It is argued that the advancement of Information, Communication and Technology went hand in hand with the emergence of certain threats and vulnerabilities to cybersecurity. In several cases, cyber attacks have targeted the information, communication and infrastructure networks of numerous organizations. Today, hackers and intruders have advanced technology within their scope that lets them access the organizational information system. The present study highlights numerous internet security related problems, it offers a broad-based overview of internet threats from the perspective of business enterprises, along with prevention measures and enhanced safety strategies. A systematic analysis of secondary literature was introduced by researchers, the study found that it is critical for organizations to choose an IT security management tool that can be categorized as best practices and standards. The Security Incident Event Management (SIEM) framework is one key instrument proposed here. SIEM instruments help security analysts gain insight into the security threats targeting the IT structures of a given organization.

Computers and networks serve a host of functions. As they provide the much-needed services, unscrupulous parties also make work difficult by promoting cyberattacks that could lead to sensitive data loss and inability to access a personal computer. The submission explores the use of firewalls in protecting users against network-driven attacks. Firewalls intercept malware attacks, phishing, and identity theft by denying access to certain suspicious sites. However, users must realize that some hardware firewalls cannot protect them from certain web attacks and software firewalls that are in-built fail to detect malicious attacks on some occasions. Therefore, some entities are forced to incorporate both hardware and software to get the desired level of protection. Using an experimental research method, the study explores the effectiveness of firewall protection by presenting more gains that demerits.

Vulnerabilities caused by cyberattacks impact negatively on the increased dependence of society on information and communication technologies (ICT) to conduct personal and business functions. An example of such an attack is the distributed denial of service (DDoS). This attack can disrupt business communication with clients and frustrate staff because of its potential to reduce connectivity and exchange of information between companies and their clients. To prevent these attacks, their modus operandi needs to be examined. Studies also must examine the latest trends of tactics used by DDoS attackers. The current paper aims to investigate several machine learning technologies for the detection of DDoS attacks. The accuracy of detection of DDoS attacks is examined using the CIC-DDoS dataset. Two techniques were used to preprocess the DDoS dataset to acquire the relevant features used to conduct the DDoS test. A total of 4 machine learning models have been used to detect DDoS. The results from the experiments show that the Random Forest machine learning model enabled DDoS detection with the highest accuracy of 99.997%, higher than Convolutional Neural Network (CNN), CatBoost, and Light GB. The novelty of the results is that they are based on empirical tests to determine the effectiveness of various machine learning models, thus improving the reliability and validity of the results and enhanced by the use of CIC-DDoS datasets associated with actual incidences of DDoS attacks, which makes the research framework easy to replicate to establish the validity of the findings.

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