相关论文: Applying Memory Forensics to Rootkit Detection

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have…

In the prevailing convergence of traditional infrastructure-based deployment (i.e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective…

Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however, often has uncertainty and poor explainability in…

The rapid evolution of encryption-based threats has rendered conventional detection mechanisms increasingly ineffective against sophisticated attack strategies. Monitoring entropy variations across hierarchical system levels offers an…

A serious threat today is malicious executables. It is designed to damage computer system and some of them spread over network without the knowledge of the owner using the system. Two approaches have been derived for it i.e. Signature Based…

Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with access only to the model's output, an attacker can employ…

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})…

Botnets are becoming increasingly prevalent as the primary enabling technology in a variety of malicious campaigns such as email spam, click fraud, distributed denial-of-service (DDoS) attacks, and cryptocurrency mining. Botnet technology…

The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a new method based on the concept of data mining. We demonstrate that the proposed method can more…

As the number and complexity of malware attacks continue to increase, there is an urgent need for effective malware detection systems. While deep learning models are effective at detecting malware, they are vulnerable to adversarial…

A digital forensic investigation aims to collect and analyse the evidence necessary to demonstrate a potential hypothesis of a digital crime. Despite the availability of several digital forensics tools, investigators still approach each…

Memory trace analysis is an important technology for architecture research, system software (i.e., OS, compiler) optimization, and application performance improvements. Hardware-snooping is an effective and efficient approach to monitor and…

As the digital landscape evolves, cybersecurity has become an indispensable focus of IT systems. Its ever-escalating challenges have amplified the importance of digital forensics, particularly in the analysis of heap dumps from main memory.…

In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum Support Vector…

The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically…

Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made…

Modern software systems have become increasingly complex, which makes them difficult to test and validate. Detecting software partial anomalies in complex systems at runtime can assist with handling unintended software behaviors, avoiding…

Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but…

Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The…

Non-Volatile Random Access Memory (NVRAM) is a novel type of hardware that combines the benefits of traditional persistent memory (persistency of data over hardware failures) and DRAM (fast random access). In this work, we describe an…