Research archive
Papers & publications.
Research spanning signed-network security, causal cyber defense,
trustworthy machine learning, natural language processing, transport
safety, and intelligent public infrastructure.
6publications
2026latest publication
5research themes
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Research themes
Causal cyber defenseMalicious URL detectionSigned-network securityNLP & embeddingsIntelligent infrastructure
How the labels work: “Preprint” means a manuscript
is publicly available before a peer-reviewed venue is recorded.
“Peer reviewed” describes the scholarly evaluation process, while
“conference paper” describes where the work was published.
2026
arXiv preprint · cs.CR · Submitted 19 August 2026 ·
arXiv:2608.19190
SiNMULI: Novel Signed Network Approach for Malicious URL
Identification
Avijit Gayen · Sayan Mondal · Angshuman Jana
SiNMULI models websites as nodes and hyperlinks as directed
trust-or-distrust edges. It applies social-balance reasoning to
infer missing edge signs, then identifies unseen domains through a
51% majority rule over their incoming links.
ProblemBlacklists and static URL features can struggle with new,
fast-changing, or deliberately obfuscated threats.
ApproachA directed signed hyperlink graph with balance-based edge
inference and rule-based node classification.
Reported result99.89% accuracy and 99.80% F1 on a graph of 48,205 nodes and
384,592 directed edges.
arXiv preprint
2026
ACM AsiaCCS ’26 · Bangalore, India · 1–5 June 2026 · pp. 1919–1921
· DOI 10.1145/3779208.3804893
POSTER—CAIRN: Causal Active Inference for Real-Time Cyber Attack
Diagnosis and Response
Sayan Mondal · Avijit Gayen · Angshuman Jana
CAIRN models enterprise telemetry as a probabilistic causal
evidence graph and performs real-time Bayesian inference over
competing attack explanations. When passive evidence is
insufficient, it selects low-risk interventions—such as focused
probes, enhanced logging, decoys, or scoped isolation—to generate
discriminative observations.
ProblemCorrelation-heavy IDS and SIEM workflows generate alerts
without enough causal clarity.
ApproachEvidence graphs, Bayesian diagnosis, GenAI-assisted CTI
extraction, and an online intervention loop.
ContributionAn infer-and-verify defense workflow that reduces false
alarms and confirms attacks faster than a passive IDS baseline
in testbed evaluation.
Peer-reviewed poster paper
2026
ISEC 2026 · Research Papers Track · Short paper · DOI
10.1145/3796563.3796574
EntroURL-Bench: A Feature-Rich Benchmark Dataset for Trustworthy
Malicious URL Detection
Avijit Gayen · Sayan Mondal · Khokan Mondal ·
Angshuman Jana
Malicious-URL classifiers are only as reliable as their evaluation
data. EntroURL-Bench addresses common limitations in public
datasets—including age, shallow lexical representations, and weak
coverage of modern attacks—with a feature-rich benchmark intended
for reproducible, trustworthy evaluation.
ProblemExisting URL datasets can be outdated, narrow, or poorly
suited to modern feature-rich detection.
FocusPhishing, malware, and malicious-web threat analysis using
richer URL representations.
ValueA consistent benchmark for comparing detection methods and
studying model trustworthiness.
Peer-reviewed conference paper
2020
ICCECE 2020 · Conference paper · DOI
10.1109/ICCECE48148.2020.9223092
Global Positioning System Based Automated Railway Level Crossing
Shankha Banerjee · Sayan Mondal · Amartya
Chakraborty · Suvendu Chattaraj
The paper proposes a GPS-based approach for detecting an
approaching train and controlling level-crossing gates
automatically. By avoiding additional trackside detection
hardware, the design aims to reduce vandalism risk, gate-operation
delay, and human coordination errors while improving passenger
safety.
ProblemManual crossings depend on timely communication and
gatekeeper action, creating avoidable risk.
ApproachTrain location from GPS drives automated opening and closing
decisions.
GoalSafer, faster crossing operation with less dedicated wayside
hardware.
Peer-reviewed conference paper
2020
Technical report · March 2020 · DOI not assigned
Some Observations and Problems Explored on Dimensionality
Reduction of Text Documents
Somyajit Chakraborty · Sayan Mondal
An exploratory study of problems encountered while reducing the
dimensionality of text representations during document embedding.
The report records practical observations and discusses possible
solutions before the authors’ broader research proposal.
DomainNatural language processing and document
representation.
QuestionHow can dimensionality be reduced while retaining useful
structure in embedded text?
ContributionA focused analysis of observed failure modes and candidate
remedies.
Technical report
2017
Preprint · DOI 10.13140/RG.2.2.13803.16167
Smart Street Light Control System Using an Algorithm and Image
Processing
Sayan Mondal
A camera-based control concept that adjusts street-light
configuration according to observed road workload or vehicle
density. The work connects image processing with adaptive
illumination to reduce unnecessary energy use while maintaining
road visibility and security.
ProblemStatic public lighting consumes energy regardless of actual
road demand.
ApproachCamera observations and image processing estimate activity
and drive lighting control.
GoalLower municipal energy use without compromising safety.
Preprint
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