Detecting malicious URLs using binary classification through adaboost algorithm

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm

Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability

Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Performance Evaluation of Machine Learning Techniques for Identifying Forged and Phony Uniform Resource Locators (URLs)

Since the invention of Information and Communication Technology (ICT), there has been a great shift from the erstwhile traditional approach of handling information across the globe to the usage of this innovation. The application of this initiative cut across almost all areas of human endeavours. ICT is widely utilized in education and production sectors as well as in various financial institutions. It is of note that many people are using it genuinely to carry out their day to day activities while others are using it to perform nefarious activities at the detriment of other cyber users. According to several reports which are discussed in the introductory part of this work, millions of people have become victims of fake Uniform Resource Locators (URLs) sent to their mails by spammers. Financial institutions are not left out in the monumental loss recorded through this illicit act over the years. It is worth mentioning that, despite several approaches currently in place, none could confidently be confirmed to provide the best and reliable solution. According to several research findings reported in the literature, researchers have demonstrated how machine learning algorithms could be employed to verify and confirm compromised and fake URLs in the cyberspace. Inconsistencies have however been noticed in the researchers’ findings and also their corresponding results are not dependable based on the values obtained and conclusions drawn from them. Against this backdrop, the authors carried out a comparative analysis of three learning algorithms (Naïve Bayes, Decision Tree and Logistics Regression Model) for verification of compromised, suspicious and fake URLs and determine which is the best of all based on the metrics (F-Measure, Precision and Recall) used for evaluation. Based on the confusion metrics measurement, the result obtained shows that the Decision Tree (ID3) algorithm achieves the highest values for recall, precision and f-measure. It unarguably provides efficient and credible means of maximizing the detection of compromised and malicious URLs. Finally, for future work, authors are of the opinion that two or more supervised learning algorithms can be hybridized to form a single effective and more efficient algorithm for fake URLs verification.Keywords: Learning-algorithms, Forged-URL, Phoney-URL, performance-compariso

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai