Hamed HaddadPajouh

TL;DR: The potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware by using RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes) is explored.

TL;DR: A taxonomy that taps into the three-layer IoT architecture as a reference to identify security properties and requirements for each layer is built upon, classifying the potential IoT security threat and challenges by an architectural view.

TL;DR: This paper proposes a novel deep Recurrent Neural Network ( RNN) learning model that utilizes the RNN to analyze Windows applications’ operation codes (Opcodes) as a case study and applies traditional machine learning classifiers to show the applicability of deep learners ( LSTM ) versus traditional models in dealing with cryptocurrency malware.

TL;DR: This paper proposes a secure architecture for IoT edge layer infrastructure, called AI4SAFE-IoT, built upon AI-powered security modules at the edge layer for protecting IoT infrastructure, and evaluated the proposed architecture based on the IoT service management score.

TL;DR: The proposed multikernel support vector machine (SVM) approach outperforms DNNs and fuzzy-based IoT malware hunting techniques, in terms of accuracy, while significantly reducing the computational cost and the training time.