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Cited by 112 publications
(42 citation statements)
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“…is said to belong to one side of the partition F with degree µ F (x) and to the other side with degree (1 − µ F (x)). Since both fuzzy rules [33] and decision tree [43], [44] yield good performance in malware analysis, we hybridize the idea behind these two methods by allowing fuzzy partitions of both types: (i) less than partition x < c, which is the fuzzy version of the ordinary partition for a continuous attribute:…”
Section: A Feature Selectionmentioning
confidence: 99%
“…is said to belong to one side of the partition F with degree µ F (x) and to the other side with degree (1 − µ F (x)). Since both fuzzy rules [33] and decision tree [43], [44] yield good performance in malware analysis, we hybridize the idea behind these two methods by allowing fuzzy partitions of both types: (i) less than partition x < c, which is the fuzzy version of the ordinary partition for a continuous attribute:…”
Section: A Feature Selectionmentioning
confidence: 99%
“…In [49], the authors applied a decision tree to build IDS to detect APT attacks. It can detect intrusion from the beginning and quickly react to APT to minimize damage.…”
Section: Countering An Advanced Persistent Threatmentioning
confidence: 99%
“…While this may not always correspond to an attack, alertbased responses address inherent problems in SCADA operations, which are otherwise not possible to capture using traditional IT-based IDS. Based on the requirements of the strict availability, the IDS can respond immediately to unusual situations [56, 63, 69, 91-93, 95, 132, 133, 135, 138, 141], or provide a delayed notification summarizing similar alarms [52,74,79,88,100,114,122,123]. Information-Centric: If we examine the information used for the detection, then IDS systems can be further categorized into Host-based Intrusion Detection (HID) and Networkbased Intrusion Detection (NID).…”
Section: Taxonomy Of Scada-based Idssmentioning
confidence: 99%