Dinil Divakaran

Dr. Dinil Mon Divakaran heads Trustwave Research, the global cyber security R&D unit of Trustwave. He is a senior research lead, working in the cross domains of cyber security and AI (Applied Statistics, Machine/Deep Learning, NLP, Data Mining, etc.). He is also an Adjunct Assistant Professor of School of Computing in NUS. Prior to this, he was the Deputy Head of the Network Security department at the A*STAR Institute for Infocomm Research (I²R). His research experience cuts across both industry and academia. He previously held faculty position at the Indian Institute of Technology (IIT) Mandi. He carried out his doctoral studies at the INRIA lab in ENS Lyon in France, in collaboration with Bell Labs. He holds a Master degree in Computer Science and Engineering from IIT Madras, India.

Research experience and interests:

Network, system and web security:

• Phishing (detection, evasion and counter-evasion)
• Large-scale security log analysis, in particular, of SIEM and EDR data (in other words, model development for threat investigation and threat hunting)
• Anomaly/attack detection in networks of different scale and size (ISP, enterprises, home consumers, IoTs, etc.)
• DDoS attacks (vulnerability exploitation, detection, mitigation and defense)
• Network forensics (detection of attack patterns, IP traceback, etc.)
• Programmable data planes (P4), SDN, NFV, etc.
• Broadly QoS/QoE: Queueing, scheduling, bandwidth allocation (of packets, flows, sessions)

Recent research highlights:

• Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Kian Hsiang Low, and Mun Choon Chan, “Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten,” in 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS), 2022.
• Ruofan Liu, Yun Lin, X. Yang, S. H. Ng, Dinil Mon Divakaran, and Jin Song Dong, “Inferring Phishing Intention via Webpage Appearance and Dynamics: A Deep Vision Based Approach,” in USENIX Security Symposium, 2022.
• Yun Lin, Ruofan Liu, Dinil Mon Divakaran, Jun Yang Ng, Qing Zhou Chan, Yiwen Lu, Yuxuan Si, Fan Zhang, and Jin Song Dong, “Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages,” in USENIX Security Symposium, 2021
• Jehyun Lee, Farren Tang, Pingxiao Ye, Fahim Abbasi, Phil Hay, and Dinil Mon Divakaran, “D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System,” in IEEE European Symposium on Security and Privacy (IEEE EuroS&P), 2021
• Jehyun Lee, Pingxiao Ye, Ruofan Liu, Dinil Mon Divakaran, and Chan Mun Choon, “Building robust phishing detection system: an empirical analysis,” in NDSS MADWeb, Feb. 2020
• Levente Csikor, Himanshu Singh, Min Suk Kang, and Dinil Mon Divakaran, “Privacy of DNS-over-HTTPS: Requiem for a Dream?,” in IEEE European Symposium on Security and Privacy (IEEE EuroS&P), 2021
• Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat, Gareth W. Peters, and Mohan Gurusamy, “Cost-aware Feature Selection for IoT Device Classification,” IEEE Internet of Things Journal, 2021
• Kushan S. K. Liyanage, Dinil Mon Divakaran, Rhishi Pratap Singh, and Mohan Gurusamy, “ADEPT: Detection and Identification of Correlated Attack-Stages in IoT Networks,” IEEE Internet of Things Journal, 2021
• Vijayanand Thangavelu, Dinil Mon Divakaran, Rishi Sairam, Suman Sankar Bhunia, and Mohan Gurusamy, “DEFT: A Distributed IoT Fingerprinting Technique,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 940–952, Feb 2019
• Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low, and Mun Choon Chan, “GEE: a gradient-based explainable variational autoencoder for network anomaly detection,” in IEEE CNS, June 2019
• Ido Nevat, Dinil Mon Divakaran, Sai Ganesh Nagarajan, Pengfei Zhang, Su Le, Ko Li Ling, and Vrizlynn Thing, “Anomaly Detection and Attribution in Networks with Temporally Correlated Traffic,” IEEE/ACM Transactions on Networking, 2018
• Dinil Mon Divakaran, Fok Kar Wai, Ido Nevat, and Vrizlynn Thing, “Evidence Gathering for Network Security and Forensics,” in Digital Investigation, vol. 20, Supplement, pp. S56 – S65, 2017