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Research Focus & Our Interests

The following topics are of particular interest to our institute in our areas of expertise:

Hardware Security

Nowadays, many companies from all over the world are involved in the IC fabrication process to enhance competence and manufacturability due to the complexity of IC design. The abbreviated time-to-market for system-on-chips (SoCs) increases the difficulty by leaving unintentional vulnerabilities in the design. These issues can be exploited by attackers once the microchips are in the field and further lead to more opportunities for adversaries to make malicious inclusions/alterations, information leakage, counterfeiting, piracy activities, adversarial attacks, etc. With increasing security vulnerabilities and trust issues, the Hardware vulnerability continues to become a national and global security threat that impacts the confidentiality, integrity, lifetime, etc. Our research focuses on all security issues such as side- channel, hardware Trojans, IP piracy, reverse engineering, and study hardware security primitives such as PUF, TRNG, Obfuscation.

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Related Selected Publications:

  • F. Tehranipoor, N. Karimian, M. Mozaffari Kermani, and H. Mahmoodi. "Deep RNN-Oriented Paradigm Shift Through Bocanet: Broken obfuscated circuit attack." In Proceedings of 2019 on Great Lakes Symposium on VLSI, pp. 335-338. 2019.

  • F. Tehranipoor, N. Karimian, W. Yan and J. A. Chandy, "DRAM-Based Intrinsic Physically Unclonable Functions for System-Level Security and Authentication," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 3, pp. 1085-1097, March 2017.

  • Z. Guo, N. Karimian, M. M. Tehranipoor, and D. Forte, "Hardware security meets biometrics for the age of IoT," 2016 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1318-1321, Montreal, QC, 2016.

  • N. Karimian, F. Tehranipoor, M. T. Rahman, S. Kelly, and D. Forte, "Genetic Algorithm for hardware Trojan detection with ring oscillator network (RON)," 2015 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1-6, Waltham, MA, 2015.

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Biometric Security

Existing user authentication approaches knowledge-based (e.g., PIN, password, or possession-based (e.g., smartcard) can be circumvented by guessing, hacking, or theft. Managing so many devices with passwords alone is ripe with challenges and it is becoming more unwieldy as the number of digital services and systems we access every day continues to grow. Compared with conventional authentication techniques, such as digital passwords, personal identification numbers, and smartcards/tokens, biometrics provide a more robust method for identifying a person, i.e., based on their distinctive physical characteristics. Biometrics can also be considered more seamless and convenient, especially for continuous authentication. Specifically, in the healthcare domain, systems already capture and process certain universal human biological signals for health monitoring. With a few minor tweaks, the same systems can re-purpose these signals for continuous human authentication. Among the human biological signals, electrocardiogram (ECG) and photoplethysmograph (PPG) are two of the most popular signals for capturing the electrical activity of the heart and changes in blood flow during heart activity.

 

Related Selected Publications:

  • N. Karimian, M. Tehranipoor, D. Woodard and D. Forte, "Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT," in IEEE Access, vol. 7, pp. 49135-49149, 2019.

  • N. Karimian, Z. Guo, M. Tehranipoor and D. Forte, "Highly Reliable Key Generation From Electrocardiogram (ECG)," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 6, pp. 1400-1411, June 2017.

  • N. Karimian, D. L. Woodard and D. Forte, "On the vulnerability of ECG verification to online presentation attacks," 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 143-151, Denver, CO, 2017. (IAPR TC4 Best Student Paper Award)

  • N. Karimian, M. Tehranipoor and D. Forte, "Non-fiducial PPG-based authentication for healthcare application," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 429-432, Orlando, FL, 2017.

  • N. Karimian, Z. Guo, M. Tehranipoor and D. Forte, "Human recognition from photoplethysmography (PPG) based on non-fiducial features," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4636-4640, New Orleans, LA, 2017.

