Towards 5G: Context Aware Resource Allocation for Energy Saving

With the objective of providing high quality of service (QoS), 5G system will need to be context-aware that uses context information in a real-time mode depends on network, devices, applications, and the environment of users’. In order to continue enjoying the benefits provided by future technologies such as 5G, we need to find solutions for reducing energy consumption. One promising solution is taking advantage of the context information available in today’s networks. In this paper, we take a step towards 5G by utilizing context information in the scheduling process as conventional packet scheduling algorithms are mainly designed for increasing throughput but not for the energy saving. We investigate a Context Aware Scheduling (CAS) algorithm which considers the context information of users along with conventional metrics for scheduling. An information model of context awareness along with a context aware framework for resource management is also presented in this paper. CAS is simulated applying a system level simulator and the results obtained show that considerable amount of energy is saved by utilizing the context information compare to conventional scheduling algorithms.     

An Autonomous Host-Based Intrusion Detection System for Android Mobile Devices

Intrusion Detection System (IDS) is crucial to protect smartphones from imminent security breaches and ensure user privacy. Android is the most popular mobile Operating System (OS), holding above 85% market share. The traffic generated by smartphones is expected to exceed the one generated by personal computers by 2021. Consequently, this prevalent mobile OS will stay one of the most attractive targets for potential attacks on fifth generation mobile networks (5G). Although Android malware detection has received considerable attention, offered solutions mostly rely on performing resource intensive analysis on a server, assuming a continuous connection between the device and the server, or on employing supervised Machine Learning (ML) algorithms for profiling the malware’s behaviour, which essentially require a training dataset consisting of thousands of examples from both benign and malicious profiles. However, in practice, collecting malicious examples is tedious since it entails infecting the device and collecting thousands of samples in order to characterise the malware’s behaviour and the labelling has to be done manually. In this paper, we propose a novel Host-based IDS (HIDS) incorporating statistical and semi-supervised ML algorithms. The advantage of our proposed IDS is two folds. First, it is wholly autonomous and runs on the mobile device, without needing any connection to a server. Second, it requires only benign examples for tuning, with potentially a few malicious ones. The evaluation results show that the proposed IDS achieves a very promising accuracy of above 0.9983, reaching up to 1.