Dr. Maha Driss
Security Engineering Lab
Computer Science Department, College of Computer and Information Systems, Prince Sultan University, KSA
RIADI Laboratory, ENSI, University of Manouba, Tunisia
E-mail: mdriss@psu.edu.sa


Dr. Iman Almomani
Security Engineering Lab
Computer Science Department, College of Computer and Information Systems, Prince Sultan University, KSA
E-mail: imomani@psu.edu.sa


Dr. Wadii Boulila
Robotics and Internet of Things Research Lab
Computer Science Department, College of Computer and Information Systems, Prince Sultan University, KSA
RIADI Laboratory, ENSI, University of Manouba, Tunisia
E-mail: wboulila@psu.edu.sa


Prof. Anis Koubaa
Robotics and Internet of Things Research Lab
Computer Science Department, College of Computer and Information Systems, Prince Sultan University, KSA
E-mail: akoubaa@psu.edu.sa

In the past few years, the Internet of Things (IoT) technologies have resulted in the emergence of several smart applications in various critical areas such as smart health, mobility, energy, cities, etc. IoT intends to link a wide range of intelligent devices and monitor systems and smart appliances in order to regulate, store, share, and analyze gathered information. The major challenge with smart ecosystems is that they might expose various vulnerabilities that can be exploited by malevolent users, jeopardizing the safety and privacy of collected data. According to Cybersecurity Ventures, worldwide cybercrime expenditures would increase by 15% each year over the next five years, approaching $10.5 trillion in 2025, up from $3 trillion in 2015. As a result, there is a critical need to investigate and analyze the present state of malware, as well as to suggest appropriate solutions to safeguard our assets and smart applications from malware attacks. The MASE 2022 Special Session at the 14th International Conference on Computational Collective Intelligence (ICCCI 2022) aims to provide the most up-to-date findings on malware analytics techniques in the smart world. The goal of this special session is to encourage malware research in areas such as design approaches, quantum cryptography, automation techniques, and intrusion detection/classification/prevention systems. The topics covered in this special session include, but are not limited to:

• Evaluation of security and privacy vulnerabilities and threats in the smart world
• IoT security frameworks for automated malware detection/analysis
• AI-based systems for securing IoT environments against attacks
• IoT Botnet attack analysis and defensive solutions
• Lineage analysis of malware families
• Lightweight security models for smart applications
• Enhancing access control with improved authentication techniques in smart ecosystems
• New types of IoT malware and their defense mechanisms
• Novel machine learning techniques applied to IoT malware
• Deep-learning-based detection of mobile malware
• Classifier fusion and ensemble-learning-based approaches for malware analysis
• Network-based machine-learning-driven detection systems
• Distributed service-based architecture for IoT security
• Blockchain-based security solutions for smart ecosystems
• Security and privacy in IoT-based fog/edge computing applications
• Cloud-based/cloud-assisted large-scale defensive solutions in IoT ecosystems
• Memory forensics approaches to deal with IoT malware
• Adversarial machine learning techniques
• Federated learning-based intelligent applications for malware analysis