The proliferation of Android-based mobile devices and mobile applications in the market has triggered the malware author to make the mobile devices as the next profitable target. With user are now able to use mobile devices for various purposes such as web browsing, ubiquitous services, online banking, social networking, MMS and etc, more credential information is expose to exploitation. Applying a similar security solution that work in Desktop environment to mobile devices may not be proper as mobile devices have a limited storage, memory, CPU and power consumption. Hence, there is a need to develop a mobile malware detection that can provide an effective solution to defence the mobile user from any malicious threat and at the same time address the limitation of mobile devices environment. Prior to this matter, this research focused on evaluating the best features selection to be used in the best machine-learning classifiers. To find the best combination of both features selection and classifier, five sets of different feature selection are applies to five different machine learning classifiers. The classifier outcome is evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), and Accuracy. The best combination of both features selection and classifier can be used to reduce features selection and at the same time able to classify the infected android application accurately.

Ransomware attacks are currently one of cybersecurity's greatest and most alluring threats. Antivirus software is frequently ineffective against zero-day malware and ransomware attacks; consequently, significant network infections could result in substantial data loss. Such attacks are also becoming more dynamic and capable of altering their signatures, resulting in a race to the bottom regarding weaponry. Cryptographic ransomware exploits crypto-viral extortion techniques. The malware encrypts the victim's data and demands payment in exchange. The attacker would release the data decryption key after accepting payment. After data encryption, the user has two options: pay the ransom or lose the data. Cryptographic ransomware causes damage that is nearly impossible to undo. Detection at an early stage of a ransomware attack's lifecycle is vital for preventing unintended consequences for the victim. Most ransomware detection technologies concentrate on detection during encryption and post-attack stages. Due to the absence of early behaviour signs, it is challenging to detect ransomware before it begins the unwanted process of mass file encryption. This study examines the relationship between API calls pattern and their nature to determine whether it is ransomware early behaviour. The purpose of this paper is to determine whether this technique can be used to early detect the presence of ransomware activity on a Windows endpoint. 582 ransomware samples that consist of ten ransomware families and 942 benign software samples were analysed. This study proposed RENTAKA, a novel framework for the early detection of cryptographic ransomware. It makes use of characteristics acquired from ransomware behaviour and machine learning. This study presented an algorithm to generate a ransomware pre-encryption dataset. This study, which includes six machine-learning models, gives satisfactory results in detecting cryptographic ransomware. The features used in this research were among the 232 features identified in Windows API calls. Five standard machine learning classifiers were employed in this experiment: Naive Bayes, k-nearest neighbours (kNN), Support Vector Machines (SVM), Random Forest, and J48. In our tests, SVM fared the best, with an accuracy rate of 93.8% and an area under the curve (AUC) of 0.979, respectively. The results indicate that we can distinguish ransomware from benign applications with low false-positive and false-negative rates.

This is an exploratory study that evaluates mobile security awareness and mobile security vulnerabilities based on practices among mobile users in higher education institutions. Using a cross-sectional survey and a convenient sampling technique, students from one of the universities in Malaysia where e-learning and m-learning are implemented were chosen as the target population. The work shows that different layers of mobile security awareness exist among learners of each establishment. Key findings include differences in perception of wireless connection in mobile devices while on a public network, seemingly inappropriate usage, security protection among mobile users, security threat created by mobile devices and security threat awareness behaviour of users. This paper indicates a generic threat model for the processes of mobile learning. After a brief discussion of the attributes involved with mobile user security behaviour and protective characteristics of digital learning from the security and dependability point of view, the use of threat models in mobile users' security is explained. Limitations and future research have been indicated.

SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

Digital forensic is part of forensic science that implicitly covers crime that is related to computer technology. In a cyber crime, digital evidence investigation requires a special procedures and techniques in order to be used and be accepted in court of law. Generally, the goals of these special processes are to identify the origin of the incident reported as well as maintaining the chain of custody so that the legal process can take its option. Subsequently, the traceability process has become a key or an important element of the digital investigation process, as it is capable to map the events of an incident from difference sources in obtaining evidence of an incident to be used for other auxiliary investigation aspects. Hence, this paper introduces a trace map model to illustrate the relationship in the digital forensic investigation process by adapting and integrating the traceability features. The objective of this integration is to provide the capability of trace and map the evidence to the sources and shows the link between the evidence, the entities and the sources involved in the process, particularly in the collection phase of digital forensic investigation framework. Additionally, the proposed model is expected to help the forensic investigator in obtaining accurate and complete evidence that can be further used in a court of law.