Related papers: Ransomware Detection Dynamics: Insights and Implic…

Crypto-ransomware attacks have been a growing threat over the last few years. The goal of every ransomware strain is encrypting user data, such that attackers can later demand users a ransom for unlocking their data. To maximise their…

In this work, we propose a two-phased approach for real-time detection and deterrence of ransomware. To achieve this, we leverage the capabilities of eBPF (Extended Berkeley Packet Filter) and artificial intelligence to develop both…

The increasing sophistication of cyber threats has necessitated the development of advanced detection mechanisms capable of identifying and mitigating ransomware attacks with high precision and efficiency. A novel framework, termed…

Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous proposed detection and defense methods, existing approaches face two fundamental limitations in large-scale industrial applications:…

Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze large amounts of network…

Entropy-based detection methodologies have gained significant attention due to their ability to analyze structural irregularities within executable files, particularly in the identification of malicious software employing advanced…

The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In…

Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the…

Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an effective solution in both the feature and problem spaces.…

In the face of increasing cyber threats, particularly ransomware attacks, there is a pressing need for advanced detection and analysis systems that adapt to evolving malware behaviours. Throughout the literature, using machine learning (ML)…

This work addresses JavaScript malware detection to enhance client-side web application security with a behavior-based system. The ability to detect malicious JavaScript execution sequences is a critical problem in modern web security as…

Ransomware can produce direct and controllable economic loss, which makes it one of the most prominent threats in cyber security. As per the latest statistics, more than half of malwares reported in Q1 of 2017 are ransomware and there is a…

Ransomware impact different organizations for years, it causes huge monetary, reputation loss and operation impact. Other than typical data encryption by ransomware, attackers can request ransom from the victim organizations via data…

Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has…

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…

Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms. In 2017, the worldwide economic impact of…

Nowadays security is major concern for any user connected to the internet. Various types of attacks are to be performed by intruders to obtaining user information as manin-middle attack, denial of service, malware attacks etc. Malware…

Since modern anti-virus software mainly depends on a signature-based static analysis, they are not suitable for coping with the rapid increase in malware variants. Moreover, even worse, many vulnerabilities of operating systems enable…

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence (AI) detectors…

Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies. Feature selection has been…