Aristidis Ilias | University of Patras | Research Excellence Award

Published on by Engineering Scientist

Dr. Aristidis Ilias | Cybersecurity Systems
| Research Excellence Award

University of Patras | Greece

Dr. Aristidis Ilias research centers on strengthening cybersecurity within industrial, cloud, and cyber-physical ecosystems through advanced cryptographic models, secure computation frameworks, and resilient system architectures. A primary contribution lies in enhancing the security of SCADA and industrial control environments by integrating Elliptic Curve Cryptography into the Modbus protocol, enabling strong protection against interception, tampering, and denial-of-service attacks while meeting the performance constraints of real-time operational systems. Further developments address security challenges in microservice-driven data pipelines, focusing on confidentiality, authentication, and integrity in distributed architectures that support critical operations. This includes designing and validating secure communication models that leverage modern cryptographic primitives and Secure Multi-Party Computation (MPC) to ensure privacy-preserving collaborative data processing. Another important research direction advances the fusion of Linear Algebra operations with MPC, examining BLAS-level computational implementations that enable secure matrix operations at scale. These contributions include performance optimization, protocol engineering, and feasibility studies supporting the potential replacement of traditional CBLAS workflows in sensitive analytical contexts. Supported by contributions across cybersecurity projects spanning industrial protection, intelligent transportation, digital transformation, and privacy compliance, the overall research portfolio—reflected in 76 citations, 15 publications, and an h-index of 6-advances secure digital infrastructures and offers scalable, next-generation frameworks for cyber-resilient computing.

Citation Metrics (Scopus)

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76

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15

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6

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Top 5 Publications

Prof. Sharmila S P | Computer Engineering | Editorial Board Member

Published on by Engineering Scientist

Prof. Sharmila S P | Computer Engineering
| Editorial Board Member

Siddaganga Institute of Technology Tumakuru | India

Prof. Sharmila S P the research work focuses on advancing cybersecurity through AI-driven, explainable, and resilient detection mechanisms capable of addressing modern, highly obfuscated threats. Central contributions include the development of memory-forensic-based feature extraction techniques that enhance the transparency and interpretability of obfuscated malware detection models, enabling isolated family distinction and reducing false positives. The work explores multi-class classification frameworks for malware analysis, leveraging machine learning paradigms to identify sophisticated adversarial behaviors across diverse threat categories. Additional research investigates Hidden Markov Model–based intrusion detection, employing a randomized Viterbi algorithm to strengthen anomaly recognition in dynamic network environments. Studies on cyber-attack prediction further analyze prevalent forecasting techniques to improve proactive defense capabilities. Complementary research examines Android malware behavior, distributed ledger applications for secure banking operations, and lightweight authentication mechanisms rooted in keystroke dynamics for user verification. With a strong emphasis on AI, machine learning, GNNs, NLP-driven analysis, reverse engineering, and volatile memory forensics, the overall body of work contributes toward building robust, explainable, and scalable cybersecurity systems capable of safeguarding digital infrastructures against evolving threats in cloud environments, embedded systems, mobile platforms, and large-scale networked ecosystems.

 Profile:  Orcid 

Featured Publications

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Unveiling evasive portable documents with explainable Kolmogorov–Arnold networks resilient to generative adversarial attacks. Applied Soft Computing, 138, 113537. https://doi.org/10.1016/j.asoc.2025.113537

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Leveraging memory forensic features for explainable obfuscated malware detection with isolated family distinction paradigm. Computers and Electrical Engineering, 121, 110107. https://doi.org/10.1016/j.compeleceng.2025.110107