  • N. Karimian, P. A. Wortman and F. Tehranipoor, "Evolving authentication design considerations for the Internet of biometric things (IoBT)," 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 1-10, Pittsburgh, PA, 2016.

Network Security

Existing user authentication approaches knowledge-based (e.g., PIN, password, or possession-based (e.g., smartcard) can be circumvented by guessing, hacking, or theft. Managing so many devices with passwords alone is ripe with challenges and it is becoming more unwieldy as the number of digital services and systems we access every day continues to grow. Compared with conventional authentication techniques, such as digital passwords, personal identification numbers, and smartcards/tokens, biometrics provide a more robust method for identifying a person, i.e., based on their distinctive physical characteristics. Biometrics can also be considered more seamless and convenient, especially for continuous authentication. Specifically, in the healthcare domain, systems already capture and process certain universal human biological signals for health monitoring. With a few minor tweaks, the same systems can re-purpose these signals for continuous human authentication. Among the human biological signals, electrocardiogram (ECG) and photoplethysmograph (PPG) are two of the most popular signals for capturing the electrical activity of the heart and changes in blood flow during heart activity.

 

Related Selected Publications:

  • Manohar Raavi, Simeon Wuthier, Pranav Chandramouli, Yaroslav Balytskyi, Xiaobo Zhou, and Sang-Yoon Chang, Security Comparisons and Performance Analyses of Post-Quantum Signature Algorithms, International Conference on Applied Cryptography and Network Security (ACNS), 2021, Virtual

  • Sang-Yoon Chang, Younghee Park, Nikhil Vijayakumar Kengalahalli, and Xiaobo Zhou, Query-Crafting DoS Threats Against Internet DNS, Proceedings of IEEE International Conference on Communications and Network Security (CNS), 2020, Avignon, France

  • Chungsik Song, Wenjun Fan, Sang-Yoon Chang, and Younghee Park, Reconstructing Classification to Enhance Machine-Learning Based Network Intrusion Detection by Embracing Ambiguity, Silicon Valley Cybersecurity Conference (SVCC), 2020, Virtual

  • N. Karimian, M. Tehranipoor, D. Woodard and D. Forte, "Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT," in IEEE Access, vol. 7, pp. 49135-49149, 2019.

  • N. Karimian, Z. Guo, M. Tehranipoor and D. Forte, "Highly Reliable Key Generation From Electrocardiogram (ECG)," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 6, pp. 1400-1411, June 2017.

  • N. Karimian, D. L. Woodard and D. Forte, "On the vulnerability of ECG verification to online presentation attacks," 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 143-151, Denver, CO, 2017. (IAPR TC4 Best Student Paper Award)

  • N. Karimian, M. Tehranipoor and D. Forte, "Non-fiducial PPG-based authentication for healthcare application," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 429-432, Orlando, FL, 2017.

  • N. Karimian, Z. Guo, M. Tehranipoor and D. Forte, "Human recognition from photoplethysmography (PPG) based on non-fiducial features," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4636-4640, New Orleans, LA, 2017.

  • N. Karimian, P. A. Wortman and F. Tehranipoor, "Evolving authentication design considerations for the Internet of biometric things (IoBT)," 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 1-10, Pittsburgh, PA, 2016.

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Software Security

Malware and software security deals with detecting malicious behavior in applications. While the most widely used approaches are very efficient in detecting known attacking patterns, modern researches are focusing on applications of machine learning algorithms to detect unknown threats. Malware detection based on machine learning typically involves training and testing models for each malware family under consideration. It is split into two main strategies, which are analyzing the file structure without actively running the application and relying on a virtual machine to analyze its behavior during the execution time. These two approaches are usually referenced as static and dynamic analysis, respectively. Mainly focusing on Windows and Android-based malware, we aim to design and train machine learning models to be able to detect unknown malicious applications. To achieve this goal, we rely on several software features, such as system calls, opcodes list, permissions requested, and many more.

