RAID 2022 : Recent Advances in Intrusion Detection
Call For Papers
Since its inception in 1997, the International Symposium on Research in Attacks, Intrusions and Defenses (RAID) has established itself as a venue where leading researchers and practitioners from academia, industry, and the government are given the opportunity to present novel research in a unique venue to an engaged and lively community.
The conference is known for the quality and thoroughness of the reviews of the papers submitted, the desire to build a bridge between research carried out in different communities, and the emphasis given on the need for sound experimental methods and measurement to improve the state of the art in cybersecurity.
This year we are soliciting research papers on topics covering all well-motivated computer security problems. We care about techniques that identify new real-world threats, techniques to prevent them, to detect them, to mitigate them or to assess their prevalence and their consequences. Measurement papers are encouraged, as well as papers offering public access to new tools or datasets, or experience papers that clearly articulate important lessons learned.
Specific topics of interest to RAID include, but are not limited to:
- Computer, network, and cloud computing security
- Malware and unwanted software
- Program analysis and reverse engineering
- Mobile Security
- Web security and privacy
- Vulnerability analysis techniques
- Usable security and privacy
- Intrusion detection and prevention
- Hardware security
- Cyber physical systems security and threats against critical infrastructures
- IoT security
- Statistical and adversarial learning for computer security
- Cyber crime and underground economies
- Denial-of-Service attacks and defenses
- Security measurement studies
- Digital forensics
Papers will be judged on novelty, significance, correctness, and clarity. We expect all papers to provide enough detail to enable reproducibility of their experimental results. We encourage authors to make both the tools and data publicly available.