Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

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.

Millions of people use the internet daily, including the elderly, who are among the most at-risk groups for cybercrime, such as fraud, hacking, and cyberbullying. However, there has been a lack of cybercrime studies focusing on them. Additionally, various cybersecurity models deployed by governments and organizations are found to be inconvenient and too technical for the elderly, especially those with impairments or conditions that require special attention. This subsequently leads to an increasing number of cybercrimes. Therefore, this paper presents a light review of existing cybersecurity models suitable for the elderly. Three methods were used to review existing cybersecurity awareness models. First, a feasibility study was conducted following several works of literature related to cybersecurity, types of cybercrimes involving the elderly, characteristics of the elderly commonly targeted by cybercriminals, cybersecurity awareness models, and the elderly's learning styles. Second, based on the literature review, the elderly's learning styles were analyzed. Finally, the cybersecurity awareness model suitable for the elderly was chosen and analyzed with respect to their phases, validity, and technicality. In conclusion, the SCSAM-Elderly was selected as the most suitable model for the elderly to learn and spread awareness of cybersecurity. This study could increase cybersecurity awareness and encourage educating the elderly to maximize their knowledge of cybersecurity. The findings from this study can guide organizations, agencies, governments, and educational institutions in their endeavours to educate the elderly about cybercrime and cyberattacks.