 

Related Selected Publications:

  • Dennis Dang, Fabio Di Troia, Mark Stamp. Malware classification using long short-term memory models, 5th International Workshop on Formal Methods for Security Engineering, 2021.

  • Aparna Sunil Kale, Fabio Di Troia, Mark Stamp. Malware classification with word embedding features, 5th International Workshop on Formal Methods for Security Engineering, 2021.

  • Zidong Jiang, Fabio Di Troia, Mark Stamp. Sentiment analysis for troll detection on Weibo, Malware Analysis using Artificial Intelligence and Deep Learning, Springer, 2020.

  • Tazmina Sharmin, Fabio Di Troia, Mark Stamp, Katerina Potika. Convolutional neural networks for image spam detection, Information Security Journal: A Global Perspective, 2020.

  • Mayuri Wadkar, Fabio Di Troia, Mark Stamp, “Detecting malware evolution using support vector machines”, in Expert Systems with Applications, April 2020.

  • Samanvitha Basole, Fabio Di Troia, Mark Stamp, “Multifamily malware models” in Journal of Computer Virology and Hacking Techniques, Jan. 2020.

  • Anukriti Sinha, Fabio Di Troia, Mark Stamp, Philip Heller. Emulation vs instrumentation for Android malware detection, Digital Forensic Investigation of Internet of Things (IoT) Devices, Springer 2019.

  • Ashraf Saber, Fabio Di Troia, Mark Stamp. Intrusion detection and CAN vehicle networks, Digital Forensic Investigation of Internet of Things (IoT) Devices, Springer 2019.

  • Supraja Suresh, Fabio Di Troia, Katerina Potika, Mark Stamp, “An analysis of Android adware”, in Journal of Computer Virology and Hacking Techniques, Sep. 2019.

  • Anish Singh Shekhawat, Fabio Di Troia, Mark Stamp, “Feature analysis of encrypted malicious traffic”, in Expert Systems with Applications, July 2019.

  • Naoki Kudo, Toshihiro Yamauchi, Thomas H. Austin. Access control mechanism to mitigate Cordova plugin attacks in hybrid applications. In Journal of Information Processing, 2018.

  • Aditya Raghavan, Fabio Di Troia, Mark Stamp, “Hidden Markov models with random restarts versus boosting for malware detection”, in Journal of Computer Virology and Hacking Techniques, Aug. 2018.

  • Naoki Kudo, Toshihiro Yamauchi, and Thomas H. Austin. Access control for plugins in Cordova-based hybrid applications. In International Conference on Advanced Information Networking and Applications, IEEE, 2017.

  • Tejas Saoji, Thomas H. Austin, and Cormac Flanagan. Using precise taint tracking for auto-sanitization. In PLAS: Proceedings of the ACM SIGPLAN Workshop on Programming Languages and Analysis for Security, ACM 2017.

  • Ismeet Kaur Makkar, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. SocioBot: a Twitter-based botnet. In International Journal of Security and Networks, 2017.

  • Thomas H Austin, Tommy Schmitz, Cormac Flanagan, “Multiple facets for dynamic information flow with exceptions,” ACM Transactions on Programming Languages and Systems (TOPLAS), 2017.

  • Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Advanced Transcriptase for JavaScript malware. In MALCON: International Conference on Malicious and Unwanted Software, 2016.

  • Jean Yang, Travis Hance, Thomas H. Austin, Armando Solar-Lezama, Cormac Flanagan, Stephen Chong. Precise, dynamic information flow for database-backed applications. In PLDI: Conference on Programming Language Design and Implementation, ACM, 2016.

  • Thomas Schmitz, Dustin Rhodes, Thomas H. Austin, Kenneth Knowles, and Cormac Flanagan. Faceted dynamic information flow via control and data monads. In POST: International Conference on Principles of Security and Trust, 2016.

  • Prakasam Kannan, Thomas H. Austin, Mark Stamp, Tim Disney, and Cormac Flanagan. Virtual values for taint and information flow analysis. In META: Workshop on Meta-Programming Techniques and Reflection, 2016.

