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The intrusion detection system (IDS) is an essential element of securitymonitoring in computer networks. An IDS distinguishes the malicious trafficfrom the benign one and determines the attack types targeting the assets of theorganization. The main challenge of an IDS is facing new (i.e., zero-day)attacks and separating them from benign traffic and existing types of attacks.Along with the power of the deep learning-based IDSes in auto-extractinghigh-level features and its independence from the time-consuming and costlysignature extraction process, the mentioned challenge still exists in this newgeneration of IDSes.  In this paper, we propose a framework for deep learning-based IDSesaddressing new attacks. This framework is the first approach using both deepnovelty-based classifiers besides the traditional clustering based on thespecialized layer of deep structures, in the security scope. Additionally, weintroduce DOC++ as a newer version of DOC as a deep novelty-based classifier.We also employ the Deep Intrusion Detection (DID) framework for thepreprocessing phase, which improves the ability of deep learning algorithms todetect content-based attacks. We compare four different algorithms (includingDOC, DOC++, OpenMax, and AutoSVM) as the novelty classifier of the frameworkand use both the CIC-IDS2017 and CSE-CIC-IDS2018 datasets for the evaluation.Our results show that DOC++ is the best implementation of the open setrecognition module. Besides, the completeness and homogeneity of the clusteringand post-training phase prove that this model is good enough for the supervisedlabeling and updating phase.

An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks