جست‌وجوی استاد و پژوهشگر

Sajal Bhatiaمشاهده پروفایل

دانشیار

• Network Security
• Cyber-Physical System Security
• Denial-of-Service Attacks
• +۹ مورد دیگر

Dr. Sajal Bhatia is an Associate Professor and Director of Cybersecurity Programs within the School of Computer Science & Engineering at Sacred Heart University, CT. His expertise spans network security, cyber-physical systems, and cybersecurity education. Ph.D. in Information Security (2013), Queensland University of Technology B.Tech in Communication & Computer Engineering (2008), LNM Institute of Information Technology Research focuses on: Network Security (wired/wireless DDoS attacks, intrusion detection) Cybersecurity Education (community college curricula, problem-based learning) Industrial Control Systems (ICS) and IoT security AI-driven resilience in critical infrastructure Recent publications address DDoS mitigation in smart grids, IoT botnet detection, and educational frameworks. Notable projects include NSF-funded CyberTraining and Protect Our Power grants. Scientific recognition: SHU Tenure and Promotion (2023) Teaching responsibilities include network security, penetration testing, and cybersecurity seminars. Previously held postdoctoral positions at Fordham University and Vanderbilt University's Institute for Software Integrated Systems.

Lu Yuمشاهده پروفایل

مدرس

• Cybersecurity
• Privacy-preserving Data Mining
• Blockchain Technology
• +۴ مورد دیگر

Lu Yu is a Lecturer in the Department of Electrical and Computer Engineering at Clemson University. She holds a Ph.D. in Electrical Engineering from Clemson University (2012) and dual B.S. degrees from Xi’an Jiaotong University in Control Theory and Control Engineering (2007) and Information Engineering (2004). Her office is located in 300B Riggs Hall, and she can be contacted at 864.656.5916 or via email. Research Focus: Dr. Yu's work centers on cybersecurity with specialized expertise in: Blockchain consensus algorithms (e.g., lightweight mining for clinical trials/supply chains) Privacy-preserving data collection techniques that avoid PII Botnet detection (DGA) and DDoS mitigation Anonymity networks and Markov decision processes Teaching: She instructs undergraduate courses in System Programming Concepts (ECE2220) and Communications Systems (ECE4270), and graduate courses including: Computer Network Security (ECE4490/6490) Malware Reverse Engineering (ECE8830) Distributed Denial-of-Service Attacks (ECE8860) No scientific awards, grants, student advisements, lab affiliations, or team collaborations are mentioned in the source material.

Frank Comeauمشاهده پروفایل

دانشیار

• Wireless Sensor Networks
• Energy Efficiency
• Underwater Communication
• +۳ مورد دیگر

Frank Comeau serves as Associate Professor and Chair of the Engineering Department at St. Francis Xavier University. He earned an engineering diploma from Université de Moncton, a B.Eng. from the Technical University of Nova Scotia (now Dalhousie University), and MASc/PhD degrees from Dalhousie University. His research focuses on energy-efficient algorithms for wireless sensor networks, specializing in terrestrial and underwater environments. Key areas include: Routing protocols for void avoidance in underwater networks Swarm intelligence for mobility-assisted localization Security hardening using adversarial machine learning Cluster-based energy conservation techniques His publications (2006-2022) demonstrate strong trends in energy optimization, underwater network protocols, and sensor localization, with recent work expanding into AI security applications. Awards and grants include: Four University Research, Publications and Teaching Awards (URPTA: 2023, 2021, 2020, 2018) Two Nova Scotia Productivity and Innovation Vouchers Two NRC IRAP SME grants As department chair, he oversees academic programs and research initiatives in engineering, though specific laboratory or student advising details are not provided.