  • Swapna Vemparala, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Malware detection using dynamic birthmarks. In IWSPA: International Workshop on Security and Privacy Analytics, ACM, 2016.

  • Nikitha Ganesh, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Static analysis of malicious Java applets. In IWSPA: International Workshop on Security and Privacy Analytics, ACM, 2016.

  • Swathi Pai, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Clustering for malware classification. In Journal in Computer Virology and Hacking Techniques, Springer, 2016.

  • Tanuvir Singh, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Support vector machines and malware detection. In Journal in Computer Virology and Hacking Techniques, Springer, 2015.

  • Anusha Damodaran, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. A comparison of static, dynamic, and hybrid analysis for malware detection. In Journal in Computer Virology and Hacking Techniques, Springer 2015.

  • Usha Narra, Fabio Di Troia, Corrado Aaron Visaggio, Thomas H. Austin, and Mark Stamp. Clustering versus SVM for malware detection. In Journal in Computer Virology and Hacking Techniques, Springer, 2015.

  • Yasuyuki Nogami and Thomas H. Austin. Associative rational points for improving random walks with collision-based attacks on elliptic curve discrete logarithm problem. In IJCIT: the International Journal of Computer and Information Technology, 2015.

  • Jared Lee, Thomas H. Austin, and Mark Stamp. Compression-based analysis of metamorphic malware. In International Journal of Security and Networks, 2015.

  • Shoma Kajitani, Yasuyuki Nogami, Shunsuke Miyoshi, and Thomas H. Austin. Volunteer computing for solving an elliptic curve discrete logarithm problem. In CANDAR: International Symposium on Computing and Networking, IEEE, 2015.

  • Ashwin Kalbhor, Thomas H. Austin, Eric Filiol, Sébastien Josse, and Mark Stamp. Dueling hidden Markov models for virus analysis. In Journal in Computer Virology and Hacking Techniques, Springer, 2014.

  • Mangesh Musale, Thomas H. Austin, and Mark Stamp. Hunting for metamorphic JavaScript malware. In Journal in Computer Virology and Hacking Techniques, Springer, 2014.

  • Chinmayee Annachhatre, Thomas H. Austin, and Mark Stamp. Hidden Markov models for malware classification. In Journal in Computer Virology and Hacking Techniques, Springer, 2014.

  • Ranjith Kumar Jidigam, Thomas H. Austin, and Mark Stamp. Singular value decomposition and metamorphic detection. In Journal in Computer Virology and Hacking Techniques, Springer, 2014.

  • Sayali Deshpande, Younghee Park, Mark Stamp, “Eigenvalue Analysis for Metamorphic Detection,” in Journal of Computer Virology and Hacking Techniques, Feb. 2014.

  • Younghee Park*, Douglas Reeves, Mark Stamp, “Deriving Common Malware Behavior through Graph Clustering, " The International Journal of Computer and Security, Elsevier, volume 39 November 2013.

  • Teja Tamboli, Thomas H. Austin, and Mark Stamp. Metamorphic code generation from LLVM bytecode. In Journal in Computer Virology and Hacking Techniques, Springer, 2013.

  • Thomas H. Austin, Jean Yang, Cormac Flanagan, and Armando Solar-Lezama. Faceted Execution of Policy-Agnostic Programs. In PLAS: Proceedings of the ACM SIGPLAN Workshop on Programming Languages and Analysis for Security, ACM, 2013.

  • Thomas H. Austin, Eric Filiol, Sébastien Josse, and Mark Stamp. Exploring hidden Markov models for virus analysis: a semantic approach. In HICSS: Hawaii International Conference on System Sciences, 2013.

  • Thomas H. Austin and Cormac Flanagan. Multiple facets for dynamic information flow. In POPL: Proceedings of the 39th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, ACM, 2012.

