ملخص
Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.
اللغة الأصلية | English |
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عنوان منشور المضيف | Security and Privacy Management, Techniques, and Protocols |
ناشر | IGI Global |
الصفحات | 255-275 |
عدد الصفحات | 21 |
رقم المعيار الدولي للكتب (الإلكتروني) | 9781522555841 |
رقم المعيار الدولي للكتب (المطبوع) | 1522555838, 9781522555834 |
المعرِّفات الرقمية للأشياء | |
حالة النشر | Published - أبريل 6 2018 |
منشور خارجيًا | نعم |
ASJC Scopus subject areas
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