Modern tactical wireless network (TWN) communication technologies are not only capable of transmitting voice but also capable of transmitting data. Due to such capabilities, TWN have high security requirements as any security breach can lead to detrimental effects. Hence, securing such an environment is not only a requirement but also a virtual prerequisite to the network centric warfare operational (NCW) theory. One key to securing this environment is to promptly and accurately recognize information warfare attacks directed to the network and respond to them. This is achieved using intrusion detection systems (IDS). However, false detection of nodes in hostile environment remains a major problem that need to be addressed. Recently, machine learning methods and algorithms have shown applicability and are growing research area for cyber security and intrusion detection. Conversely, several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. The question then becomes, which one amongst these machine learning algorithms have the potential to enhance or address IDS issues in TWN. In this paper, seven machine learning classifiers are analyzed; Multi-Layer Perceptron, Bayesian Network, Support Vector Machine (SMO), Adaboost, Random Forest, Bootstrap Aggregation, and Decision Tree (J48). WEKA tool was used to implement and evaluate the classifiers. The results obtained indicate that ensemble-based learning methods outperformed single learning methods when we consider the detection accuracy metrics; AUC, TPR, and FPR. However, ensemble classifiers tend to be slower in in terms of build time and model test time.

The Internet of things is an infrastructure of connected things (sensors, refrigerators, smart phones, etc.) which is changing the way we live, play, work, communicate and conduct business. Even though these things have such great impact in our lives, they are susceptible to security issues and vulnerabilities, which often result in negative consequences such as loss of confidentiality, integrity and availability. Hence, some users are still skeptical about the use of IoT. This paper explores the security problems that arise due to IoT device firmware updatability. New vulnerabilities are uncovered all of the time. If a device is non-upgradeable, then the vulnerability will exist for the rest of the device's lifetime. As a countermeasure, it is imperative to design security measures to enable automatic update of IoT devices. This paper focuses on current research work based on IoT firmware updates with the aim of highlighting issues related with security of firmware updates. It specifically focuses on the security challenges that face low-end IoT devices and Low-power Wide Area Networks.

Cloud computing is a revolutionary technology that is changing the way people and organizations conduct business. It promises to help organizations save money on IT expenditure while increasing reliability, efficiency and productivity. However, despite the potential benefits that the cloud promises its users, it is facing some security challenges. Insider threats are some of the growing security concerns that are hindering the adoption of the cloud. Cloud providers are faced with a challenge of monitoring usage patterns of users so as to ensure that malicious insiders do not compromise the security of customer data and applications. Solutions are still needed to ensure that the data stored in the cloud is secure from malicious insiders of the cloud service provider. This paper presents an Insider Threat Detection Model that can be used to detect suspicious insider activities. An experimental system was designed to implement this model. This system uses sequential rule mining to detect malicious users by comparing incoming events against user profiles.

Since their inaugural releases in 2007, Google’s Android and Apple’s iOS have grown to dominate the mobile OS market share. Currently, they jointly possess over 99% of the global market share with Android being the leading mobile Operating System of choice worldwide, controlling close to 70% of the market share. Mobile devices have enabled the exponential growth of a plethora of mobile applications that play key roles in enabling many use cases that are pivotal in our daily lives. On the other hand, access to a large pool of potential end users is available to both legitimate and nefarious applications, thus making mobile devices a burgeoning target of malicious applications. Current malware detection solutions rely on tedious, time-consuming, knowledge-based, and manual processes to identify malware. This paper presents BarkDroid, a novel Android malware detection technique that uses the low-level Bark Frequency Cepstral Coefficients audio features to detect malware. The results obtained outperform results obtained using other features on the same datasets. BarkDroid achieved 97.9% accuracy, 98.5% precision, an F1 score of 98.6%, and shorter execution times.