Brendan D Saltaformaggioمشاهده پروفایل

دانشیار

• Computer Systems Security
• Cyber Forensics
• Vetting of Untrusted Software
• +۳ مورد دیگر

Brendan Saltaformaggio is an Associate Professor at Georgia Institute of Technology , with joint appointments in the School of Cybersecurity and Privacy and School of Electrical and Computer Engineering , and a courtesy appointment in the School of Computer Science . His research focuses on computer systems security , cyber forensics , and vetting untrusted software , particularly in mobile and IoT environments . Education : B.S. with Honors in Computer Science from University of New Orleans (2012) M.S. (2014) and Ph.D. (2016) in Computer Science from Purdue University Recipient of ACM SIGSAC Doctoral Dissertation Award (2017), Symantec Research Labs Graduate Fellowship (2016), and Inaugural Emil Stefanov Memorial Fellowship (2016) Research Interests include binary analysis , instrumentation , memory forensics , and mobile/IoT security . His work has led to 15+ publications at top venues like USENIX Security , IEEE S&P , and ACM CCS , with real-world impact such as the removal of hundreds of malicious packages from repositories and collaboration with the US FBI . Scientific Awards include Cisco Systems University Research Program Gift (2021) CSAW Applied Research Competition Award Finalist (2019, 2022) Best Paper Awards at USENIX Security 2014 and ACM CCS 2015 CREATE-X Golden Ticket Award (2019) Class of 1969 Teaching Fellowship (2020) He directs the Cyber Forensics Innovation (CyFI) Laboratory , dedicated to advancing cybercrime investigation and malware prevention in mobile and IoT environments . The lab's work has been recognized in global media outlets like Gizmodo , MSN , and ScienceDaily .

• Database Systems
• Privacy-Preserving Data Processing
• Cybersecurity
• +۵ مورد دیگر

Nicholas L Farnan is a Lecturer in the Department of Computer Science at the University of Pittsburgh. He teaches courses such as Algorithms & Data Structures 2 (CS1501) and Programming Languages for Web Applications (CS1520). Research Interests : His work focuses on privacy-preserving database systems, including preference-aware query optimization, distributed query processing, and intensional access control. He has also contributed to cybersecurity research in bot detection and DDoS mitigation. Key Research Trends : His publications demonstrate a trajectory of developing privacy-aware database technologies (2010–2015) and earlier work on network security mechanisms (2008–2009). Contact: Email nlf4@pitt.edu | Office: 6313 Sennott Square Building, Pittsburgh, PA 15260 | Phone: 1-412-624-8401

• Cybersecurity
• AI-driven malware detection
• Adversarial machine learning
• +۴ مورد دیگر

Rahul Yumlembam is a Research Fellow at Northumbria University's Computer and Information Sciences Department. His research focuses on AI-driven cybersecurity solutions, including ransomware detection, adversarial machine learning, and graph neural networks. He completed his PhD in 2024 with a thesis titled Enhancing Malware Mitigation using Graph Neural Networks, Adversarial Retraining and Conformal Prediction , supervised by Dr. B. Issac and Dr. L. Yang. His work emphasizes explainable AI models and uncertainty quantification, with applications in IoT security, mobile malware analysis, and EEG-based robotics control. Recent publications address botnet detection, adversarial attack mitigation, and conformal prediction frameworks. No scientific awards are listed. His academic contributions include interdisciplinary research at the intersection of cybersecurity and biomedical engineering.

Ryan Van Antwerpمشاهده پروفایل

استادیار مدعو

• Cybersecurity
• Malware Analysis
• Reverse Engineering
• +۴ مورد دیگر

Ryan Van Antwerp is an Adjunct Assistant Professor at the University of Delaware's Department of Electrical and Computer Engineering, where he teaches Reverse Engineering and Penetration Testing. He holds a Ph.D. in Electrical and Computer Engineering from the University of Delaware (2013), specializing in malware analysis and reverse engineering. Currently, he works at Comcast Cable in the Customer Protection department, focusing on strategic initiatives to safeguard over 22 million residential Internet subscribers from cyber threats. His research interests span cybersecurity, including malware analysis, botnet identification, fraud detection systems, and network security. Notable projects include large-scale botnet remediation strategies and detection methods for compromised devices. His work emphasizes practical applications of reverse engineering to distinguish malicious software from benign code. Recent publications (2021–2025) focus on combating fraud through advanced user identification systems, detecting rogue hotspots, and identifying compromised devices in networks. Though no specific awards are cited, his contributions to both academia and industry reflect impactful cybersecurity research. Advising/Grants: No graduate students listed. His industry role at Comcast provides applied research opportunities rather than traditional academic grants. He is part of the Customer Protection team, which develops proactive defense strategies against evolving cyber threats. Labs/Teams: Engaged in Comcast's cybersecurity initiatives, though no specific academic lab affiliations are mentioned.

