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PurposeGiven how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.Design/methodology/approachThis research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.FindingsThe tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.Originality/valueCyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

Nowadays, technology has led to cheap and easy communication through use of various social media platforms. Traditionally grapevine was communicated from one person to another directly without any media in between. The rate of transmission of information by grapevine has increased significantly due to social media. This paper seeks to find the effectiveness of grapevine as a communication strategy in tertiary administration operating during the COVID-19 pandemic. A case study was done within the department of Computer Science at a state university in Zimbabwe. Information which got to employees and students from management in a structured manner was compared to that which got to employees and students through grapevine. The information was obtained through document analysis and interviews. This information was analysed and the effectiveness of grapevine was deduced.The effectiveness of communicating through grapevine was measured in terms of the information being able to be accessed, its clarity level, the level of distortion, rate of transmission from sender to receiver and whether there was a two way communication.

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