The enormous advancement of digital technology and the Internet usage have significantly improved our lives, but have threatened our security and privacy as well. Cyberattacks may have harmful long-term implications to individuals and organizations. High school students are accessible targets for various cybercrimes due to the lack of cybersecurity knowledge and cyber-safe practices. It is important that education about cybersecurity awareness and cyber hygiene practices must begin at a young age. Offering cybersecurity knowledge through interactive tutorials and game-based techniques may increase students' interest in this domain. To develop a security mindset and improve the perception and attitude towards cybersecurity, we created an interactive cybersecurity framework for high school students. Through this framework, we attempt to ef-fectively educate students in cybersecurity through interactive animated visualization modules developed in Unity 3D engine, enabling learning of physical, software, and mathematical aspects of cybersecurity. Each topic in the visualization tool is explained in four stages including information, interaction, explanation, and assessment. Several surveys have been conducted to determine whether this framework enhances users' cognitive abilities.

The increasing use of computer technologies to perform everyday activities simplifies living, but brings the underlying cybersecurity concern to the fore. Due to the accessibility of smartphones, many teenagers are “online” for significant hours in a day. Many middle and high school students have been victims of a cybercrime through online activities. Additionally, various incidents of Internet fraud have been reported where teenagers are persuaded to buy games, music, and videos without realizing they are falling for a scam or disclosing their credit card information. Studies have shown that implementing a successful security awareness camp is crucial in boosting cybersecurity and attracting talent to this domain. This paper discusses our efforts on creating smartphone apps in the context of cyber-security to encourage safe use of apps and raise awareness among teenagers. The strategy used is to develop apps with the intention of closing security gaps. By doing this, teenagers gain a wealth of information about cybersecurity. This work aims to develop students' problem-solving skills and create a cybersecurity mindset for dealing with real-world cybersecurity-related problems such as malware or phishing assaults and to promote interest in cybersecurity careers among high school students utilizing smartphone-based interactive learning modules. We also examine gender-specific patterns and evaluate whether students' cybersecurity problem-solving skills have improved due to this novel intervention.

Several host intrusion detection systems (HIDSs) based on system call analysis have been proposed in the past to detect intrusions and malware using relevant datasets. Machine learning (ML) techniques have been applied on those datasets to improve the performances of HIDSs. However, the emphasis given on their real-world deployment is limited. To address this issue, we propose a framework for system call processing for benign and malware Android apps with an ability of early detection of malware. We extracted and analyzed system call traces for benign and malware apps, and processed their system call traces with N-gram and TF-IDF models. Six ML algorithms – Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machines, and Multi-layer Perceptron – were trained for the malware detection system. The experimental results demonstrate that our Android malware detection system (AMDS), using traces of 3000 system calls, is capable of early detection with an average accuracy of 99.34%. We also implemented an Android app based on a client-server architecture for the proposed AMDS to demonstrate its deployment for malware detection in real-time.

As the use of Wi-Fi networks grows, so does the increase in security threats. Attackers continue to improve their attack methods, which create the need for developing effective mechanisms to detect the sophisticated attacks. In this work, we propose an implementation of intrusion detection system for Wi-Fi networks using an ensemble learning method. The AWID Wi-Fi intrusion dataset is used to discover the necessary features needed for the efficient IDS implementation. We apply several ensemble learning methods on this dataset and finalize the best one for the proposed IDS implementation. The performance of IDS is reported using well-known metrics including accuracy, precision, recall, and f-measure.