following:
• Cybersecurity
• Spam Detection
• Secure online social networks
• Anomaly and intrusion detection in the network
• Malware analysis and detection
• Security models based AI for protecting IoT networks
• Intrusion Detection for IoT systems
• Distributed AI Systems and Architectures
• eBusiness, eCommerce, eHealth, eLearning
• Finance and AI
• Extreme Machine Learning
• Applications of neural networks in data analytics
• CNN, LSTM
• Automation and control system
• Smart mobility and transportation
• Signal and Image Processing
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