Topic: Novel Approaches for Software Security: Challenges and Solutions

Software security is the practice of protecting software systems and data through program reinforcement and formal logic analysis. With the widespread application of the software-defined paradigm in various environments, a large number of malicious behaviors threaten software security from different attack surfaces. The emergence of advanced technologies such as Artificial Intelligence (AI), Natural Language Processing (NLP), Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) networks presents both new opportunities and challenges in the domain of software security.

These cutting-edge technologies have significantly influenced both offensive and defensive aspects of software security. On the defensive side, AI and machine learning models can enhance vulnerability detection, identify malicious code, and improve Advanced Persistent Threat (APT) detection. Conversely, attackers are also leveraging these technologies to develop more sophisticated methods. AI-driven tools can automate the generation of adversarial samples to bypass security mechanisms, and advanced NLP models can help craft more convincing phishing attacks. Backdoor and data poisoning issues also create uncertainty for AI models. The dual-use nature of these technologies underscores the need for innovative and robust security measures.

Based on this demand, the field of information security needs more innovative methods for existing software security. Relevant topics include, but are not limited to:
● Support to the software development process (analysis, architecture, construction, maintenance and evolution);
● Complexity and systems thinking applied to the software security management and the software security engineering;
● The software security platform development and governance;
● Dynamic reconfiguration at different levels of software security (strategic, tactical, operational) to revisit coordination mechanisms and emergent behavior;
● Strategies, relationships, and applications of software security;
● Software security sustainability and diversity;
● Studies relative to software security within specific domains and areas (open source, mobile, embedded, autonomous systems, AI, digital transformations, the Internet of Things (IoT) technologies, telecommunication, etc.);
● Techniques and tools for software security verification/validation/testing;
● Techniques and tools for the vulnerability detection;
● Generative AI applied to the software security testing and adversarial sample generation for related models;
● Large Language Models (LLMs) for adaptive software attack surface management and software asset management;
● LLMs for automatically generating training samples for software security models;
● Malicious code identification using AI and machine learning techniques;
● Studies relative to the APT detection through LLMs;
● Automated security analysis and threat modeling with AI-driven tools;
● Integration of AI in continuous security monitoring and incident response;
● Defensive strategies against AI-driven cyber attacks;
● Case studies and real-world applications of AI in enhancing software security.

Software security, artificial intelligence (AI), large language models (LLMs), advanced persistent threat (APT), defensive strategies

31 Jan 2025