@conference {263, title = {Automating ECU Identification for Vehicle Security}, booktitle = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)}, year = {2016}, month = {Dec}, abstract = {

The field of vehicular cybersecurity has received considerable media and research attention in the past few years. Given the increasingly connected aspect of consumer automobiles, along with the inherent danger of these machines, there has been a call for experienced security researchers to contribute towards the vehicle security domain. The proprietary nature of Controller Area Network (CAN) bus messages, however, creates a barrier of entry for those unfamiliar, due to the need to identify what the messages on a given vehicle{\textquoteright}s bus are broadcasting. This work aims to automate the process of correlating CAN bus messages with specific Electronic Control Unit (ECU) functions in a new vehicle, by creating a machine learning classifier that has been trained on a dataset of multiple vehicles from different manufacturers. The results show that accurate classification is possible, and that some ECUs that broadcast similar vehicle dynamics broadcast similar CAN messages.

}, keywords = {Acceleration, automobiles, automotive electronics, automotive security, Brakes, broadcasting, CAN bus, CAN bus messages, classification, consumer automobiles, control engineering computing, controller area network, controller area networks, ECU identification automation, electronic control unit, electronic engineering computing, embedded systems, learning (artificial intelligence), machine learning, machine learning classifier, pattern classification, Protocols, Security, security of data, Training, vehicle bus, vehicular ad hoc networks, vehicular cybersecurity, Wheels}, doi = {10.1109/ICMLA.2016.0111}, author = {Jaynes, Michael and Ram Dantu and Varriale, Roland and Evans, Nathaniel} } @conference {67, title = {Another free app: Does it have the right intentions?}, booktitle = {Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on}, year = {2014}, month = {July}, abstract = {

Security and privacy holds a great importance in mobile devices due to the escalated use of smart phone applications (app). This has made the user even more vulnerable to malicious attacks than ever before. We aim to address this problem by proposing a novel framework to identify potential Android malware apps by extracting the intention and their permission requests. First, we constructed a dataset consisting of 1,730 benign apps along with 273 malware samples. Then, both datasets were subjected to source code extraction. From there on, we followed a two phase approach to identify potential malware samples. In phase 1, we constructed a machine learning model to group benign apps into different clusters based on their operations known as the task-intention. Once we trained the model, it was used to identify the task-intention of an Android app. Further, in this phase, we only used the benign apps to construct the task-intentions and none of the malware signatures were involved. Therefore, our approach does not use machine learning models to identify malware apps. Then, for each task-intention group, we extracted the permission-requests of the apps and constructed the probability mass functions (PMF). We named the shape of this PMF as Intention-Shape or I-Shape. In phase 2, we used the permission-requests, task-intentions and I-Shapes to identify potential malware apps. We compared the permission-requests of an unknown app with its corresponding I-Shape to identify the potential malware apps. Using this approach, we obtained an accuracy of 89\% in detecting potential malware samples. The novelty of our work is to perform potential malware identification without training any models with malware signatures, and utilization of I-Shapes to identify such potential malware samples. Our approach can be utilized to identify the safety of an app before it is installed as it performs static code analysis. Further, it can be utilized in pre-screening or multi-layer security sys- ems. It is also highly useful in screening malware apps when launching in Android markets.

}, keywords = {Android app task-intention identification, Android malware apps, Androids, benign apps, clusters, Feature extraction, Humanoid robots, I-Shape, intention-shape, invasive software, learning (artificial intelligence), machine learning model, malicious attacks, Malware, Mathematical model, mobile computing, permission-requests, PMF, probability mass functions, Shape, smart phone applications, smart phones, source code (software), source code extraction, static code analysis, Unsupervised learning}, doi = {10.1109/PST.2014.6890950}, author = {Fazeen, Mohamed and Ram Dantu} } @conference {58, title = {Automatic Calibration Using Receiver Operating Characteristics Curves}, booktitle = {2007 2nd International Conference on Communication Systems Software and Middleware}, year = {2007}, month = {Jan}, abstract = {

Application-level filters, such as e-mail and VoIP spam filters, that analyze dynamic behavior changes are replacing static signature-recognition filters. These application-level filters learn behavior and use that knowledge to filter unwanted requests. Because behavior of a service request{\textquoteright}s participating entities changes rapidly, filters must adapt quickly by using end user{\textquoteright}s preferences about receiving that service request message. Many adaptive filters learn from the participating entities{\textquoteright} behavior; however, none configure themselves automatically to an end user{\textquoteright}s changing tolerance levels. Also, filter administrators cannot manually change the threshold for each service request in real time. Traditional adaptive filters fail when administrators must optimize multiple filter thresholds manually and often. Thus, to improve a filter{\textquoteright}s learning, we must automate its threshold-update process. We propose an automatic threshold-calibration mechanism using Receiver Operating Characteristics (ROC) curves that updates the threshold based on an end user{\textquoteright}s feedback. To demonstrate the mechanism{\textquoteright}s real-time applicability, we integrated it in a Voice over IP (VoIP) spam filter that analyzes incoming Spam over IP Telephony (SPIT) calls. Using this mechanism, we observed good improvement in the VoIP spam filter{\textquoteright}s accuracy. Further, computing and updating the optimum threshold in realtime does not impede the filter{\textquoteright}s temporal performance because we update thresholds after each call{\textquoteright}s completion. Because we reach an optimum threshold for any initial setting, this mechanism works efficiently when we cannot predict end-user behavior. Furthermore, automatic calibration proves efficient when using multiple threshold values.

}, keywords = {adaptive filters, application-level filter learning, automatic threshold-calibration mechanism, behavior, calibration, Computer science, Computer worms, Electronic mail, end user feedback, Filtering, information filtering, information filters, Internet telephony, learning (artificial intelligence), Protocols, receiver operating characteristics curves, sensitivity analysis, service request message, telecommunication traffic, Threshold, tolerance, Viruses (medical)}, doi = {10.1109/COMSWA.2007.382484}, author = {Kolan, Prakash and Vaithilingam, Ram and Ram Dantu} }