Rishikesh Sahay

Nikhil Kumar Dora, Sumit Kumar Tetarave, Rishikesh Sahay et al.

With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes financial loss and reputation damage to organizations. According to report of Verizon, 36% of all data breaches involved phishing, highlighting the need for intelligent, adaptive, and explainable security mechanisms. This paper examines the efficiency of different machine learning algorithms in phishing detection on heterogeneous phishing datasets that include a publicly available UCI dataset, our generated datasets using tools such as EvilGinx and Zphisher, and AI generated datasets. Moreover, this work incorporates explainable AI (XAI) techniques such as Information Gain, SHAP (SHapley Additive Explanations), and LIME (Local Interpretable Model-Agnostic Explanations) to examine the most influential features impacting classification outcomes. To support practical deployment, this work also incorporates an MCP-based phishing URL detection system that offers real-time URL analysis, feature extraction, confidence-based classification, and AI-assisted security interpretation. The experimental results demonstrate that among classical models the highest accuracy is obtained by Logistic Regression at 92.44%, among ensemble models CatBoost achieved the highest accuracy at 95.01%, among neural network CNN achieved an accuracy of 94.02%, and among transformer-based models, DistilBERT got the highest accuracy at 99.78%

Rishikesh Sahay, Bell Eapen, Weizhi Meng et al.

With frequently evolving Advanced Persistent Threats (APTs) in cyberspace, traditional security solutions approaches have become inadequate for threat hunting for organizations. Moreover, SOC (Security Operation Centers) analysts are often overwhelmed and struggle to analyze the huge volume of logs received from diverse devices in organizations. To address these challenges, we propose an automated and dynamic threat hunting framework for monitoring evolving threats, adapting to changing network conditions, and performing risk-based prioritization for the mitigation of suspicious and malicious traffic. By integrating Agentic AI with Splunk, an established SIEM platform, we developed a unique threat hunting framework. The framework systematically and seamlessly integrates different threat hunting modules together, ranging from traffic ingestion to anomaly assessment using a reconstruction-based autoencoder, deep reinforcement learning (DRL) with two layers for initial triage, and a large language model (LLM) for contextual analysis. We evaluated the framework against a publicly available benchmark dataset, as well as against a simulated dataset. The experimental results show that the framework can effectively adapt to different SOC objectives autonomously and identify suspicious and malicious traffic. The framework enhances operational effectiveness by supporting SOC analysts in their decision-making to block, allow, or monitor network traffic. This study thus enhances cybersecurity and threat hunting literature by presenting the novel threat hunting framework for security decision- making, as well as promoting cumulative research efforts to develop more effective frameworks to battle continuously evolving cyber threats.

Elyson A. De La Cruz, Rishikesh Sahay, Md Rasel Al Mamun

Engineering managers increasingly must decide how to introduce generative artificial intelligence (AI), retrieval-augmented generation, and coding agents into high-risk operational functions without weakening accountability, privacy, cost discipline, or auditability. The central message of this study is that AI-assisted operational decision support should be managed as a governed engineering capability before it is scaled as automation. Security operations centers (SOCs) provide a suitable setting because they combine privileged telemetry, specialist expertise, software repositories, cloud services, and evidence-sensitive decisions. This study uses Kusto Query Language (KQL) and Microsoft Azure security capabilities as a bounded technical instantiation of that broader engineering management problem. KQL is read-only in ordinary query use, but read-only does not mean risk-free: AI-assisted queries can still create privacy, cost, performance, schema-validity, and decision-quality risks through broad scans, sensitive-field exposure, stale intelligence, and misleading interpretations. Using design science research, the study develops a governed AI query-broker artifact that separates AI planning from operational execution through schema-grounded retrieval, approved templates, policy validation, read-only adapters, normalized outputs, auditable agent traces, and engineering review board gates. The contribution is not a new KQL technique, security product, or detection algorithm. Rather, the study contributes a management framework for governing AI-assisted operational decision support in high-risk digital infrastructure by specifying design propositions, role accountability, maturity stages, quality gates, evaluation criteria, and evidence boundaries.

Kwabena Opoku Frempong-Kore, Rishikesh Sahay, Md Rasel Al Mamun et al.

With the widespread use of software systems in critical infrastructures such as hydropower plants has brought many advantages, yet it has exposed these systems to cyber threats. Cyber risk assessment & mitigation is important to identify cyber threats and protect these systems from unwanted incidents. This paper evaluates and compares the two risk assessment methodologies namely Hazard and Operability Study (HAZOP) and BowTie analysis for identifying cyber induced threats in hydropower systems. We selected these two methodologies because they offer a complementary perspective for cyber-safety risk assessment. Each method is first applied in traditional form to identify hazards, barriers, and threat scenarios arising from accidental causes, then extended to examine how findings change under cyber-induced causation. The traditional HAZOP identifies 18 deviations across five control parameters; the cyber extension shows how an adversary can coordinate multiple deviations to produce outcomes that conventional safeguards cannot detect. The BowTie analysis maps preventive and mitigation barriers around a top event; the cyber extension reveals that barriers appearing independently can share network infrastructure a single attacker could compromise, challenging the defense-in-depth assumption. Together, the two methods provide complementary coverage: HAZOP systematically enumerates what can go wrong, while BowTie shows how barriers provide layered protection. The cyber extension applied to both exposes assumptions, independent causes in HAZOP and independent barriers in BowTie, that do not hold against a coordinated adversary. As a result of this study, this paper highlights a practical two-stage approach to adapt established safety methods to identify cybersecurity challenges in hydropower control systems, provides pros and cons of these methodologies, and shows area of applicability.

Jose Rubio-Hernan, Rishikesh Sahay, Luca De Cicco et al.

Cyber-physical technologies are prone to attacks, in addition to faults and failures. The issue of protecting cyber-physical systems should be tackled by jointly addressing security at both cyber and physical domains, in order to promptly detect and mitigate cyber-physical threats. Towards this end, this letter proposes a new architecture combining control-theoretic solutions together with programmable networking techniques to jointly handle crucial threats to cyber-physical systems. The architecture paves the way for new interesting techniques, research directions, and challenges which we discuss in our work.