相关论文: Automated Ransomware Behavior Analysis: Pattern Ex…

This study provides a comprehensive synthesis of Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), in ransomware defense. Using a "review of reviews" methodology based on PRISMA, this paper gathers…

Ransomware is a growing threat that typically operates by either encrypting a victim's files or locking a victim's computer until the victim pays a ransom. However, it is still challenging to detect such malware timely with existing…

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is…

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…

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…

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…

We present a novel approach to identify ransomware campaigns derived from attack timelines representations within victim networks. Malicious activity profiles developed from multiple alert sources support the construction of alert graphs.…

Most of today's security solutions, such as security information and event management (SIEM) and signature based IDS, require the operator to evaluate potential attack vectors and update detection signatures and rules in a timely manner.…

Memory forensics is an effective methodology for analyzing living-off-the-land malware, including threats that employ evasion, obfuscation, anti-analysis, and steganographic techniques. By capturing volatile system state, memory analysis…

"Alert fatigue" is one of the biggest challenges faced by the Security Operations Center (SOC) today, with analysts spending more than half of their time reviewing false alerts. Endpoint detection products raise alerts by pattern matching…

Encryption-based attacks have introduced significant challenges for detection mechanisms that rely on predefined signatures, heuristic indicators, or static rule-based classifications. Probabilistic Latent Encryption Mapping presents an…

Intrusion detection systems (IDS) reinforce cyber defense by autonomously monitoring various data sources for traces of attacks. However, IDSs are also infamous for frequently raising false positives and alerts that are difficult to…

In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task. However, the performance discrepancy caused by different implementation details may conceal the…

This paper proposes a methodology for host-based anomaly detection using a semi-supervised algorithm namely one-class classifier combined with a PCA-based feature extraction technique called Eigentraces on system call trace data. The…

Advanced attack campaigns span across multiple stages and stay stealthy for long time periods. There is a growing trend of attackers using off-the-shelf tools and pre-installed system applications (such as \emph{powershell} and \emph{wmic})…

As computing systems become increasingly advanced and as users increasingly engage themselves in technology, security has never been a greater concern. In malware detection, static analysis, the method of analyzing potentially malicious…

The number of malicious software (malware) is growing out of control. Syntactic signature based detection cannot cope with such growth and manual construction of malware signature databases needs to be replaced by computer learning based…

In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. metamorphic malware. In this paper, we study the frequency of opcode occurrence to detect unknown…

Adversarial robustness of deep models is pivotal in ensuring safe deployment in real world settings, but most modern defenses have narrow scope and expensive costs. In this paper, we propose a self-supervised method to detect adversarial…

To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable…