Abstract
This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.
Highlights
In recent years cyberattacks, especially those targeting systems that keep or process sensitive information, are becoming more sophisticated
We have redesigned the proposed system, we have presented the integration of RDTIDs into a three-tier fog computing architecture for IoT networks and have evaluated its performance against a Bot-IoT dataset
We use the CICIDS2017 dataset [24], which we split in training and testing datasets, in order to evaluate their performance in detecting network intrusions and we compare it with other machine learning methods proposed by previous researchers, including, WISARD [28], ForestPA [29], J48 Consolidated [30], LIBSVM [31], FURIA [32], RandomForest, REPTree, MLP, NaiveBayes, JRip and J48
Summary
Especially those targeting systems that keep or process sensitive information, are becoming more sophisticated. Our model must provide a low false alarm rate and a high detection rate both for frequent and infrequent attacks while on the same require low computing in order to perform classification. The latter characteristic is very important when IDSs are deployed in industrial control systems that operate critical infrastructures where correct and fast notification about cyber attacks is crucial [7].
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