@conference {576, title = {Adaptive and Predictive SDN Control During DDoS Attacks}, booktitle = {2020 IEEE International Conference on Intelligence and Security Informatics (ISI)}, year = {2020}, abstract = {

While Distributed Denial of Service (DDoS) attacks continue to plague the Internet, Software Defined Networks (SDNs) offer the promise of configuration elasticity for all devices in the plant to mitigate these attacks and stabilize the network in real-time. Since it is difficult to distinguish between legitimate and attack traffic, we propose a closed-loop feedback control mechanism using a multi-loop proportional, integral (PI) controller and a model predictive controller (MPC) that will regulate the system characteristics with disturbance rejection. Results from our setup implemented in the SDN platform show that our dynamic and predictive control models provide for a graceful degradation of real-time services in the SDN environment in real-time.

}, doi = {10.1109/ISI49825.2020.9280542}, author = {Vempati, Jagannadh and Dantu, Ram and Badruddoja, Syed and Thompson, Mark} } @conference {334, title = {Automatic Feedback Control for Graceful Degradation of Real-Time Services in the Face of an Attack}, booktitle = {2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)}, year = {2018}, month = {Oct}, abstract = {

Distributed denial of service (DDoS) attacks continue to pose a serious threat to various businesses and consumers. With the growth in the number of devices connected to the internet, these attacks continue to grow in number. Despite the availability of security tools, the attacks continue to happen and are causing various businesses to sweat. These tools may take anywhere from a few hours to a few days to counter the attacks, which is unacceptable. In this paper, we put forth a novel feedback control mechanism to minimize the effect of volumetric attacks such as DDoS. During an attack, the feedback control model detects and reduces the impact of the attack by maintaining the service level agreements (SLA) of the network service. The controller makes intelligent decisions to ensure the quality of service (QoS) metrics are gracefully degraded by tuning the micro-firewall rules such as the committed information rate and burst size. A proportional Integral (PI) controller is used as a closed-loop feedback controller to maintain the stability of the network in spite of an attack. This proposed architecture is verified in a lab setup, and we observe that we are able to minimize the degradation of the real-time service so that the user{\textquoteright}s quality of experience (QoE) is preserved. We validate the proposed architecture with a model generated by using the system identification technique. Results from the setup show that the closed-loop feedback control model stabilizes the network in real-time.

}, keywords = {authorisation, automatic feedback control, closed loop systems, closed-loop feedback control model, Computer crime, computer network management, computer network security, DDoS, Distributed Denial of Service attacks, feedback control, Graceful Degradation, Internet, Mathematical model, network service, Packet loss, PI control, proportional Integral controller, QoE, QoS, quality of experience, quality of service, Real Time Services, real-time service, real-time systems, Resilient, service level agreements, service metrics, SLA, state space, Streaming media, System Identification, volumetric attacks}, doi = {10.1109/CIC.2018.00-37}, author = {Vempati, Jagannadh and Ram Dantu} } @conference {382, title = {Performance Analysis of Elliptic Curves for Real-Time Video Encryption}, booktitle = {2018 National Cyber Summit (NCS)}, year = {2018}, month = {June}, abstract = {

The use of real-time video streaming is increasing day-by-day, and its security has become a serious issue now. Video encryption is a challenging task because of its large frame size. Video encryption can be done with symmetric key as well as asymmetric key encryption. Among different asymmetric key encryption technique, ECC performs better than other algorithms like RSA in terms of smaller key size and faster encryption and decryption operation. In this work, we have analyzed the performance of 18 different ECC curves and suggested some suitable curves for real-time video encryption.

