A cyber security architecture for military networks using a cognitive network approach

Cyber security is one of the main topics that are discussed around the world today. The threat is real, and it is unlikely to diminish. People, business, governments, and even armed forces are networked in a way or another. Thus, the cyber threat is also facing military networking. On the other hand, the concept of Network Centric Warfare sets high requirements for military tactical data communications and security. A challenging networking environment and cyber threats force us to consider new approaches to build security on the military communication systems. The purpose of this thesis is to develop a cyber security architecture for military networks, and to evaluate the designed architecture. The architecture is described as a technical functionality. As a new approach, the thesis introduces Cognitive Networks (CN) which are a theoretical concept to build more intelligent, dynamic and even secure communication networks. The cognitive networks are capable of observe the networking environment, make decisions for optimal performance and adapt its system parameter according to the decisions. As a result, the thesis presents a five-layer cyber security architecture that consists of security elements controlled by a cognitive process. The proposed architecture includes the infrastructure, services and application layers that are managed and controlled by the cognitive and management layers. The architecture defines the tasks of the security elements at a functional level without introducing any new protocols or algorithms. For evaluating two separated method were used. The first method is based on the SABSA framework that uses a layered approach to analyze overall security of an organization. The second method was a scenario based method in which a risk severity level is calculated. The evaluation results show that the proposed architecture fulfills the security requirements at least at a high level. However, the evaluation of the proposed architecture proved to be very challenging. Thus, the evaluation results must be considered very critically. The thesis proves the cognitive networks are a promising approach, and they provide lots of benefits when designing a cyber security architecture for the tactical military networks. However, many implementation problems exist, and several details must be considered and studied during the future work.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.