Guest Editors
Prof. Miodrag Zivkovic, Singidunum University, Serbia
Prof. Nebojsa Bacanin, Singidunum University, Serbia
Prof. Bosko Nikolic, University of Belgrade, Serbia
Summary
In an era dominated by digital connectivity and information exchange, the importance of robust network security measures cannot be overstated. As organizations and individuals alike become increasingly reliant on interconnected systems, the threat landscape for potential cyber-attacks continues to evolve. Next-generation network security represents a paradigm shift in how organizations approach the protection of their digital assets. With the ever-evolving landscape of cyber threats, traditional security measures have proven insufficient, prompting the development of more advanced and adaptive security strategies. One key aspect of next-generation network security is the emphasis on proactive threat prevention rather than reactive response. This involves leveraging advanced technologies such as artificial intelligence, machine learning, and behavioral analytics to identify and mitigate potential threats in real-time, enabling organizations to stay ahead of sophisticated attacks.
In the realm of next-generation network security, there is a heightened focus on the concept of Zero Trust. Unlike traditional security models that rely on perimeter defenses, Zero Trust assumes that threats can exist both inside and outside the network. This approach mandates continuous verification of the identity and security posture of users, devices, and applications, irrespective of their location. Micro-segmentation, another vital aspect of next-generation security, involves dividing networks into smaller, isolated segments, limiting lateral movement within the network and reducing the impact of a potential breach.
Moreover, next-generation network security extends its reach beyond the traditional boundaries of on-premises networks. As organizations increasingly adopt cloud services and hybrid infrastructures, security measures must adapt to protect data and applications in diverse environments. This entails the integration of cloud-native security solutions, robust encryption protocols, and comprehensive visibility into network activities. In essence, next-generation network security reflects a holistic and dynamic approach to cybersecurity, designed to address the intricacies of modern business operations and the evolving tactics of cyber adversaries.
The rise of the Internet of Things (IoT) and the integration of smart city infrastructures in essential industries expand the potential points of attack, creating opportunities for cyber warfare and illicit activities. Nonetheless, the proliferation of legacy technologies and systems strengthens operational frameworks, reinforcing entry points with advanced firewalls that secure pivotal sectors. Advanced networks, bolstered by layered vigilance and resilience and incorporating ML and AI solutions, play a pivotal role in empowering critical industries to detect and address threats proactively, ensuring the effective protection of their servers.
This special issue aims to explore the latest advancements, challenges, and innovative solutions in the field of network security, and investigate the role of artificial intelligence (AI) and machine learning (ML) in enhancing network security. Authors are invited to discuss the integration of AI algorithms for anomaly detection, threat intelligence, and automated incident response. The special issue delves into a wide array of topics related to network security, covering both theoretical and practical aspects. Contributions from researchers, practitioners, and experts in the field will shed light on emerging threats, cutting-edge technologies, and strategic approaches to safeguarding networks.
Keywords
Next-Generation Threats
Zero Trust Architecture
Micro-Segmentation
Artificial Intelligence in Network Security
Blockchain and Decentralized Security
Internet of Things (IoT) Security
Industry 4.0 and Healthcare 4.0 security
Cloud Systems security
Security measures for banking and credit operations
5G Security - Ensuring the security of networks as 5G technology becomes more widespread
Quantum Computing and Cryptography - Exploring the impact of quantum computing on current cryptographic algorithms and the development of quantum-resistant encryption methods
Feature selection by metaheuristics approaches for Intrusion detection and network security
Tuning and training Machine Learning models by metaheuristics for Intrusion detection and network security
Explainable AI (XAI) for Security - Enhancing the interpretability of machine learning models in security applications to understand and trust the decision-making process
Deep Learning for Malware Detection - Exploring advanced deep learning architectures for the detection and classification of malware, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs)
Behavioral Biometrics - User Behavior Analytics
Machine Learning for Threat Intelligence - Integrating machine learning techniques with threat intelligence feeds to proactively identify and mitigate emerging cyber threats
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