Guanhua Yanمشاهده پروفایل

دانشیار

• Cybersecurity
• Big Data
• Networking
• +۱ مورد دیگر

Guanhua Yan is an Associate Professor in the School of Computing at Binghamton University. He holds a PhD in Computer Science from Dartmouth College (2005) and previously worked at Los Alamos National Laboratory for nine years as a Research Associate and Research Scientist. His research focuses on cybersecurity, big data, networking, and modeling/simulation, with notable contributions to 5G network security, malware classification using AI, and adversarial machine learning. Education: PhD in Computer Science, Dartmouth College, 2005 Research Interests: Cybersecurity: Including 5G security, malware detection, and adversarial attacks Big Data: Anomaly detection and large-scale workflow analysis Networking: IoT protocols, SDN/NFV, and telecom infrastructure Modeling & Simulation: Network behavior modeling and vulnerability testing Recent work emphasizes proactive defenses against evolving cyber threats, such as 5G core network hardening and developing explainable AI for malware classification. His publications span over two decades, addressing topics ranging from Bluetooth worm propagation to modern 5G vulnerability exploitation. No scientific awards or grants are explicitly mentioned in the provided text. He advises no listed students, though his research likely involves graduate-level collaboration.

• Anonymous Communication
• Cybersecurity
• Network Security
• +۳ مورد دیگر

Nikita Borisov is a Professor at the University of Illinois, affiliated with the Siebel School of Computing and Data Science, Electrical and Computer Engineering department, the Information Trust Institute, and the Coordinated Science Lab. His research focuses on cybersecurity, anonymous communication, privacy-preserving technologies, and secure distributed systems. He has contributed to defenses against network adversaries, secure multiparty computation, and AI security. Borisov has received prestigious awards including the NSF CAREER Award (2010) and the HP Labs Innovation Research Program Award (2010). His work spans theoretical frameworks and practical implementations, addressing challenges in Tor network optimization, cryptocurrency security, and privacy in mobile devices. He actively collaborates on projects like DiProber for relay capacity estimation and ProbFlow for probabilistic programming in anonymous networks. His research outputs include over 90 publications, with notable contributions in anonymous communication systems, sensor-based privacy attacks, and blockchain applications. He leads initiatives in privacy-preserving protocols and has advised on critical infrastructure security, reflecting his commitment to advancing both academic and applied cybersecurity solutions.

Kirill Levchenkoمشاهده پروفایل

دانشیار

• Cyber-physical system security
• Network security
• e-crime
• +۴ مورد دیگر

Kirill Levchenko is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign, previously at UC San Diego. He leads research in cyber-physical system security, network security, and e-crime analysis. His work focuses on evidence-based studies of cybersecurity threats, including aviation cybersecurity and embedded system vulnerabilities. Research Interests: Cyber-physical systems, network security, e-crime, Internet service abuse, and aviation cybersecurity. His major projects include the PacketLab network measurement system and aviation cybersecurity analysis. Key contributions span malware ecosystems, ransomware tracking, and healthcare privacy breaches. Teaching includes courses like Computer Security I (ECE 422/CS 461), Computer Systems Engineering (ECE 391), and introductory computing courses. He advises Ph.D. and M.S. students in security-related research. Labs/Teams: Based in the Coordinated Science Lab (CSL 458), he collaborates on projects like the Triton avionics testbed and PacketLab measurement tools. His work integrates hardware-software co-design for embedded security and network programmability.

Stephen Herwigمشاهده پروفایل

استادیار

• Systems Security
• Cybersecurity
• Distributed Systems
• +۴ مورد دیگر

Stephen Herwig is an Assistant Professor in the Department of Computer Science at William & Mary. His research focuses on systems security, particularly protecting applications in untrusted environments such as the cloud. He explores extending operating systems and distributed systems using trusted hardware and applied cryptography. Herwig earned his PhD from the University of Maryland under the guidance of Dave Levin. Education: PhD in Computer Science, University of Maryland (Advisor: Dave Levin) Research Interests: Trusted Execution Environments Secure Distributed Systems Cloud and IoT Security Sandboxing Techniques Privacy-Preserving Networks Key Contributions: Developed the secmodel_sandbox kernel module for NetBSD Designed Bento for Tor network function virtualization Studied the Hajime IoT botnet Awards: Best Presentation Award at EuroSec 2025 for Mazu architecture Advising & Grants: Actively recruiting self-motivated students interested in secure systems development. Emphasizes low-level software expertise and proactive communication. Labs/Projects: Maintains the secmodel_sandbox open-source project on GitHub, focusing on dynamic kernel security policies for NetBSD.

