Related papers: Detecting Malicious Code by Exploiting Dependencie…

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform.…

With the growing number of malware and cyber attacks, there is a need for "orthogonal" cyber defense approaches, which are complementary to existing methods by detecting unique malware samples that are not predicted by other methods. In…

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an…

In order to find a specific type of malware which is related to GSM frequency, we propose an algorithm according to the most essential characteristics of this malware. At first, detect whether or not there exists a specific thread in the…

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's…

Graphical modeling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this translates into detecting the support of the precision…

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely…

Converting malware into images followed by vision-based deep learning algorithms has shown superior threat detection efficacy compared with classical machine learning algorithms. When malware are visualized as images, visual-based…

Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification…

Graph-based detection methods leveraging Function Call Graphs (FCGs) have shown promise for Android malware detection (AMD) due to their semantic insights. However, the deployment of malware detectors in dynamic and hostile environments…

In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware detection, family classification, and malware attribute…

Software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation of code in vulnerability detection tasks have achieved…

The popularity of dynamic malware analysis has grown significantly, as it enables analysts to observe the behavior of executing samples, thereby enhancing malware detection and classification decisions. With the continuous increase in new…

A detection system, modeled in a graph, is composed of "detectors" positioned at a subset of vertices in order to uniquely locate an ``intruder" at any vertex. \emph{Identifying codes} use detectors that can sense the presence or absence of…

Behavior of a malware varies with respect to malware types. Therefore,knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve…

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android…

Due to continuous increase in the number of malware (according to AV-Test institute total ~8 x 10^8 malware are already known, and every day they register ~2.5 x 10^4 malware) and files in the computational devices, it is very important to…

While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains…

The spreading of dangerous malware or faults in inter-dependent networks of electronics devices has raised deep concern, because from the ICT networks infections may propagate to other Critical Infrastructures producing the well-known…

Abstract-Email cyber-attacks based on malicious documents have become the popular techniques in today's sophisticated attacks. In the past, persistent efforts have been made to detect such attacks. But there are still some common defects in…