}, keywords = {Delays, ECC, Elliptic curve cryptography, encryption, Jitter, real-time systems, Real-time video streaming, Security, Streaming media}, doi = {10.1109/NCS.2018.00015}, author = {Sen, Nilanjan and Ram Dantu and Vempati, Jagannadh and Thompson, Mark} } @conference {340, title = {Uninterrupted Video Surveillance in the Face of an Attack}, booktitle = {2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)}, year = {2018}, month = {Aug}, abstract = {

Distributed denial of service (DDoS) attacks continue to plague businesses and consumers alike, and due to an ever-growing digital landscape, these attacks are expected to grow in size and complexity. Current mitigation techniques ranging from hours to days are completely unacceptable given the cost and inconvenience these attacks place in our society. This paper puts forth three feedback control mechanisms to minimize the effects of DDoS attacks on real-time traffic. The first, called differentiated services code point (DSCP) Markdown, is a passive approach that uses micro firewall rules to lower the priority of out-of-profile packets while a second mechanism actively drops the out-of-profile packets based on rate and burst size parameters. The third technique uses parallel links when feedback is applied to stabilize the network after an attack has been detected. Results from all three techniques have shown to have a positive effect on real-time traffic. The first two approaches were able to stabilize network traffic in real-time, while the parallel links technique resulted in a slight delay. We validate the feedback mechanisms with our model that was generated using the system identification technique. Results show that the feedback architecture provides a fit accuracy with positive results.

}, keywords = {attack mitigation techniques, authorisation, autoregressive processes, burst size parameters, Computer crime, computer network security, DDoS, DDoS attacks, differentiated services code point markdown, DiffServ networks, distributed denial of service attack, DSCP, DSCP Markdown approach, feedback control, feedback mechanisms, Internet, IP networks, Jitter, Mathematical model, micro-firewall, out-of-profile packets, parallel links technique, QoS, quality of service, real-time services, real-time systems, Resilient, Streaming media, system identification technique, uninterrupted video surveillance, video surveillance}, doi = {10.1109/TrustCom/BigDataSE.2018.00121}, author = {Vempati, Jagannadh and Ram Dantu and Thompson, Mark} } @conference {281, title = {Feedback Control for Resiliency in Face of an Attack}, booktitle = {Proceedings of the 12th Annual Conference on Cyber and Information Security Research}, year = {2017}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Distributed Denial of Service(DDoS) attacks are inevitable. The existing defensive mechanisms are relatively outdated. In this paper, we present a passive mechanism to reduce the impact of an attack on the network. We designed and implemented a robust feedback architecture, to maintain the stability of the network despite attacks. During an attack, the controller of the feedback architecture detects the irregularities in the response time and the necessary changes are made to the configuration to maintain the network in steady state. In this approach first, we model the network using black-box system identification technique. Second, we validate the model with test data by conducting various experiments such as varying the network topology. Last, we test the model with the feedback architecture built in our lab environment. Results show that the feedback architecture provides an average model fit accuracy with positive results.

}, isbn = {978-1-4503-4855-3}, doi = {10.1145/3064814.3064815}, url = {http://doi.acm.org/10.1145/3064814.3064815}, author = {Vempati, Jagannadh and Thompson, Mark and Ram Dantu} } @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 {216, title = {Smart phone monitoring of second heart sound split}, booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2014}, month = {Aug}, abstract = {

Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.

}, keywords = {acoustic signal processing, aortic component, biomedical equipment, Biomedical monitoring, cardiac diagnosis, cardiology, continuous wavelet transform, Continuous wavelet transforms, discrete wavelet transform, discrete wavelet transforms, fast Fourier transform, fast Fourier transforms, frequency domain, Heart, heart auscultation, heartbeat recording, medical signal processing, Monitoring, patient diagnosis, patient monitoring, pulmonic component, second heart sound split, smart phone monitoring, smart phones, stethoscope}, doi = {10.1109/EMBC.2014.6944050}, author = {Thiyagaraja, Shanti and Vempati, Jagannadh and Ram Dantu and Sarma, Tom and Dantu, Siva} } @conference {217, title = {NFC Based Two-Pass Mobile Authentication}, booktitle = {Research in Attacks, Intrusions, and Defenses: 16th International Symposium, RAID 2013, Rodney Bay, St. Lucia, October 23-25, 2013, Proceedings}, year = {2013}, publisher = {Springer}, organization = {Springer}, author = {Vempati, Jagannadh and Bajwa, Garima 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} }