Abstract
Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.
Highlights
The World Economic Forum listed cyber-threat as one of the most important threats to the world economy in its 2019 Global Risk Report [1]
We have considered all of these problem and propose and efficient solution for detection of malicious traffic before it is transmitted through Internet of Things (IoT) device
4.1 Binary classification In binary classification, we applied Random Forest (RF), SVM and Artificial Neural Network (ANN) on optimal hyper-parameters found during parameter tuning phase
Summary
The World Economic Forum listed cyber-threat as one of the most important threats to the world economy in its 2019 Global Risk Report [1]. Companies are likely to suffer paralyzing attacks in the near term that will shut down daily operations, causing unimaginable revenue losses that exceed the breaches we have experienced to date. Such debilitating cyber-attacks would eventually lead to significant rise in investments for building adequate cyber security capabilities. IoT is a collection of linked, interconnected or interlinked digital devices, mechanical equipment, entities or items, creatures or individuals, equipped with unique identification and the capacity This involves the ability to direct information and commands over a typically wireless connection without involving interaction of humans either with computers or humans itself. Pace and complexity of today’s risky environment, we ought to be capable of responding to dangers posed by such attacks in a timely and effective manner
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