The rapid rise in Android applications has fueled a significant surge in the creation and distribution of malicious apps by cybercriminals. Numerous tools and applications are utilized to detect Android malware apps. However, they cannot effectively...
Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Contex...
The landscape of cybercrime has undergone significant transformations over the past decade. Present-day threats include AI-generated attacks, deep fakes, 5G network vulnerabilities, cryptojacking, and supply chain attacks, among others. To remain res...
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employ...
The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before...
Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As...
The evolution of malware poses significant challenges to the security of cyberspace. Machine learning-based approaches have demonstrated significant potential in the field of malware detection. However, such methods are partially limited, such as hav...
Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in...
Detecting previously unseen malware remains a critical challenge for modern cybersecurity systems due to the rapid evolution of malicious software and the limitations of traditional supervised detection models. This paper proposes a Dual-Channel Mamb...
With the progress and evolution of the IoT, which has resulted in a rise in both the number of devices and their applications, there is a growing number of malware attacks with higher complexity. Countering the spread of malware in IoT networks is a...
The surge in malware threats propelled by the rapid evolution of the internet and smart device technology necessitates effective automatic malware classification for robust system security. While existing research has primarily relied on some feature...
The rapid evolution of malware families poses significant challenges for cybersecurity systems, particularly when newly emerging threats lack sufficient labeled data. Although image-based deep learning approaches have achieved strong performance unde...
Due to the rapid development and widespread presence of malware, deep-learning-based malware detection methods have become a pivotal approach used by researchers to protect private data. Behavior-based malware detection is effective, but changes in t...
Technologically speaking, humanity lives in an age of evolution, prosperity, and great development, as a new generation of the Internet has emerged; it is the Internet of Things (IoT) which controls all aspects of lives, from the different devices of...
The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily. While malware classification and d...
Mobile malware evolution induces concept drift, degrading the performance of static supervised detectors. Concept Drift Adaptation with Active Learning (CDA-AL) mitigates this problem by querying informative samples for model updating, but existing m...
Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-se...
Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dyna...
The rapid evolution of malware continues to pose severe challenges to cybersecurity, highlighting the need for accurate and efficient detection systems. Traditional signature- and heuristic-based methods are increasingly inadequate against sophistica...
We propose the Clustered Link-Prediction SEIRS model with Temporal Node Activation (CLP-SEIRS-T), a novel epidemiological framework that integrates community structure, link prediction, and temporal activation schedules to simulate malware propagatio...
This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of rever...
With the rapid evolution of cyberattack techniques, identifying dynamic behavioral intents from Application Programming Interface call sequences has become a fundamental modality for ensuring reliable malware detection and information security. Howev...
In this paper, we explore how incident handling procedures are currently being implemented to efficiently mitigate malicious software. Additionally, it aims to provide a contextual understanding of diverse malcodes and their operational processes. Th...
Life without computers is unimaginable. However, computers remain vulnerable to cybercrimes, a USD 6 trillion industry that the world has come to accept as a “necessary evil”. Third-party permissions resulting in an attack surface (AS) an...
The Aho-Corasick (AC) algorithm remains one of the most influential developments in deterministic multi-pattern matching due to its ability to recognize multiple strings in linear time within a single data stream. Originally conceived for bibliograph...
The rapid evolution of Android malware and increasingly sophisticated obfuscation techniques challenge traditional detection systems. This study presents a rigorous, unified comparative evaluation of three methodological paradigms-classical machine l...
Covert channel communications are of vital importance for the ill-motivated purposes of cyber-crooks. Through these channels, they are capable of communicating in a stealthy way, unnoticed by the defenders and bypassing the security mechanisms of pro...
With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible)...
This article analyzes how artificial intelligence (AI) is influencing the evolution of cybercrime in Ecuador. The use of AI tools to create new threats, such as intelligent malware, automated phishing, and financial fraud, is on the rise. The main pr...
The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individu...
Due to widespread adoption of mobile communications devices and increasingly high throughput capacity of cellular networks, Third-Generation (3G) and Long Term Evolution (LTE) are becoming ever more popular. Millions of smart phones with 3G capabilit...
Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including sur...
The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical chal...
Ransomware is a type of malware that leverages encryption to execute its attacks. Its continuous evolution underscores its dynamic and ever-changing nature. The evolving variants use varying timelines to launch attacks and associate them with varying...
The convergence of ubiquitous connectivity, large-scale data generation, and rapid advancements in machine learning is transforming the field of cybersecurity. The widespread adoption of interconnected systems including Internet of Things devices, mo...
The rapid evolution of network infrastructure through the softwarization of network elements has led to an exponential increase in the attack surface, thereby increasing the complexity of threat protection. In light of this pressing concern, European...
In this paper, the authors propose a complete multiclass traffic classification model with an unknown-traffic screening method based on anomaly detection using Isolation Forest and a topological awareness method for context-based risk analysis, a dyn...