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Evaluating the behaviour of stream learning algorithms for detecting invasion on wireless networks

Ensuring protection in computer networks is an increasingly difficult task because of the sheer number and variability of threats currently encountered. Intrusion detection systems (IDSs) is usually used to improve the security of information in computers networks, including any content that has value to a person or company. IDS monitor computers or networks to identify malicious activity or unauthorised access. An open issue is how much data is necessary for constructing models for predicting invasion in wireless networks, specially considering that are some scenarios that dataset is not promptly available. Our approach should consider constructing classifiers given a dataset and, as the dataset grows, new classifiers are constructed. Other strategy is explore stream learning algorithms that adapt models along the time. In addition to studying the applicability of stream learning algorithms. This work aims to investigate whether in terms of processing time, stream algorithms are more efficient than batch ones.