Publications (MOST UPDATED LIST CAN BE FOUND IN THE LISTINGS BELOW)

Journal Papers

1. KP, Muhammed Shafi, Serena Nicolazzo, Antonino Nocera, Vinod P. "How secure is forgetting? linking machine unlearning to machine learning attacks." Neurocomputing (2025): 131971.

2. Sameera, K. M., P. Vinod, and K. A. Rafidha Rehiman. "WeiDetect: Weibull distribution-based defense against poisoning attacks in federated learning for network intrusion detection systems." Journal of Information Security and Applications 95 (2025): 104275.

3. KP, Muhammed Shafi, P. Vinod, Rafidha Rehiman KA, and Alejandro Guerra-Manzanares. "HExNet: Enhancing malware classification through hierarchical CNNs and multi-level feature attribution." Journal of Information Security and Applications 94 (2025): 104207.

4. Arikkat, Dincy R., P. Vinod, KA Rafidha Rehiman, Corrado A. Visaggio, Andrea Di Sorbo, and Mauro Conti. "Discerning reliable cyber threat indicators for timely Cyber Threat Intelligence," Journal of Computer Virology and Hacking Techniques 21, no. 1 (2025): 25.

5. Arikkat, Dincy R., P. Vinod, Rafidha Rehiman KA, Serena Nicolazzo, Marco Arazzi, Antonino Nocera, and Mauro Conti. "Droidttp: Mapping android applications with ttp for cyber threat intelligence." Journal of Information Security and Applications 93 (2025): 104162.

6. Arazzi, Marco, Dincy R. Arikkat, Serena Nicolazzo, Antonino Nocera, Rafidha Rehiman KA, Vinod P., and Mauro Conti. "NLP-based techniques for cyber threat intelligence." Computer Science Review 58 (2025): 100765.

7. Brosolo, Matteo, P. Vinod, and Mauro Conti. "Through the static: Demystifying malware visualization via explainability." Journal of Information Security and Applications 91 (2025): 104063.

8. Sameera, K. M., Arnaldo Sgueglia, P. Vinod, Rafidha Rehiman KA, Corrado Aaron Visaggio, Andrea Di Sorbo, and Mauro Conti. "SecDefender: Detecting low-quality models in multidomain federated learning systems." Future Generation Computer Systems 164 (2025): 107587.

9. Johny, J.A., Asmitha, K.A., Vinod, P., G.Radhamani, Rafidha Rehiman, Mauro Conti,  Deep learning fusion for effective malware detection: leveraging visual features. Cluster Comput 28, 135 (2025).

10. Laudanna, Sonia, Andrea Di Sorbo, P. Vinod, Corrado Aaron Visaggio, and Gerardo Canfora. "Transformer or Autoencoder? Who is the ultimate adversary for attack detectors?." International Journal of Information Security 24, no. 1 (2025): 26.

11. Jacob, S., Vinod, P. and Menon, V.G. (2024), CAPP: Context-Aware Privacy-Preserving Continuous Authentication in Smartphones. Security and Privacy e478. https://doi.org/10.1002/spy2.478

12. Arikkat, D.R., Cihangiroglu, M., Conti, M., KA, R.R., Nicolazzo, S., Nocera, A. and Vinod, P., 2024. SeCTIS: A framework to Secure CTI Sharing. Future Generation Computer Systems, p.107562.

13. Avantika Gaur, Preeti Mishra, Vinod P., Arjun Singh, Vijay Varadharajan, Uday Tupakula, Mauro Conti, vDefender: An explainable and introspection-based approach for identifying emerging malware behaviour at hypervisor-layer in virtualization environment, Computers and Electrical Engineering, Volume 120, Part B, 2024, 109742, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2024.109742

14. Asmitha, K. A., P. Vinod, Rafidha Rehiman KA, Neeraj Raveendran, and Mauro Conti. "Android malware defense through a hybrid multi-modal approach." Journal of Network and Computer Applications (2024): 104035.

15. Sameera K.M., Vinod P., Rafidha Rehiman K.A., Mauro Conti, LFGurad: A defense against label flipping attack in federated learning for vehicular network, Computer Networks, 2024, 110768, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2024.110768.

16. Arikkat, D., Vinod, P., Rafidha Rehiman, K.A. et al. XAITrafficIntell: Interpretable Cyber Threat Intelligence for Darknet Traffic Analysis. J Netw Syst Manage 32, 88 (2024). https://doi.org/10.1007/s10922-024-09842-8

17. Arikkat, Dincy R., P. Vinod, Rafidha Rehiman KA, Serena Nicolazzo, Antonino Nocera, Georgiana Timpau, and Mauro Conti. "OSTIS: A novel Organization-Specific Threat Intelligence System." Computers & Security (2024): 103990.

