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The purpose of designing a fusion-based distributed intrusion detection system is to detect intrusion which was rather not detected by single IDS system.
Since it is not technically feasible to build a system with no vulnerabilities, Intrusion detection System, which can effectively detect intrusion accesses have attracted attention.
Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns.
Since the early days of research on intrusion detection, anomaly-based approaches have been proposed to detect intrusion attempts.
Gates protecting dangerous material were kept unlocked, it said, and none had alarms to detect intrusion.
Finally, a SVM is trained to detect intrusion.
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Current rules-based and anomaly-based intrusion detection systems detect intrusions either by matching patterns of network and users activities with pre-defined rules or they define the normal profile of system usages and then look for deviation.
Traditional intrusion detection systems still have limitations of adaptability because they are just able to detect intrusions previously set in system design.
This paper proposes a multiple-level hybrid classifier, a novel intrusion detection system, which combines the supervised tree classifiers and unsupervised Bayesian clustering to detect intrusions.
The system uses wireless sensors on doors, windows and other spots around the house to detect intrusions.
Sarasamma et al. [39] proposed a novel multilevel hierarchical Kohonen net to detect intrusions in networks.
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