Marco Deseriisمشاهده پروفایل

دانشیار

• Digital Democracy
• Technopolitics
• Hacktivism
• +۴ مورد دیگر

Marco Deseriis is an Associate Professor in the Faculty of Social and Political Sciences at the Scuola Normale Superiore in Florence, Italy. His research integrates media studies, democratic theory, and social movement studies to analyze digital platforms, AI systems, and their impact on political participation and representation. Previously, he was an Assistant Professor at Northeastern University (Boston) and held postdoctoral fellowships at New York University and Eugene Lang College (New York). He earned his PhD from New York University in 2011. Research Interests: Digital democracy, technopolitics, hacktivism, social movement media, and movement parties. His work explores how digital technologies reshape political organization, activism, and governance. Publications: Authored monographs include Piattaforme e Partecipazione Politica (2024), Improper Names: Collective Pseudonyms from the Luddites to Anonymous (2015), and Net.Art: L’arte della Connessione (2008). Over 20 peer-reviewed articles address climate activism, liquid democracy, digital parties, and hacktivism. Awards & Grants: European Commission’s Marie Curie Fellowship (2016–18), Andrew Mellon Postdoctoral Fellowship (2011–12). Key Contributions: Pioneered analysis of improper names in collective pseudonyms, digital party typologies, and the intersection of technology and political representation. His work bridges theoretical frameworks with empirical studies of modern social movements and political organizations.

• Cybersecurity
• Machine Learning
• Network Security
• +۳ مورد دیگر

Lorenzo Fernandez Maimo is an Associate Professor at the University of Murcia, affiliated with the Faculty of Informatics and the Department of Computer Engineering and Technology. His research focuses on cybersecurity, machine learning, and network security, with a particular emphasis on detecting botnets and ransomware in data networks. He is part of the Mobile Computing and Computer Vision research group and has previously contributed to groups such as Applied Engineering Architecture and Parallel Computing. He holds a Ph.D. from the University of Murcia, awarded in 2019 for his thesis titled Detección de botnets y ransomware en redes de datos mediante técnicas de aprendizaje automático , supervised by Dr. Félix J. García Clemente. Contact: lfmaimo@um.es

Oluwatobi Fajanaمشاهده پروفایل

مدرس

• Data Mining
• Cybersecurity
• Network Analysis
• +۲ مورد دیگر

Oluwatobi Fajana is a Lecturer at the University of Portsmouth, affiliated with the Faculty of Technology and School of Computing. His research focuses on data mining, cybersecurity, and network analysis. He has published works on text mining for crowdfunding analytics and Tor botnet detection. No awards or grants are explicitly mentioned in the provided text. Research interests span computational methods applied to market trends and cybersecurity challenges. Recent publications emphasize leveraging machine learning for network security and product innovation analysis. No student advisees or laboratory affiliations are listed in the current data.

• Machine Learning
• Optimization Metaheuristics
• Cloud Computing
• +۹ مورد دیگر

Ivana Štrumberger is a Researcher at the Faculty of Informatics and Computing at Singidunum University in Belgrade, Serbia. Currently active in academia, she focuses on applying optimization metaheuristics to machine learning problems across diverse domains. Academic affiliation: Faculty of Informatics and Computing, Singidunum University Email: istrumberger@singidunum.ac.rs Research Focus Her work spans machine learning optimization, neural network tuning, and cloud computing applications. Key research areas include: Metaheuristic algorithms for deep learning (LSTM, GRU, CNNs) Feature selection and dimensionality reduction Cybersecurity applications (phishing detection, network intrusion) Financial forecasting (cryptocurrencies, oil prices) Environmental modeling (PAHs pollution, solar power prediction) Publication Trends Recent publications analyze: Hybrid models combining metaheuristics with XGBoost and ELM Applications of sine-cosine algorithm and Harris hawks optimization Time series prediction in energy and financial domains Deep learning for pandemic-related tasks (Covid-19 detection) Cloud computing resource scheduling solutions