Jannatul Ferdous

The increasing sophistication of malware threats has led to growing concerns in the anti-malware community, as malware poses a significant danger to online users despite the availability of numerous defense solutions. This study aims to comprehensively review malware evolution and current attack trends to identify effective defense mechanisms. It reviews the most recent journal articles, conference proceedings, reports, and online resources published during the last five years. We extensively review the malware landscape from 1970 to the present and analyze malware types, operational mechanisms, attack vectors, and vulnerabilities. Furthermore, we explore different defensive strategies developed in response to these evolving threats. Our findings highlight the increasing sophistication of malware attack trends, including a surge in cryptojacking, attacks on mobile devices, Internet of Things devices, ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based attacks, exploitation of remote employees, and attack trends on edge networks. Defense strategies have also evolved in parallel, emphasizing multilayered security measures to counter these dynamic threats. This study highlights the critical need for robust, multilayered security measures to combat dynamic malware. Despite these advancements, some open challenges and significant research gaps remain, which require further innovation. This review serves as a valuable guide for cybersecurity professionals by identifying the key trends, challenges, limitations, and future cybersecurity research opportunities.

Ransomware attacks are becoming increasingly sophisticated, thereby rendering conventional detection methods less effective. Recognizing this challenge, this study reviews advanced detection mechanisms and explores the potential of artificial intelligence (AI) techniques to improve detection capabilities. This study reviews the recent literature, including journal articles, conference proceedings, and online resources since 2017, to offer insights into the current state of AI-based ransomware detection and suggests future research directions. This study contributes significantly to the development of a systematic evaluation framework that evaluates each component of the AI-based detection model framework using specific criteria and methodologies and analyzes how various AI algorithms respond to different ransomware attacks, thereby providing insights for more effective and robust detection methods. This review begins with an overview of AI and ransomware, and discusses various types of ransomware attacks, the process of an attack chain, and emerging trends. We then review the existing literature on the core components of AI-based ransomware detection models, including the datasets and challenges arising during data collection, data pre-processing, feature engineering techniques, model training, and performance evaluation for effective model training. This study assessed the detection performance of AI models using metrics such as accuracy, precision, recall, and F1-score. By synthesizing these findings, we identify gaps in the current research and suggest future directions for enhancing AI-based ransomware detection techniques. The insights provided aim to guide researchers and practitioners in developing more robust methods for detecting and mitigating ransomware attacks by using AI.

Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to target diverse platforms such as PCs, mobile devices, IoT, and cloud platforms. While previous studies have explored single platform-based malware detection, no existing research has comprehensively reviewed malware detection across diverse platforms using machine learning (ML) techniques. With the rise of malware on PC or laptop devices, mobile devices and IoT systems are now being targeted, posing a significant threat to cloud environments. Therefore, a platform-based understanding of malware detection and defense mechanisms is essential for countering this evolving threat. To fill this gap and motivate further research, we present an extensive review of malware detection using ML techniques with respect to PCs, mobile devices, IoT, and cloud platforms. This paper begins with an overview of malware, including its definition, prominent types, analysis, and features. It presents a comprehensive review of machine learning-based malware detection from the recent literature, including journal articles, conference proceedings, and online resources published since 2017. This study also offers insights into the current challenges and outlines future directions for developing adaptable cross-platform malware detection techniques. This study is crucial for understanding the evolving threat landscape and for developing robust detection strategies.

Iron-based amorphous alloys have attracted technological and scientific interests due to their excellent soft magnetic properties. The typical nanocrystalline alloy with the composition of Fe73.5Cu1Nb3Si13.5B9 known as FINEMENT has been studied for structural properties analysis. Recently, it is found that after proper annealing the amorphous alloy like Fe73.5Cu1Nb3Si13.5B9 has a transition to the nanocrystalline state, thus exhibiting good magnetic properties. The alloy in the form of ribbon of 10 mm width and 25mm thickness with the composition of Fe73.5Cu1Nb3Si13.5B9 was prepared by rapid quenching method. The prepared ribbon sample has been annealed for 30 min in a controlled way in the temperature range 490℃ - 680℃. By analyzing X-ray diffraction (XRD) patterns, various structural parameters such as lattice parameters, grain size and silicon content of the nanocrystalline Fe(Si) grains, crystallization behavior and nanocrystalline phase formation have been investigated. In the nanocrystalline state, Cu helps the nucleation of α-Fe(Si) grains while Nb controls their growth, Si and B has been used as glass forming materials. Thus on the residual amorphous, the nanometric Fe(Si) grains develops. From broadening of fundamental peaks, the optimum grain size has been determined in the range of 7 - 23 nm.

Nanocrystalline Fe-based alloys offer a new opportunity for tailoring soft magnetic materials. Nanocrystalline alloy in the form of ribbon with the composition of Fe73.5Cu1Nb3Si13.5B9 was prepared by rapid quenching method for soft magnetic properties analysis. The rapidly quenched alloy has been annealed in a controlled way in the temperature range 490℃ to 680℃ and annealing time 1 min to 60 min. The study of the structural parameters has been investigated by means of XRD analysis. Magnetic properties were analyzed by measuring B-H loop and frequency dependence of initial permeability. Enhanced value of initial permeability by two orders of magnitude and very low value of relative loss factor of the order of 10–3 has been observed with the variation of annealing temperature and time. The initial permeability for the optimum annealed sample has been found 23,064 as compared with 360 for its amorphous counterpart. The initial permeability spectra show dispersion around 100 kHz. Magnetic hysteresis has been investigated by measuring B-H loops at various magnetic fields for different annealing temperature and time. The coercivity and remanence has been found to decrease significantly for optimized annealed condition compared to as-cast state. The core loss of the samples decreases with the annealing time which indicates the good magnetic property of soft magnetic materials.