@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} } @article {28, title = {Behavior-based Adaptive Call Predictor}, journal = {ACM Trans. Auton. Adapt. Syst.}, volume = {6}, year = {2011}, pages = {21:1{\textendash}21:28}, abstract = {

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people{\textquoteright}s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely {\textquotedblleft}Incoming Call Forecast{\textquotedblright} and {\textquotedblleft}Intelligent Address Book.{\textquotedblright} For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller{\textquoteright}s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user{\textquoteright}s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user{\textquoteright}s calling behavior and reciprocity. We show that the recent trend of the caller/user{\textquoteright}s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user{\textquoteright}s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8\%, false negative rate of 1\%, and error rate of 9\%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70\% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.

}, keywords = {behavior, call logs, call matrix, convergence time, Prediction, trace distance}, issn = {1556-4665}, doi = {10.1145/2019583.2019588}, url = {http://doi.acm.org/10.1145/2019583.2019588}, author = {Phithakkitnukoon, Santi and Ram Dantu and Claxton, Rob and Eagle, Nathan} } @article {294, title = {System and method for packet level restoration of IP traffic using overhead signaling in a fiber optic ring network}, year = {2007}, month = {01/2007}, publisher = {Alcatel SA}, address = {US}, abstract = {

An apparatus and a method for forwarding data packets through a fiber optic ring network includes forwarding data packets on a packet by packet basis. A node on the fiber optic ring network decides on a packet by packet basis whether to transmit on the working or protection path. Because this decision is being made on a packet level, reservation of throughput resources no longer is made at a one to one ratio. Rather, protection path resources are reserved at a ratio significantly less than one to one. In one embodiment, no resources are reserved for path restoration or protection path routing. Rather, quality of service provisioning is used to resolve interference situations wherein instantaneous demand exceeds capacity. A node evaluates ring conditions in relation to the demand of traffic resources and the relative quality of service ratings to determine whether and how to forward a packet. Additionally, a node decides whether to use the working or protection path by considering the final destination on the fiber optic ring network, any identified ring conditions and, in some embodiments, quality of service. The ring conditions to which the inventive nodes respond include OSI layer 1, layer 2 and layer 3 conditions.

}, keywords = {fiber, network, node, optic, ring}, issn = {US7167443B1}, url = {https://patents.google.com/patent/US7167443/en}, author = {Ram Dantu and Ester, Gary and O{\textquoteright}Connell, Pete} } @article {295, title = {Quality indicator and method for frame selection in wireless network}, year = {2006}, month = {07/2006}, publisher = {Cisco Technology Inc}, chapter = {US7079512B1}, address = {US}, abstract = {

A method and system for indicating quality of a radio frame transmitted over a wireless link includes receiving a radio frame from the wireless link. A power indicator for the radio frame is determined based on a power control trend of the wireless link. A quality indicator for the radio frame is generated based on the power indicator.

}, keywords = {frame, indicator, link, quality, radio}, url = {https://patents.google.com/patent/US7079512/en}, author = {Alam, Mahbubul and Ram Dantu and Holur, Balaji and Eyceoz, Tugay} }