This paper intends to detect IoT malicious attacks through deep learning models and demonstrates a comprehensive evaluation of the deep learning and graph-based models regarding malicious network traffic detection. The models particularly are based on GraphSAGE, Bidirectional encoder representations from transformers (BERT), Temporal Convolutional Network (TCN) as well as Multi-Head Attention, together with Bidirectional Long Short-Term Memory (BI-LSTM) Multi-Head Attention and BI-LSTM and LSTM models. The chosen models demonstrated great performance to model temporal patterns and detect feature significance. The observed performance are mainly due to the fact that IoT system traffic patterns are both sequential and diverse, leaving a rich set of temporal patterns for the models to learn. Experimental results showed that BERT maintained the best performance. It achieved 99.94% accuracy rate alongside high precision and recall, F1-score and AUC-ROC score of 99.99% which demonstrates its capabilities through temporal dependency capture. The Multi-Head Attention offered promising results by providing good detection capabilities with interpretable results. On the other side, the Multi-Head Attention model required significant processing time like BI-LSTM variants. The GraphSAGE model achieved good accuracy while requiring the shortest training time but yielded the lowest accuracy, precision, and F1 score compared to the other models.

It is important to learn about attackers and their attacking strategies so that better and more effective defense systems can be built. During the reconnaissance stage, attackers intend to probe potential targets through various techniques including social engineering attacks. Phishing through email is a well-known, cheap, easy, and surprisingly effective technique for obtaining the needed information. This type of attack targets individuals and thus utilizes weaknesses that might exist in each person. Given the uniqueness of each individual’s personality, attackers make sure the right persuasion principle technique is employed for each targeted individual. This paper describes efforts to build machine-learning transformers, the emerging technique in language modeling, with the goal of building classifiers that take into account different types of persuasion principles. More specifically, the paper describes efforts to build machine-learning transformers based on BERT, RoBERTa, and DistilBERT and captures their classification results. The results show that these transformers are accurate enough to build a classification of phishing emails with respect to persuasion techniques. Furthermore, we report that the RoBERTa model is able to train faster than BERT and DistilBERT models.

Various decentralized applications have deployed millions of smart contracts (SCs) on the Blockchain networks. SCs enable programmable transactions involving the transfer of monetary assets between peers on a Blockchain network without any need to a central authority. However, similar to any software program, SCs may contain security issues. Software se-curity engineers and researchers have already uncovered several Ethereum BlockChain and SC vulnerabilities. Still, researchers continuously discover many more security flaws in deployed SCs. Indeed, the popularity of SCs attracts adversaries to launch new attack vectors. Thus, efficient vulnerability detection is necessary. This paper lists broad known vulnerabilities in SCs and classifies them based on the multi-class categories such as Suicidal, Prodigal, Greedy, and Normal SCs. The paper adopts artificial recurrent neural network architecture such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) used in deep learning to identify and then classify vulnerable Scs.