Intrusion detection with autoencoder based deep learning machine
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In changing and constantly evolving information age, together with the developments in computer and internet technology, the production, digitization, storage and sharing of information has become much easier than in the past. The sharing of information via computer networks and the Internet has made information security a vital issue for people, institutions and organizations with critical data. Various information security policies have been established in order to protect the critical preserve data and prevent unauthorized access to this data. Intrusion detection systems which is one of the indispensable elements of information security policies, constantly monitor the network and the system to detect possible unauthorized access and infiltrations. So far, many machine learning methods such as artificial neural networks, support vector machines, decision trees have been used in intrusion detection systems. In this study, differently from other studies, autoencoder based deep learning machines are proposed for intrusion detection. KDDcup99 data set containing 22 attack types has been used in the study and a performance with 99.42% of succes rate has been achieved.