Related papers: Ransomware Detection using Process Memory

This study examines how Artificial Intelligence can aid in identifying and mitigating cyber threats in the U.S. across four key areas: intrusion detection, malware classification, phishing detection, and insider threat analysis. Each of…

Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data.…

File encrypting ransomware increasingly employs intermittent encryption techniques, encrypting only parts of files to evade classical detection methods. These strategies, exemplified by ransomware families like BlackCat, complicate file…

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services,…

Malware detection is challenging when faced with automatically generated and polymorphic malware, as well as with rootkits, which are exceptionally hard to detect. In an attempt to contribute towards addressing these challenges, we…

Ransomware attacks have become a pervasive and costly form of cybercrime, causing tens of millions of dollars in losses as organizations increasingly pay ransoms to mitigate operational disruptions and financial risks. While prior research…

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker.…

Modern ransomware often generate and manage cryptographic keys on the victim's machine, giving defenders an opportunity to capture exposed keys and recover encrypted data without paying the ransom. However, recent work has raised the…

As memory scales down to smaller technology nodes, new failure mechanisms emerge that threaten its correct operation. If such failure mechanisms are not anticipated and corrected, they can not only degrade system reliability and…

Ransomware impact hinges on how easily an intruder can move laterally and spread to the maximum number of assets. We present a graph-theoretic formulation that casts lateral movement as a path-closure problem over a probability semiring to…

Security has become ubiquitous in every domain today as newly emerging malware pose an ever-increasing perilous threat to systems. Consequently, honeypots are fast emerging as an indispensible forensic tool for the analysis of malicious…

The use of operating system API calls is a promising task in the detection of PE-type malware in the Windows operating system. This task is officially defined as running malware in an isolated sandbox environment, recording the API calls…

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…

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior…

Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail…

The problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to…

Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even…

The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection…

In this chapter, readers will explore how machine learning has been applied to build malware detection systems designed for the Windows operating system. This chapter starts by introducing the main components of a Machine Learning pipeline,…

Malware has become a widely used means in cyber attacks in recent decades because of various new obfuscation techniques used by malwares. In order to protect the systems, data and information, detection of malware is needed as early as…