Norah Alhumam

In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes not only conduct rudimentary analyses but have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.

The Internet of Things (IoT) has become a pervasive technology with various applications ranging from smart homes and cities to industrial automation and healthcare. However, the increasing adoption of IoT devices has also raised significant concerns about cybersecurity and privacy. Blockchain, as a distributed and immutable ledger technology, has been proposed as a potential solution to enhance the security and privacy of IoT systems. Blockchain-based IoT systems offer several benefits, such as decentralization, transparency, and data integrity. However, they also pose unique cybersecurity challenges that need to be addressed for their secure and reliable deployment. In this paper, we review the existing literature and highlight the key challenges in cybersecurity for blockchain-based IoT systems. We categorize these challenges into three main areas: (i) IoT device security, (ii) blockchain security, and (iii) integration of IoT devices with blockchain (network security). Through an in-depth analysis, we present the current state of research and discuss potential solutions for each challenge. Additionally, we contribute by identifying future research directions to address these challenges and enhance the cybersecurity of blockchain-based IoT systems.