Departmental Research - School of Computer Science

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  • Enhancing Intrusion Detection Systems Using Intelligent False Alarm Filter: Selecting the Best Machine Learning Algorithm

    Researcher: D. Ahmad Faour

    Intrusion Detection Systems (IDSs) have been widely implemented in various network environments as an essential component for current Information and Communications Technologies (ICT). However, false alarms are a big problem for these systems, in which a large number of IDS alarms, especially false positives, could be generated during their detection. This issue greatly decreases the effectiveness and the efficiency of an IDS and heavily increases the burden on analyzing real alarms. To mitigate this problem, researchers identify and analyze the reasons for causing this problem, present a survey through reviewing some related work in the aspect of false alarm reduction, and introduce a promising solution of constructing an intelligent false alarm filter to refine false alarms for an IDS.

  • Automatic recognition of emotional states

    Researchers: Mr Haitham Maarouf, D. Alaa Ramadan and D. Ahmad Faour

    Although the advances of technology have significantly improved and the accidents in transportation have become less frequent, disastrous accidents continue to occur. Most feedback has shown that the behavior of the human operator was one of the most important reasons for the disaster accident. If an operator falls asleep or gets distracted (surprised), an accident will occur. Automatic recognition of emotional states is a challenging problem which requires the attention of researchers. In this study, the state of the human operator will be identified and analyzed based on the facial expressions and sound features which will be extracted online (Real-time).


Enhancing Intrusion Detection Systems Using Intelligent False Alarm Filter: Selecting the Best Machine Learning Algorithm

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