Conference Overview
The International Conference on Secure and Trustworthy Artificial Intelligence Systems (ICSTAIS 2026) invites original research contributions that advance the state-of-the-art in secure, trustworthy, and resilient AI systems. As artificial intelligence becomes increasingly pervasive in critical applications, ensuring the security, reliability, and trustworthiness of these systems has emerged as a paramount concern for researchers, practitioners, and policymakers worldwide.
ICSTAIS 2026 provides a premier international platform for researchers, academicians, industry professionals, and graduate students to present cutting-edge research, share innovative solutions, and foster collaborative discussions on the convergence of artificial intelligence and cybersecurity. The conference emphasizes both theoretical foundations and practical applications, encouraging submissions that bridge the gap between research and real-world implementation.
Important Dates
| Paper Submission Deadline |
May 10, 2026 |
| Notification of Acceptance |
May 25, 2026 |
| Camera-Ready Submission |
May 31, 2026 |
| Early Bird Registration |
May 30, 2026 |
| Conference Dates |
June 12-13, 2026 |
All deadlines are 11:59 PM IST
Scope and Topics of Interest
We solicit high-quality original research papers, survey papers, and position papers addressing various aspects of secure and trustworthy AI systems.
Topics of interest include, but are not limited to:
- AI model verification, validation, and formal methods
- Secure AI lifecycle management and DevSecOps for ML
- Ethical frameworks, AI governance, and regulatory compliance
- Trust metrics and assessment methodologies for AI systems
- Certification and standardization of AI security practices
- Machine learning and deep learning for intrusion detection and prevention
- AI-driven threat intelligence and automated threat hunting
- Intelligent security incident response and digital forensics
- Anomaly detection in network traffic and system behavior
- AI-enhanced malware detection and classification
- Adversarial machine learning and defense mechanisms
- Model theft, inversion, and membership inference attacks
- Data poisoning and backdoor attacks on AI systems
- Secure deployment of AI in cloud, edge, and IoT environments
- Privacy-preserving machine learning architectures
- Advanced deepfake generation and detection techniques
- Forensic analysis of manipulated multimedia content
- AI-powered disinformation detection and mitigation
- Social engineering threats amplified by synthetic media
- Legal and ethical implications of deepfake technology
- Adversarial example generation and defense strategies
- Certified defenses and provable robustness guarantees
- Robustness testing and red-teaming methodologies
- Secure transfer learning and domain adaptation
- Federated learning security and privacy
- Differential privacy in machine learning systems
- Secure multiparty computation for collaborative AI
- Homomorphic encryption for privacy-preserving inference
- Federated learning with privacy guarantees
- Synthetic data generation for privacy protection
- Explainable AI for security-critical applications
- Interpretability techniques for black-box AI models
- Bias detection, fairness, and algorithmic accountability
- Human-AI interaction in security contexts
- Trust calibration and uncertainty quantification
- AI applications in securing smart grids, healthcare, transport, and finance
- Cyber-physical security and AI-based industrial control protection
- AI resilience in emergency and disaster scenarios
- Quantum-resistant cryptography for AI communications
- Quantum computing threats to current AI security
- Quantum machine learning and its security implications
- Quantum-enhanced cybersecurity protocols
- Post-quantum cryptographic implementations in AI systems
- AI for cyber risk assessment and management
- Bio-inspired and neuromorphic security models
- Sustainable and green AI security frameworks
- AI ethics and responsible disclosure practices
- Future challenges in AI security and privacy
Submission Guidelines
Paper Categories:
- Full Papers: Original research contributions (max 5 pages)
- Survey Papers:Comprehensive reviews of specific research areas (max 5 pages)
Formatting Requirements:
- Papers must be written in English and formatted according to Nova Science Formatting
- Submissions must be original and not published or under review elsewhere
- Papers should be submitted in PDF format through the conference submission system
- All submissions will undergo rigorous peer review by at least three experts
Review Process:
- Double-blind review: Author identities will be concealed from reviewers
- Rigorous evaluation: Papers will be assessed based on novelty, technical quality, significance, and clarity
- Feedback provided: Detailed reviews will be provided to all authors
- Multiple review rounds: Selected papers may undergo revision cycles
Publication and Indexing
Accepted papers will be published in Nova Science (Scopus indexed) journal, ensuring wide dissemination and high visibility in the academic community. The proceedings will be indexed in major databases including:
- Scopus
- Web of Science
- DBLP
- Google Scholar
Selected high-quality papers may be invited for extended versions in special issues of relevant journals.
Special Features
Best Paper Awards
- Best Paper Award for outstanding research contribution
- Best Student Paper Award for exceptional work by graduate students
- Best Industry Paper Award for practical applications and industrial relevance
Workshops and Tutorials
- Pre-conference workshops on specialized topics
- Hands-on tutorials by industry experts
- Poster sessions for student researchers
Industry Engagement
- Industry exhibition and demonstration sessions
- Networking opportunities with leading technology companies
- Career development sessions for young researchers
Submission Instructions
- Prepare your manuscript according to Nova Science formatting guidelines
- Submit your manuscript through the Microsoft CMT conference portal only, and not through any emaill
- Ensure anonymity by removing author information from the submission
- Include supplementary materials if applicable (datasets, code, etc.)
- Confirm compliance with ethical guidelines and plagiarism policies
CMT ACKNOWLEDGMENT: The following acknowledgment must remain VISIBLE on your conference website, as well as the conference proceedings if applicable. Do not use any modifiers; keep it as plain text:
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
