The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper’s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era.

The Internet’s expansion has changed how the services accessed and businesses operate. Blockchain is an innovative technology that emerged after the rise of the Internet. In addition, it maintains transactions on encrypted databases that are distributed among many computer networks, much like digital ledgers for online transactions. This technology has the potential to establish a decentralized marketplace for Internet retailers. Sensitive information, like customer data and financial statements, should be routinely transferred via e-commerce. As a result, the system becomes a prime target for cybercriminals seeking illegal access to data. As e-commerce increases, so does the frequency of hacker attacks that raise concerns about the safety of e-commerce platforms’ databases. Owing to the sensitivity of customer data, employee records, and customer records, organizations must ensure their protection. A data breach not only affects an enterprise’s financial performance but also erodes clients’ confidence in the platform. Currently, e-commerce businesses face numerous challenges, including the security of the e-commerce system, transparency and trust in its effectiveness. A solution to these issues is the application of blockchain technology in the e-commerce industry. Blockchain technology simplifies fraud detection and investigation by recording transactions and accompanying data. Blockchain technology enables transaction tracking by creating a detailed record of all the related data, which can assist in identifying and preventing fraud in the future. Using blockchain cryptocurrency will record the sender’s address, recipient’s address, amount transferred, and timestamp, which creates an immutable and transparent ledger of all transaction data.

After the emergence of the Internet of Things (IoT), the way devices interact with each other changed, as it allowed automation and seamless communication in various fields. However, various challenges related to security and trust have emerged, hindering the widespread adoption of the IoT. Blockchain technology is considered the ideal solution to face these challenges because of its immutable and decentralized nature. This paper explores the potential of blockchain technology to address critical security and trust challenges within the rapidly growing IoT ecosystem. Through a systematic literature review, this study examines how blockchain’s decentralized, immutable, and transparent features contribute to enhancing security and trust in IoT networks. Key findings indicate that blockchain integration can prevent data manipulation, ensure robust identity management, and facilitate transparent, verifiable transactions, supporting both security and trust in IoT systems. These attributes not only improve IoT security but also promote sustainable practices by optimizing resource efficiency, reducing environmental impact, and enhancing resilience in systems like supply chain management and smart grids. Additionally, this study identifies open research challenges and suggests future directions for optimizing blockchain in IoT environments, focusing on scalability, energy-efficient consensus mechanisms, and efficient data processing.

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware that aims to steal user’s money. Ransomware is a form of malware that encrypts a victim’s files. The attacker then demands a ransom from the victim to restore access to the data upon a large payment. Ransomware is a way of stealing money in which a user’s files are encrypted and the decrypted key is held by the attacker until a ransom amount is paid by the victim. This systematic literature review (SLR) highlights recent papers published between 2020 and 2024. This paper examines existing research on early ransomware detection methods, focusing on the signs, frameworks, and techniques used to identify and detect ransomware before it causes harm. By analyzing a wide range of academic papers, industry reports, and case studies, this review categorizes and assesses the effectiveness of different detection methods, including those based on signatures, behavior patterns, and machine learning (ML). It also looks at new trends and innovative strategies in ransomware detection, offering a classification of detection techniques and pointing out the gaps in current research. The findings provide useful insights for cybersecurity professionals and researchers, helping guide future efforts to develop strong and proactive ransomware detection systems. This review emphasizes the need for ongoing improvements in detection technologies to keep up with the constantly changing ransomware threat landscape.

Software defined networks (SDNs) offer novel approaches to managing networks by separating the control plane from the data plane to enable programmable control over network resources effectively and dynamically. This framework supports monitoring of traffic flow and detection of threats while also enabling easy adaptation of network configurations, which is critical in safeguarding against cyber threats. However, this separation also brings forth security risks that cyber attackers may exploit. In this examination, the basic concepts of SDN are explained, pointing out their benefits compared to conventional networks and exploring the security issues that are part of SDN architectures. Different types of threats that focus on SDN layers are categorized and how they impact network security while suggesting different ways to address them. Furthermore, the review highlights issues and suggests potential research paths to enhance SDN security measures and ensure their effectiveness against ever-changing cyber dangers.

The rapid rise of smartphones and mobile applications has fostered an environment conducive to the advancement of mobile malware, presenting considerable obstacles for both detection and mitigation efforts. This analysis delves into the latest developments in mobile malware, underscoring the shift from basic threats to more complex forms of attacks, including ransomware, spyware, and banking trojans. It discusses significant progress in malware detection techniques, particularly those utilizing behavioral analysis, artificial intelligence (AI), and machine learning (ML). The paper points out deficiencies in current detection methods and stresses the importance of hybrid approaches that integrate both static and dynamic analyses to tackle issues such as obfuscation, polymorphism, and encrypted threats. Furthermore, the research investigates the impact of collaborative threat intelligence sharing and AI-driven tools in improving malware detection and analysis. This systematic review offers a thorough framework for comprehending the progression of mobile malware, assessing existing detection strategies, and pinpointing future research and security initiatives.