Abstract:
IoT devices have fundamental security flaws that leave them open to a variety of security threats and
attacks, including attacks from botnets. Therefore, creators of botnets continue to take advantage of the
security vulnerabilities inherent in IoT devices to control many host devices on networks to launch cyber attacks on their target systems. The ongoing development of techniques to evade and obfuscate existing
detection and security procedures makes it difficult to discover IoT bot vulnerabilities. This study proposes
a deep learning method to detect two famous botnet-based attacks: the mirai and Bashlite bots on IoT
devices. Our approach implements a 1-dimensional convolutional neural network model (1D-CNN) that is
trained on 115 features of real traffic data collected from nine commercial internet of things devices infected
by the two mentioned IoT bots to recognize 10 classes of attacks and 1 class of benign traffic. The trained
multiclass classification malware detection model was evaluated on 847513 samples, containing 7062606
instances from the N-BaIoT dataset. We further trained two existing models: Plain Feed forward neural
network and a popular supervised machine learning classifier, (Logistic Regression) models on the same
preprocessed datasets, and compared the classification performances against our proposed model. The
experimental results show that our 1D neuron-based model produced a higher prediction in terms of
overall classification accuracy over the two models. It was further noted that our model's performance was
superior to those of earlier studies on deep learning-based IoT botnet detection.