  • Thomas H. Austin, Cormac Flanagan, and Martín Abadi. A functional view of imperative information flow. In APLAS: 10th Asian Symposium on Programming Languages and Systems, 2012.

  • Younghee Park, Salvatore J. Stolfo, “Software Decoys for Insider Threat ", in the 7th ACM Symposium on Information, Computer and Communications Security(ASIACCS), Seoul, South Korea, May 2012.

  • Thomas H. Austin and Cormac Flanagan. Permissive dynamic information flow analysis. In PLAS: Proceedings of the ACM SIGPLAN Workshop on Programming Languages and Analysis for Security, ACM, 2010.

  • Thomas H. Austin and Cormac Flanagan. Efficient purely-dynamic information flow analysis. In PLAS: Proceedings of the ACM SIGPLAN Workshop on Programming Languages and Analysis for Security, ACM, 2009.

  • Younghee Park, Douglas R. Reeves, “Identification of Bot Commands By Run-time Execution Monitoring ", Proceedings of 25th Annual Computer Security Applications Conference (ACSAC 2009), Hawaii, USA, December 2009.

Blockchain Security

Blockchain-based cryptocurrency replaces centralized institutions with a distributed network of Internet-based miners to generate currency and process financial transactions. Such blockchain systems reach consensus using PoW and PoS. With a decentralized distributed system, it introduces new threats and attacks in the blockchain network, such as DDoS attacks to prevent mining activities, and exploiting blocks for attacks. Also, blockchain miners processing the transactions and generating blocks are rational and incentivized by financial rewards. Prior research in blockchain security studied such financial incentives for the miners and identified attacks based on withholding and delaying block submissions.

 

Related Selected Publications:

  • Arijet Sarker, SangHyun Byun, Wenjun Fan, and Sang-Yoon Chang, Blockchain-Based Root of Trust Management in Security Credential Management System for Vehicular Communications, ACM Symposium on Applied Computing (SAC), 2021, Virtual

  • Wenjun Fan, Hsiang-Jen Hong, Xiaobo Zhou, and Sang-Yoon Chang, A Generic Blockchain Framework for Securing Decentralized Applications, IEEE International Conference on Communications (ICC), 2021, Virtual

  • Sang-Yoon Chang, Share Withholding in Blockchain Mining, Proceedings of EAI International Conference on Security and Privacy in Communication Networks (SecureComm), 2020, Virtual

  • Wenjun Fan, Younghee Park, Shubham Kumar, Priyatham Ganta, Xiaobo Zhou, and Sang-Yoon Chang, Blockchain-Enabled Collaborative Intrusion Detection in Software Defined Networks, Proceedings of IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020, Virtual

  • Wenjun Fan, Shubham Kumar, Vrushali Jadhav, Sang-Yoon Chang, and Younghee Park, A Privacy Preserving E-Voting System based on Blockchain, Silicon Valley Cybersecurity Conference (SVCC), 2020, Virtual

  • Chris Pollett, Thomas H. Austin, Katerina Potika, and Justin Rietz. TontineCoin: murder-based proof-of-stake. In DAPPS: International Conference on Decentralized Applications and Infrastructures, IEEE, 2020.

  • Thomas H. Austin. SpartanGold: A blockchain for education, experimentation, and rapid prototyping. In SVCC: Silicon Valley Cybersecurity Conference, 2020.

  • Yan Chen, Thomas H. Austin, and Philip Heller. BioBlockchain: Useful Proof-of-Work with Multiple Sequence Alignment. In SVCC: Silicon Valley Cybersecurity Conference, 2020.

  • Thomas H. Austin, Paul Merrill, Justin Rietz. SharedWealth: Disincentivizing Mining Pools through Burning and Minting. In BSCT: International Workshop on Blockchain and Smart Contracts Technologies, 2020.

  • Chris Pollett, Thomas H. Austin, Katerina Potika, and Justin Rietz. TontineCoin: Brief Description In FOCODILE International Workshop on Foundations of Consensus and Distributed Ledgers, 2020.

