Related papers: Malware static analysis and DDoS capabilities dete…
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure, evolving rapidly in sophistication and eluding traditional detection mechanisms. This evolution demands a comprehensive…
Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming…
Distributed Denial of Service (DDOS) attack is one of the most common network attacks. DDoS attacks are becoming more and more diverse, which makes it difficult for some DDoS attack detection methods based on single network flow…
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an…
For the traditional denial-of-service attack detection methods have complex algorithms and high computational overhead, which are difficult to meet the demand of online detection; and the experimental environment is mostly a simulation…
One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by…
DroidDissector is an extraction tool for both static and dynamic features. The aim is to provide Android malware researchers and analysts with an integrated tool that can extract all of the most widely used features in Android malware…
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task. To make matters worse, there is an intrinsic bias in these…
Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However, many potential features can be used, and it is time…
While much of the current research in deep learning-based vulnerability detection relies on disassembled binaries, this paper explores the feasibility of extracting features directly from raw x86-64 machine code. Although assembly language…
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detection rates…
In this paper we present an elaborated graph-based algorithmic technique for efficient malware detection. More precisely, we utilize the system-call dependency graphs (or, for short ScD graphs), obtained by capturing taint analysis traces…
There are many occasions in which the security community is interested to discover the authorship of malware binaries, either for digital forensics analysis of malware corpora or for thwarting live threats of malware invasion. Such a…
As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the…
The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw bytes extracted from executables. These architectures reach…
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these…
Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the identity and behavior of PDF malware. State-of-the-art…
This paper presents an experimental design and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that…
Software debloating techniques are applied to craft a specialized version of the program based on the user's requirements and remove irrelevant code accordingly. The debloated programs presumably maintain better performance and reduce the…
Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an…