Atul Kumar

A. Karthick kumar, S. Rathnamala, T. Vijayashanthi et al.

Cyberattacks in an Internet of Things (IoT) environment can have significant impacts because of the interconnected nature of devices and systems. An attacker uses a network of compromised IoT devices in a botnet attack to carry out various harmful activities. Detecting botnet attacks poses several challenges because of the intricate and evolving nature of these threats. Botnet attacks erode trust in IoT devices and systems, undermining confidence in their security, reliability, and integrity. Deep learning techniques have significantly enhanced the detection of botnet attacks due to their ability to analyze and learn from complex patterns in data. This research proposed the stacking of Deep convolutional neural networks, Bi-Directional Long Short-Term Memory (Bi-LSTM), Bi-Directional Gated Recurrent Unit (Bi-GRU), and Recurrent Neural Networks (RNN) for botnet attacks detection. The UNSW-NB15 dataset is utilized for botnet attacks detection. According to experimental results, the proposed model accurately provides for the intricate patterns and features of botnet attacks, with a testing accuracy of 99.76%. The proposed model also identifies botnets with a high ROC-AUC curve value of 99.18%. A performance comparison of the proposed method with existing state-of-the-art models confirms its higher performance. The outcomes of this research could strengthen cyber security procedures and safeguard against new attacks.

Alex Mathai, Kranthi Sedamaki, Debeshee Das et al.

Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is largely attributed to the development of generalizable source code representations that effectively capture the syntactic and semantic characteristics of code. In recent years, pre-trained transformer-based models, inspired by natural language processing (NLP), have shown remarkable success in SE tasks. However, source code contains structural and semantic properties embedded within its grammar, which can be extracted from structured code-views like the Abstract Syntax Tree (AST), Data-Flow Graph (DFG), and Control-Flow Graph (CFG). These code-views can complement NLP techniques, further improving SE tasks. Unfortunately, there are no flexible frameworks to infuse arbitrary code-views into existing transformer-based models effectively. Therefore, in this work, we propose CodeSAM, a novel scalable framework to infuse multiple code-views into transformer-based models by creating self-attention masks. We use CodeSAM to fine-tune a small language model (SLM) like CodeBERT on the downstream SE tasks of semantic code search, code clone detection, and program classification. Experimental results show that by using this technique, we improve downstream performance when compared to SLMs like GraphCodeBERT and CodeBERT on all three tasks by utilizing individual code-views or a combination of code-views during fine-tuning. We believe that these results are indicative that techniques like CodeSAM can help create compact yet performant code SLMs that fit in resource constrained settings.