18. Singh, A., Mishra, P., Vinod, P. et al. SFC-NIDS: a sustainable and explainable flow filtering based concept drift-driven security approach for network introspection. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04444-0 

19. Sameera K.M., Serena Nicolazzo, Marco Arazzi, Antonino Nocera, Rafidha Rehiman K.A., Vinod P., Mauro Conti, “Privacy-preserving in Blockchain-based Federated Learning systems”, Computer Communications (2024) 

20. Asmitha, K. A., Vinod Puthuvath, K. A. Rafidha Rehiman, and S. L. Ananth. "Deep learning vs. adversarial noise: a battle in malware image analysis." Cluster Computing (2024): 1-30. 

21. Verma, Hitesh, Smita Naval, Bala Prakasa Rao Killi, and P. Vinod. "Indoor localization using device sensors: A threat to privacy." Microprocessors and Microsystems (2024): 105041. 

22. Asha, S., P. Vinod, and Varun G. Menon. "A defensive attention mechanism to detect deepfake content across multiple modalities." Multimedia Systems 30, no. 1 (2024): 56. 

23. Jacob, S., Vinod, P., Subramanian, A. and Menon, V.G., 2023. Affect sensing from smartphones through touch and motion contexts. Multimedia Systems, pp.1-15.

24. Jyothish, A., Mathew, A. and Vinod, P., 2023. Effectiveness of machine learning-based android malware detectors against adversarial attacks. Cluster Computing, pp.1-21. 

25. Asha, S., Vinod, P. and Menon, V.G., 2023. A defensive framework for deepfake detection under adversarial settings using temporal and spatial features. International Journal of Information Security, pp.1-12. 

26. Alaeiyan, M., Parsa, S. and Vinod, P., 2023. Sober: Explores for invasive behaviour of malware. Journal of Information Security and Applications, 74, p.103451. 

27. Dhanya, K.A., Vinod, P., Yerima, S.Y., Bashar, A., David, A., Abhiram, T., Antony, A., Shavanas, A.K. and Kumar, G., 2023. Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN. IEEE Systems Journal. 

28. Jacob, S., Puthuvath, V., Akarsh, M., George, J., Joseph, J. and Joy, J., 2023. A smartphone authentication system based on touch gesture dynamics. Concurrency and Computation: Practice and Experience, 35(1), p.e7449. 

29. Kumbalaparambi, T.S., Menon, R., Radhakrishnan, Vinod P., 2023. Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model. Environmental Science and Pollution Research, 30(4), pp.10414-10425.

30.  Anandhi, V., Vinod, P., Menon, V.G. and Aditya, K.M., 2022. Performance evaluation of deep neural network on malware detection: visual feature approach. Cluster Computing, 25(6), pp.4601-4615. 

31. Renjith, G., Vinod, P. and Aji, S., 2022. Evading Machine-Learning-Based Android Malware Detector for IoT Devices. IEEE Systems Journal. 

32. Conti, M., Vinod, P. and Vitella, A., 2022. Obfuscation detection in Android applications using deep learning. Journal of Information Security and Applications, 70, p.103311. 

33. Conti, M., Khandhar, S. and Vinod, P., 2022. A few-shot malware classification approach for unknown family recognition using malware feature visualization. Computers & Security, 122, p.102887. 

34. Renjith, G., Laudanna, S., Aji, S., Visaggio, C.A. and Vinod, P., 2022. GANG-MAM: GAN based enGine for modifying Android malware. SoftwareX, 18, p.100977. 

35. Asha, S. and Vinod, P., 2022. Evaluation of adversarial machine learning tools for securing AI systems. Cluster Computing, pp.1-20. 

36. Vishnu, P.R., Vinod, P. and Yerima, S.Y., 2022. A deep learning approach for classifying vulnerability descriptions using self attention based neural network. Journal of Network and Systems Management, 30, pp.1-27. 

37. Ashik, M., Jyothish, A., Anandaram, S., Vinod, P., Mercaldo, F., Martinelli, F. and Santone, A., 2021. Detection of malicious software by analyzing distinct artifacts using machine learning and deep learning algorithms. Electronics, 10(14), p.1694. 

38. Anandhi, V., Vinod, P. and Menon, V.G., 2021. Malware visualization and detection using DenseNets. Personal and Ubiquitous Computing, pp.1-17. 

39. Anupama, M.L., Vinod, P., Visaggio, C.A., Arya, M.A., Philomina, J., Raphael, R., Pinhero, A., Ajith, K.S. and Mathiyalagan, P., 2022. Detection and robustness evaluation of android malware classifiers. Journal of Computer Virology and Hacking Techniques, 18(3), pp.147-170. 

40. Pinhero, A., Anupama, M.L., Vinod, P., Visaggio, C.A., Aneesh, N., Abhijith, S. and AnanthaKrishnan, S., 2021. Malware detection employed by visualization and deep neural network. Computers & Security, 105, p.102247. 

41. Yerima, S.Y., Alzaylaee, M.K., Shajan, A. and P, V., 2021. Deep learning techniques for android botnet detection. Electronics, 10(4), p.519. 

