energy consumption metrics for mobile device dynamic malware detection

Abstract The ineffectiveness of signature-based malware detection systems prevents the detection of malware, even objects of trivial obfuscation techniques, makes mobile devices vulnerable. In this paper a dynamic technique to detect malware on Android platform is proposed. We exploit a set of energy related features i.e., feature which can be symptomatic of abnormal battery consumption. We built different models exploiting four different supervised machine learning classification algorithms, obtaining for all the evaluated models an accuracy greater than 0.91

Technique for IoT cyberattacks detection based on the energy consumption analysis

Today Smart Home is a system for managing the basic life support processes of both small systems (commercial, office premises, apartments, cottages) and large automated complexes (commercial and industrial complexes). One of the important tasks to be solved by the concept of a modern Smart Home is the problem of preventing the malware spread and the usage of IoT infrastructure. One of the possible approaches for abnormal behavior of the IoT devices and IoT cyberattack detection is the monitoring of the energy consumption. Thus, an effective control and monitoring of heating, ventilation, air conditioning, more efficient use of traditional appliances and the introduction of energy-efficient equipment in the building are important to ensure and decision making in the terms of cybersecurity. In addition, improving the efficiency of energy management and monitoring is the approach to increasing effectiveness of the IoT cyberattack detection in the IoT infrastructure. The paper presents a technique for IoT attacks detection based on the IoT devices energy consumption analysis, which take into account the energy consumption related user's preference modes. With aim to improve the accuracy of IoT cyberattacks detection and localize the IoT malware on these IoT devices the IoT software opcodes sequences analysis is applied. The proposed approach allows detecting the performing of the IoT devices such attacks, for example, as DoS/DDoS with high efficiency, at a level of about 99.88% and localizing malicious IoT software on these devices with accuracy of about 99.66%