The advent of rapid digital technology has opened doors to a new domain for criminal activities, commonly termed as computer crimes. Stringent penalties have been instituted by various countries and institutions to combat these offenses executed through computers or networks. Central to investigating these crimes is digital evidence, pivotal in the realm of digital forensics. The digital forensics process comprises five critical phases: acquisition, preservation, analysis, reconstruction, and presentation. Its core aim is to locate and present digital evidence in court, aiding in determining the culpability of individuals involved in computer crimes. This discipline encompasses specialized fields such as data recovery, conversion, erasure, file identification, encryption, decryption, and IP address tracing to apprehend culprits. This paper conducts a thorough examination of the digital forensics stages using sample cases that elucidate five distinct computer crimes. It delves into evidence origins, collection methods, and preservation techniques utilized in the investigation. Once this meticulous process is completed, the digital forensics team compiles documented findings for presentation in a court of law.

Cloud computing and IoT security are pivotal technologies that have reshaped the IT landscape. Leading tech giants like Google, Microsoft, and Amazon have harnessed their potential, continually enhancing these services. Despite their increasing user bases, these technologies face significant hurdles, with security emerging as the foremost challenge. This paper delves into the security aspects of both cloud computing and IoT services, encompassing security components, processes, threats, and real-world examples of security-related technologies. We also explore the impacts, including benefits, limitations, and future potential of cloud computing and IoT security services. In our exploration, we define cloud computing and the Internet of Things (IoT) to provide a foundational understanding of these technologies. We then investigate their real-world applications, highlighting their relevance and ubiquity in various domains. The core of this paper revolves around the discussion of security issues in cloud computing and IoT. For IoT, we dissect security components, processes, and threats, offering real-world technology examples to illustrate their significance. Similarly, for cloud computing, we delve into its security components, processes, and threats while showcasing practical applications of security technologies. The impact section assesses the benefits of cloud computing and IoT security. It also candidly addresses issues, limitations, and challenges associated with these technologies, offering insights into potential future advancements. This comprehensive study underscores the significance of security in the domains of cloud computing and IoT, offering a valuable resource for both practitioners and researchers. As these technologies continue to evolve, understanding their security implications is crucial for harnessing their full potential.

The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursuit. This study aims to delve deep into this dataset, utilizing data mining methods and robust classification algorithms like Logistic Regression and Random Forest in a Jupyter Notebook environment. Rigorous model training, testing, and fine-tuning strive for the utmost predictive accuracy. Data cleaning and preprocessing play a crucial role in establishing a reliable dataset for accurate predictions. Beyond numbers, the project emphasizes data visualization's impact, transforming raw data into comprehensible insights for effective communication. The Logistic Regression Model exhibits an impressive 87.6% accuracy, highlighting its potential in predicting academic performance. Moreover, the Random Forest Model excels with a remarkable 100% accuracy in forecasting student grades, showcasing its effectiveness in this domain.

Zero-day threats are a more severe and constantly developing menace to various participants including large companies, government offices, and educational establishments. These entities may contain valuable information and essential operations that attract cyber attackers. These exploits are especially devastating as they target weaknesses that an organization’s vendors are not even aware of, making them have no protection against them. This paper focuses on the background and use of zero-day exploitation and the structure and technologies of these complex malware attacks. We examine two notable real-life cases: the case of ‘HAFNIUM targeting Exchange Servers with zero-day exploits’ that was investigated by Microsoft 365 Security and Microsoft Threat Intelligence, and the ‘Log4j vulnerability’ case that was reported by the National Cyber Security Centre. These cases show the critical effects of zero-day vulnerabilities and measures taken to combat them. Additionally, this paper outlines different strategies that can be used to prevent zero-day attacks with the help of modern technologies. These are fast patch release, effective IDS/IPS, and a security model that involves constant vigilance and use of behavioral analytics. Thus, by studying the lifecycle of zero-day vulnerabilities and the structure of the exploits, one can enhance the protection of the organization against the threats that are invisible to traditional security systems. This extensive survey is designed to be useful in understanding the characteristics of zero-day vulnerabilities, the technologies for their mitigation, and the constant threat and development in the field of cybersecurity. Thus, it is possible to strengthen the protection against these threats that are constant and develop with time by analyzing the previous events and predicting potential problems.

Ransomware, a combination of "ransom" and "malware," is a type of malicious software designed to encrypt or block access to a victim's data or system, demanding a ransom for its release Initially targeting individuals, ransomware has evolved to attack businesses for greater financial gain. It mainly exists in two forms: encrypting ransomware, which holds data hostage, and non-encrypting ransomware, which blocks system access and displays a ransom note. The energy sector has been notably targeted by ransomware, exemplified by the 2021 attack on Colonial Pipeline by the DarkSide group, which led to a temporary shutdown and a significant fuel shortage on the US East Coast. Similarly, in 2023, Russian hackers using LockBit ransomware disrupted Royal Mail, halting international deliveries and incurring millions in recovery costs despite no ransom payment. These incidents underscore the need for comprehensive cybersecurity strategies that combine human vigilance with advanced technologies like AI and machine learning. By adopting a multi-layered protection approach, organizations can better prepare for and mitigate the risks posed by ransomware attacks safeguarding sensitive data and ensuring business continuity.