2008 Proceeding- Program Chairs:
- Dirk Balfanz,
- Jessica Staddon
It is our great pleasure to welcome you to the 1st ACM Workshop on AISec -- AISec '08. The mission of this new workshop is to stimulate increased collaboration between the Security and AI communities. It is our strong belief that such collaboration is the best route towards fully realizing the security and privacy benefits of today's ubiquitous information.
The call for papers attracted 20 submissions from Asia, Canada, Europe and the United States. The program committee accepted 7 research papers and 2 position papers covering a variety of topics, including usable access control and authentication, malware and network attack defense and reputation systems. In addition, the program includes two exciting invited talks. The first is by Dr. Chris Clifton of Purdue University; a prominent leader in both the privacy and data mining communities. The second is by Dr. Carl Landwehr, IARPA and University of Maryland. Dr. Landwehr is very well-known for his information assurance research and currently is the Program leader for the National Intelligence Community Information Assurance Research at IARPA, a program with many challenging problems intersecting both Security and AI.
We give our heartfelt thanks to the program committee and external reviewers. It is quite challenging crafting a program for a cross-disciplinary conference. The program committee made significant strides in defining this largely new field of research and in soliciting relevant and novel research contributions, and all the reviewers worked very hard to give useful and insightful feedback to the authors.
Proceeding Downloads
POSH: a generalized captcha with security applications
A puzzle only solvable by humans, or POSH, is a prompt or question with three important properties: it can be generated by a computer, it can be answered consistently by a human, and a human answer cannot be efficiently predicted by a computer. In fact, ...
User-controllable learning of security and privacy policies
Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have successfully been used to refine models of user preferences, such as in ...
Open problems in the security of learning
- Marco Barreno,
- Peter L. Bartlett,
- Fuching Jack Chi,
- Anthony D. Joseph,
- Blaine Nelson,
- Benjamin I.P. Rubinstein,
- Udam Saini,
- J. D. Tygar
Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible ...
Cognitive security for personal devices
Humans should be able to think of computers as extensions of their body, as craftsmen do with their tools. Current security models, however, are too unlike those used in human minds-for example, computers authenticate users by challenging them to repeat ...
Opportunities for private and secure machine learning
While the interplay of Artificial Intelligence and Security covers a wide variety of topics, the 2008 AISec program largely focuses on use of artificial intelligence techniques to aid with traditional security concerns: intrusion detection, security ...
Robust content-driven reputation
In content-driven reputation systems for collaborative content, users gain or lose reputation according to how their contributions fare: authors of long-lived contributions gain reputation, while authors of reverted contributions lose reputation. ...
Adaptive distributed mechanism against flooding network attacks based on machine learning
Adaptive techniques based on machine learning and data mining are gaining relevance in self-management and self-defense for networks and distributed systems. In this paper, we focus on early detection and stopping of distributed flooding attacks and ...
Cyber security and artificial intelligence: from fixing the plumbing to smart water
Computer security and artificial intelligence in their early days didn't seem to have much to say to each other. AI researchers were interested in making computers do things that only humans had been able to do, while security researchers aimed to fix ...
Malware detection using adaptive data compression
A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of ...
A data mining approach for analysis of worm activity through automatic signature generation
This paper proposes a novel framework to automatically discover and analyze traffic generated by computer worms and other anomalous behaviors that interact with a non-solicited traffic monitoring system. Network packets are analyzed by an Intrusion ...
Automatic feature selection for anomaly detection
A frequent problem in anomaly detection is to decide among different feature sets to be used. For example, various features are known in network intrusion detection based on packet headers, content byte streams or application level protocol parsing. A ...
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