Related papers: Ransomware detection using stacked autoencoder for…

Sparse Autoencoders (SAEs) provide potentials for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transparent and controllable AI systems. We systematically analyze…

The rise of ransomware attacks has necessitated the development of effective strategies for identifying and mitigating these threats. This research investigates the utilization of a feature selection algorithm for distinguishing…

For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the…

Modern threat landscapes continue to evolve with increasing sophistication, challenging traditional detection methodologies and necessitating innovative solutions capable of addressing complex adversarial tactics. A novel framework was…

This paper assesses the performance of five machine learning classifiers: Decision Tree, Naive Bayes, LightGBM, Logistic Regression, and Random Forest using latent representations learned by a Variational Autoencoder from malware datasets.…

We present an approach to identifying which ransomware adversaries are most likely to target specific entities, thereby assisting these entities in formulating better protection strategies. Ransomware poses a formidable cybersecurity threat…

Ransomware attacks have increased significantly in recent years, causing great destruction and damage to critical systems and business operations. Attackers are unfailingly finding innovative ways to bypass detection mechanisms,…

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection…

Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine…

Ransomware has become one of the most serious cybersecurity threats causing major financial losses and operational disruptions worldwide.Traditional detection methods such as static analysis, heuristic scanning and behavioral analysis often…

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these…

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is…

Computing is still under a significant threat from ransomware, which necessitates prompt action to prevent it. Ransomware attacks can have a negative impact on how smart grids, particularly digital substations. In addition to examining a…

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a…

Deep learning is a kind of feature learning method with strong nonliear feature transformation and becomes more and more important in many fields of artificial intelligence. Deep autoencoder is one representative method of the deep learning…

The use of multi-threading and file prioritization methods has accelerated the speed at which ransomware encrypts files. To minimize file loss during the ransomware attack, detecting file modifications at the earliest execution stage is…

Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing…

Ransomware continues to evolve in complexity, making early and explainable detection a critical requirement for modern cybersecurity systems. This study presents a comparative analysis of three Transformer-based Large Language Models (LLMs)…

The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals…

This paper summarizes the research conducted for a malware detection project using the Canadian Institute for Cybersecurity's MalMemAnalysis-2022 dataset. The purpose of the project was to explore the effectiveness and efficiency of machine…