Tony Abou-Assaleh

N-gram-based detection of new malicious code

The current commercial anti-virus software detects a virus only after the virus has appeared and caused damage. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, we explore the idea of automatically detecting new malicious code using the collected dataset of the benign and malicious code. We obtained accuracy of 100% in the training data, and 98% in 3-fold cross-validation.

Relaxed Unification - Proposal

The traditional unification is strict in the sense that it requires a perfect agreement between the terms being unified. In practice, data is seldom error-free and can contain incorrect information. Traditional unification fails when the data is imperfect. We propose relaxed unification as a new theory that relaxes the constraints of the traditional unification. The goal of relaxed unification is to tolerate possible errors and inconsistencies in the data and facilitate reasoning under uncertainty.

Relaxed unification - Proposal

Classical unification is strict in the sense that it requires a perfect agreement between the terms being unified. In practice, data is seldom error-free and can contain incorrect information. Classical unification fails when the data is imperfect. Fuzzy unification partially addresses uncertainty but requires the creation of fuzzy databases. We propose relaxed unification as a new theory that relaxes the constraints of classical unification without requiring special preprocessing of data. The goal of relaxed unification is to tolerate possible errors and inconsistencies in the data and facilitate reasoning under uncertainty.

A probabilistic evaluation function for relaxed unification

Classical unification is strict in the sense that it requires a perfect agreement between the terms being unified. In practise, data are seldom error-free and can contain incorrect information. Classical unification fails when the data are imperfect. Relaxed unification is a new formalism that relaxes the rigid constraints of classical unification and enables reasoning under uncertainty and in the presence of inconsistent data. We propose a probabilistic evaluation function to evaluate the degree of mismatches in relaxed terms and illustrate its use with an example.

Towards the Theory of Relaxed Unification

Classical unification requires a perfect agreement between the terms being unified. In practice, data is seldom error-free and can contain inconsistent information. Classical unification fails when the data is imperfect. We propose the Theory of Relaxed Unification as a new theory that relaxes the constraints of classical unification without requiring special pre-processing of data. Relaxed unification tolerates possible errors and inconsistencies in the data and facilitate reasoning under uncertainty. The Theory of Relaxed Unification is more general and has higher efficacy than the classical Theory of Unification. We present the fundamental concepts and an algorithm for relaxed unification.