  • Paul Merrill, Thomas H. Austin, Justin Rietz, and Jon Pearce. Ping-pong governance: token locking for enabling blockchain self-governance. In MARBLE: International Conference on Mathematical Research for Blockchain Economy, 2019.

  • Paul Merrill, Thomas H. Austin, Jenil Thakker, Younghee Park, Justin Rietz, "Lock and Load: A Model for Free Blockchain Transactions through Token Locking," IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON), San Fransico USA, April 4-9, 2019.

  • Sang-Yoon Chang, Chang-Wu Chen, Younghee Park, “Uncle-Block Attack: Blockchain Mining Threat Beyond Block Withholding for Rational and Uncooperative Miners, accepted for the 17th International Conference on Applied Cryptography and Network Security, 2019.

  • Sang-Yoon Chang, Younghee Park*, “Silent Timestamping for Blockchain Mining Pool Security,” International Workshop on Computing, Networking and Communications (CNC) in conjunction with International Conference on Computing, Networking and Communications (ICNC 2019), Honolulu, Hawaii, USA, February 18-21, 2019.

  • Jenil Thakker, Ikwhan Chang, Younghee Park*, “Secure Data Management in Internet-of-Things Based on Blockchain,” IEEE International Conference on Consumer Electronics (ICCE), Las Vegas USA Jan., 2020.

  • Gokay Saldamli, Shreyas Babji, Miroslav Grubic, Nischala Raja Shekar, Chaitra Satyanarayana, “Blockchain based Application for Exchange of Left over Foreign Currency,” in the Sixth IEEE International Conference on Software Defined Systems (SDS), 2019.

  • Gokay Saldamli, Sohil Sanjay Mehta, Pranjali Sanjay Raje, Madhuri Shiva Kumar, Sumedh Sunil Deshpande, “Identity Management via Blockchain,” in Proceedings of the International Conference on Security and Management (SAM), 2019.

IOT Security

Internet of Things (IoT) connects billions of sensors, actuators and other internet connected objects which will communicate with each other in the network. The Internet of Things can encompass a large number of diverse end-to-end applications in different domains, such as Health Infrastructure, Smart Grid, and Smart Cities. The Internet of Things (IoT) is emerging as a force to be reckoned with on the internet and in the economy overall. With the growing IoT devices usage, attackers illegitimately access the networked IoT devices and launch various network attacks. Thus, the IoT brings with it a host of potential new security breaches and attacks. There are many security problems for IoT applications and privacy, and IoT networks. Attackers illegitimately access the networked IoT devices and launch various network attacks on the Internet. Due to the unattended nature of the IoT devices with security limitations allowing for devices to be compromised, most IoT attacks originate from millions of compromised devices.

 

Related Selected Publications:

  • Sang-Yoon Chang, Sristi L. S. Kumar, Yih-Chun Hu, Younghee Park, "Power-Positive Networking: Wireless-Charging-Based Networking to Protect Energy Against Battery DoS Attacks," ACM Transaction on Sensor Networks, Vol. 1, No. 1, February 2019.

  • Mihui Kim, Younghee Park*, Pankaj Balasaheb Dighe, “Privacy-Preservation Using Group Signature for Incentive Mechanisms in Mobile Crowd Sensing,” Journal of Information Processing System, October 1, 2019.

  • Gokay Saldamli, Levent Ertaul, Asharani Shankaralingappa, “Analysis of Lightweight Message Authentication Codes for IoT Environments,” in the Fourth IEEE International Conference on Fog and Mobile Edge Computing (FMEC), 2019.

  • Gokay Saldamli, Levent Ertaul, Bharani Kodirangaiah, “Post-Quantum Cryptography on IoT: Merkle's Tree Authentication,” Proceedings of the International Conference on Wireless Networks (ICWN), 2018.

