Related papers: Clustering and Analysis of Vulnerabilities Present…

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial…

Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as…

Cluster analysis often serves as the initial step in the process of data classification. In this paper, the problem of clustering different length input data is considered. The edit distance as the minimum number of elementary edit…

Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent…

Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. With recent advancements in deep learning -- deep clustering…

This paper attempts to strengthen the pursued research on social engineering (SE) threat identification, and control, by means of the author's illustrated classification, which includes attack types, determining the degree of possible harm…

Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of…

Large Language Models (LLMs), characterized by being trained on broad amounts of data in a self-supervised manner, have shown impressive performance across a wide range of tasks. Indeed, their generative abilities have aroused interest on…

Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic inputs within an end-to-end learning framework. Despite their…

Vulnerability discovery and exploits detection are two wide areas of study in software engineering. This preliminary work tries to combine existing methods with machine learning techniques to define a metric classification of vulnerable…

Machine learning and data mining algorithms play important roles in designing intrusion detection systems. Based on their approaches toward the detection of attacks in a network, intrusion detection systems can be broadly categorized into…

We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.…

Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control…

Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent…

Identifying and categorizing specific robot tasks, behaviors, and resources is an essential precursor to reproducing and evaluating robotics experiments across laboratories and platforms. Without some means of capturing how one environment,…

While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data…

Algorithmic complexity vulnerabilities are a class of security problems that enables attackers to trigger the worst-case complexity of certain algorithms. Such vulnerabilities can be leveraged to deploy low-volume, asymmetric, CPU-based…

Large Language Models (LLMs) have emerged as powerful tools for automating programming tasks, including security-related ones. However, they can also introduce vulnerabilities during code generation, fail to detect existing vulnerabilities,…

Robotics landscape is experiencing big changes. Robots are spreading and will soon be everywhere. Systems traditionally employed in industry are being replaced by collaborative robots, while more and more professional and consumer robots…

Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critical nodes is a central theme in contemporary research,…