42. Taheri, R., Javidan, R., Shojafar, M., Vinod, P. and Conti, M., 2020. Can machine learning model with static features be fooled: an adversarial machine learning approach. Cluster computing, 23, pp.3233-3253. 

43. Ananya, A., Aswathy, A., Amal, T.R., Swathy, P.G., Vinod, P. and Mohammad, S., 2020. SysDroid: a dynamic ML-based android malware analyzer using system call traces. Cluster Computing, 23(4), pp.2789-2808. 

44. Alaeiyan, M., Parsa, S., Vinod, P. and Conti, M., 2020. Detection of algorithmically-generated domains: An adversarial machine learning approach. Computer Communications, 160, pp.661-673. 

45. Rajesh, S., Paul, V., Menon, V.G., Jacob, S. and Vinod, P., 2019. Secure brain-to-brain communication with edge computing for assisting post-stroke paralyzed patients. IEEE Internet of Things Journal, 7(4), pp.2531-2538. 

46. Deepa K., Radhamani, G., Vinod, P., Shojafar, M., Kumar, N. and Conti, M., 2019. Identification of Android malware using refined system calls. Concurr. Comput. Pract. Exp, 31, p.e5311. 

47. Vinod, P., Zemmari, A. and Conti, M., 2019. A machine learning based approach to detect malicious android apps using discriminant system calls. Future Generation Computer Systems, 94, pp.333-350. 

Conferences

1. Laudanna, Sonia, Mattia Marino, Andrea Di Sorbo, P. Vinod, Corrado Aaron Visaggio, and Gerardo Canfora. "Behind Enemy Lines: Strengthening Android Malware Detection with Adversarial Training." In The 20th International Conference on Availability, Reliability and Security (ARES 2025), pp. 142-160. Cham: Springer Nature Switzerland, 2025.

2. Sameera, K. M., Abhinav, M.,  Amal, P. P., Abhiram, T. Babu, Abishek, Raj K., Amal, Tomichen, Anaina, P., Vinod, P., Rafidha, Rehiman K. A., Mauro, Conti", "DLShield: A Defense Approach Against Dirty Label Attacks in Heterogeneous Federated Learning", In Security, Privacy, and Applied Cryptography Engineering. SPACE 2024. Lecture Notes in Computer Science, vol 15351. Springer, Cham. https://doi.org/10.1007/978-3-031-80408-3_9, 2025, pp. 129-148

3. Ullah, Ubaid, Sonia Laudanna, P. Vinod, Andrea Di Sorbo, Corrado Aaron Visaggio, and Gerardo Canfora. "Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media." In European Symposium on Research in Computer Security, pp. 208-228. Cham: Springer Nature Switzerland, 2024.

4. Brosolo, Matteo, Vinod Puthuvath, Asmitha Ka, Rafidha Rehiman, and Mauro Conti. "SoK: Visualization-based Malware Detection Techniques." In Proceedings of the 19th International Conference on Availability, Reliability and Security, pp. 1-13. 2024.

5. Arikkat, Dincy R., P. Vinod, Rafidha Rehiman KA, Serena Nicolazzo, Antonino Nocera, and Mauro Conti. "Relation Extraction Techniques in Cyber Threat Intelligence." In International Conference on Applications of Natural Language to Information Systems, pp. 348-363. Cham: Springer Nature Switzerland, 2024.

6. Aravind, P. C., Dincy R. Arikkat, Anupama S. Krishnan, Bahja Tesneem, Aparna Sebastian, Mridul J. Dev, K. R. Aswathy, KA Rafidha Rehiman, and P. Vinod. "CyTIE: Cyber Threat Intelligence Extraction with Named Entity Recognition." In International Conference on Advancements in Smart Computing and Information Security, pp. 163-178. Cham: Springer Nature Switzerland, 2023.

7. Simoni, Marco, Andrea Saracino, Vinod P., and Mauro Conti. "Morse: Bridging the gap in cybersecurity expertise with retrieval augmented generation.", In the Proc. of The 40th ACM/SIGAPP Symposium On Applied Computing, Sicily, Italy, March 31 - April 4, 2025.

8. Attila Mester, Zalan Bodo, Vinod P. and Mauro Conti, Towards a malware family classification model using static call graph instruction visualization, In the Proc. of NSS 2024: 18th International Conference on Network and System Security SocialSec 2024: 10th International Symposium on Security and Privacy in Social Networks and Big Data, Zayed University, Abu Dhabi, UAE, November 20-22, http://nsclab.org/nss-socialsec2024/papers.html

Book Chapters

1. Sameera, K. M., Dincy R. Arikkat, P. Vinod, Rehiman KA Rafidha, Azin Aneez, and Mauro Conti. "Federated Learning: An Overview." Machine Learning, Deep Learning and AI for Cybersecurity (2025): 393.