Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Mobile device has become an essential tool among the community across the globe and has turned into a necessity in daily life. An extensive usage of mobile devices for everyday life tasks such as online banking, online shopping and exchanging e-mails has enable mobile devices to become data storage for users. The data stored in these mobile devices can contain sensitive and critical information to the users. Hence, making mobile devices as the prime target for cybercriminal. To date, Android based mobile devices is one of the mobile devices that are dominating the phone market. Moreover, the ease of use and open-source feature has made Android based mobile devices popular. However, the widely used Android mobile devices has encourage malware author to write malicious application. In a short duration of time mobile malware has rapidly evolve and have the capability to bypass signature detection approach which requires a constant signature update to detect mobile malware. To overcome this drawback an anomaly detection approach can be used to mitigate this issue. Yet, using a single classifier in an anomaly detection approach will not improve the classification detection performance. Based on this reason, this research formulates an ensemble classification method of different n-gram system call sequence features to improve the accuracy of mobile malware detection. This research proposes n-number of classifier models for each different n-gram sequence call feature. The probability output of each classifier is then combined to produce a better classification performance which is better compared to a single classifier

The Caltech CSN project collects sensor data from thousands of personal devices for realtime response to dangerous earthquakes

The proliferation of smartphones and other powerful sensor-equipped consumer devices enables a new class of Web application: community sense and response (CSR) systems, distinguished from standard Web applications by their use of community-owned commercial sensor hardware. Just as social networks connect and share human-generated content, CSR systems gather, share, and act on sensory data from users' Internet-enabled devices. Here, we discuss the Caltech Community Seismic Network (CSN) as a prototypical CSR system harnessing accelerometers in smartphones and consumer electronics, including the systems and algorithmic challenges of designing, building, and evaluating a scalable network for real-time awareness of dangerous earthquakes

Integrated Framework for Data Quality and Security Evaluation on Mobile Devices

Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor data quality and security metrics. The framework employs a knowledge graph to facilitate its adaptation in new applications development and enables knowledge accumulation. Privacy aspects evaluation is demonstrated by the detection of novel and sophisticated attacks on data privacy on the example of colluded applications attack recognition. We develop multiple calculi for DQ and security evaluation, such as a hierarchical fuzzy rules expert system, neural networks, and an algebraic function. Case studies that demonstrate the framework\u27s performance in solving real-life tasks are presented, and the achieved results are analyzed. These case studies confirm the framework\u27s capability of performing comprehensive DQ evaluations. The framework development resulted in producing multiple products, and tools such as datasets and Android OS applications. The datasets include the knowledge base of sensors embedded in modern mobile devices and their quality analysis, technological signals recordings of smartphones during the normal usage, and attacks on users\u27 privacy. These datasets are made available for public use and can be used for future research in the field of data quality and security. We also released under an open-source license a set of Android OS tools that can be used for data quality and security evaluation

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Keystrokes Inference Attack on Android: A Comparative Evaluation of Sensors and Their Fusion

Introducing motion sensors into smartphones contributed to a wide range of applications in human-phone interaction, gaming, and many others. However, built-in sensors that detect subtle motion changes (e.g. accelerometers), might also reveal information about taps on touch screens: the main user input mode. Few researchers have already demonstrated the idea of exploiting motion sensors as side-channels into inferring keystrokes. Taken at most as initial explorations, much research is still needed to analyze the practicality of the new threat and examine various aspects of its implementation. One important aspect affecting directly the attack effectiveness is the selection of the right combination of sensors, to supply inference data. Although other aspects also play crucial role (e.g. the features set), we start in this paper by focusing on the comparison of different available sensors, in terms of the inference accuracy. We consider individual sensors shipped on Android phones, and study few options of preprocessing their raw datasets as well as fusing several sensors' readings. Our results indicate an outstanding performance of the gyroscope, and the potential of sensors data fusion. However, it seems that sensors with magnetometer component or the accelerometer alone have less benefit in the context of the adverted attack