  • Kamran Naseem Kalokhe, Younghee Park*, Sang-Yoon Chang, “Resilient SDN-based Communication in Vehicular Network,” The International Workshop on Security and Privacy Assurance Technologies for Emerging Networks (SPATEN), June 22, 2018 in conjunction with the 13th International Conference on Wireless Algorithms, Systems, and Applications (WASA) June 20-22, 2018.

  • Mason Itkin, Mihui Kim, Younghee Park*, “Development of Cloud-based UAV Monitoring and Management System,” in Journal of Sensors, November 2016.

  • Younghee Park*, Shruti Daftari, Pratik Inamdar, Snehal Salavi, Aniket Savanand, Youngsoo Kim, “IoTGuard: Scalable and Agile Safeguards for Internet of Things,” IEEE MILCOM, November 2016.

  • Levent Ertaul, Johan Hadiwijaya Yang, Gokay Saldamli, “Analyzing homomorphic encryption schemes in securing wireless sensor networks (WSN),” in International Journal of Computer Science and Network Security (IJCSNS), 2015.

  • R. Berthier, D. Urbina, A. Cardenas, M. Guerrero, U. Herberg, J. G. Jetcheva, D. Mashima, J. Huh, and R. Bobba. On the Practicality of Detecting Anomalies with Encrypted Traffic in AMI. Proceedings of the IEEE Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, Nov. 3-6, 2014.

  • A. Cardenas, R. Berthier, R. Bobba, J. Huh, J. G. Jetcheva, D. Grochocki, and William Sanders. A Framework for Evaluating Intrusion Detection Architectures in Advanced Metering Infrastructures. IEEE Transactions on Smart Grid, Vol. 5, Issue 2, pp. 906-915, March 2014.

  • R. Berthier, J. G. Jetcheva, D. Mashima, J. Huh, D. Grochocki, R. Bobba, A. Cardenas, and W. H. Sanders. Reconciling Security Protection and Monitoring Requirements in Advanced Metering Infrastructures. Proceedings of the IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, Canada, Oct. 21-24, 2013.

  • D. Grochocki, J. Huh, R. Berthier, R. Bobba, A. Cardenas, J. G. Jetcheva, and W. Sanders. AMI Threats, Intrusion Detection Requirements and Deployment Recommendations. Proceedings of the Third IEEE International Conference on Smart Grid Communications (SmartGridComm), Tainan City, Taiwan, November 2012.

Big Data Security

Machine/deep learnings attempt to discover hidden, valid patterns and relationships among the attributes in a large dataset. These approaches allow a more flexible and distributable way to identify malicious activities based on limited, incomplete, and nonlinear data sources. Applying machine/deep learning techniques constructs and optimizes detection models automatically, which would eliminate the tedious work of human experts for the data analysis and model.

 

Related Selected Publications:

  • Jiangzhou Deng, Yong Wang, Junpeng Guo, Yongheng Deng, Jerry Gao, Younghee Park, “A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data,” Journal of Information Science, October 2019.

  • Kunal Goswami, Younghee Park*, Chungsik Song, “Impact of Reviewer Social Interaction on Online Consumer Review Fraud Detection,” Journal of Big Data (Springer), May, 2017.

  • Richard Chow, Hongxia Jin, Bart Knijnenburg, Gokay Saldamli, “Differential data analysis for recommender systems,” in Proceedings of the 7th ACM conference on Recommender systems, 2013.

Security Education

Cybersecurity is a unique field, as its development and practice are closely tied with the system/application it is protecting and the experts proactively consider potential vulnerabilities and threats even before they materialize as exploits and attacks. Our research areas include applying the theory and techniques from social science and testing novel mechanisms and approaches to improve the cybersecurity education.

 

Related Selected Publications:

  • Kay Yoon and Sang-Yoon Chang, Teaching Team Collaboration in Cybersecurity: A Case Study from A Transactive Memory Systems Perspective, IEEE Global Engineering Education Conference (EDUCON), 2021, Vienna, Austria.

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