This paper addresses the problem of detecting masquerading, a security attack in which an intruder assumes the identity of a legitimate user. Many approaches based on Hidden Markov Models and various forms of Finite State Automata have been proposed to solve this problem. The novelty of our approach results from the application of techniques used in bioinformatics for a pair-wise sequence alignment to compare the monitored session with past user behavior. Our algorithm uses a semi-global alignment and a unique scoring system to measure similarity between a sequence of commands produced by a potential intruder and the user signature, which is a sequence of commands collected from a legitimate user. We tested this algorithm on the standard intrusion data collection set. As discussed in the paper, the results of the test showed that the described algorithm yields a promising combination of intrusion detection rate and false positive rate, when compared to published intrusion detection algorithms.
An implicit association test is a human psychological test used to measure subconscious associations. While widely recognized by psychologists as an effective tool in measuring attitudes and biases, the validity of the results can be compromised if a subject does not follow the instructions or attempts to manipulate the outcome. Compared to previous work, we collect training data using a more generalized methodology. We train a variety of different classifiers to identify a participant's first attempt versus a second possibly compromised attempt. To compromise the second attempt, participants are shown their score and are instructed to change it using one of five randomly selected deception methods. Compared to previous work, our methodology demonstrates a more robust and practical framework for accurately identifying a wide variety of deception techniques applicable to the IAT.
We present an experimental study of a learning algorithm for the longest common subsequence problem, LCS . Given an arbitrary input domain, the algorithm learns an LCS -procedure tailored to that domain. The learning is done with the help of an oracle, which can be any LCS -algorithm. After solving a limited number of training inputs using an oracle, the learning algorithm outputs a new LCS -procedure.Our experiments demonstrate that, by allowing a slight loss of optimality, learning yields a procedure which is significantly faster than the oracle. The oracle used for the experiments is the np -procedure by Wu et al. , a modification of Myers' classical LCS -algorithm. We show how to scale up the results of learning on small inputs to inputs of arbitrary lengths. For the domain of two random 2-symbol inputs of length n , learning yields a program with 0.999 expected accuracy, which runs in O ( n 1.41 )-time, in contrast with O ( n 2 /log n ) running time of the fastest theoretical algorithm that produces optimal solutions. For the domain of random 2-symbol inputs of length 100,000, the program runs 10.5 times faster than the np -procedure, producing 0.999- accurate outputs. The scaled version of the evolved algorithm applied to random inputs of length 1 million runs approximately 30 times faster than the np -procedure while constructing 0.999- accurate solutions. We apply the evolved algorithm to DNA sequences of various lengths by training on random 4-symbol sequences of up to length 10,000. The evolved algorithm, scaled up to the lengths of up to 1.8 million, produces solutions with the 0.998-accuracy in a fraction of the time used by the np .
Users of complex applications need advice, assistance, and feedback while they work. We are experimenting with "adjunct" user agents that are aware of the history of interaction surrounding the accomplishment of a task. This paper describes an architectural framework for constructing these agents. Using this framework, we have implemented a critiquing system that can give task-oriented critiques to trainees while they use operating system tools and software applications. Our approach is generic, widely applicable, and works directly with off-the-shelf software packages.
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