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Introduction
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Publications
Publications (16)
Generative Artificial Intelligence (GenAI) is rapidly reshaping the global financial landscape, offering unprecedented opportunities to enhance customer engagement, automate complex workflows, and extract actionable insights from vast financial data. This survey provides an overview of GenAI adoption across the financial ecosystem, examining how ba...
Current malware (malicious software) analysis tools focus on detection and family classification but fail to provide clear and actionable narrative insights into the malignant activity of the malware. Therefore, there is a need for a tool that translates raw malware data into human-readable descriptions. Developing such a tool accelerates incident...
Large Language Models (LLMs) have been used in cybersecurity in many ways, including their recent use as intelligent agent systems for autonomous security analysis. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents across various cybersecurity skill sets. Early attempts to apply...
The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient,...
Advanced Persistent Threat (APT) attribution is a critical task and essential for defensive measure, guiding policy decision, and improving cyber resilience. This research aims to establish a credible connection between APT attack-related malware and the threat groups most likely to be their originators. These malware are usually developed by threa...
The current state of Advanced Persistent Threats (APT) attribution primarily relies on time-consuming manual processes. These include mapping incident artifacts onto threat attribution frameworks and employing expert reasoning to uncover the most likely responsible APT groups. This research aims to assist the threat analyst in the attribution proce...
Understanding the modus operandi of adversaries aids organizations to employ efficient defensive strategies and share intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the thre...
Advanced Persistent Threat (APT) attribution is a critical challenge in cybersecurity and implies the process of accurately identifying the perpetrators behind sophisticated cyber attacks. It can significantly enhance defense mechanisms and inform strategic responses. With the growing prominence of artificial intelligence (AI) and machine learning...
Honeypots serve as a valuable deception technology, enabling security teams to gain insights into the behaviour patterns of attackers and investigate cyber security breaches. However, traditional honeypots prove ineffective against advanced adversaries like APT groups due to their evasion tactics and awareness of typical honeypot solutions. This pa...
In the present cyber landscape, the sophistication level of malware attacks is rising steadily. Advanced Persistent Threats (APT) and other sophisticated attacks employ complex and intelligent malware. Such malware integrates numerous malignant capabilities into a single complex form of malware, known as multipurpose malware. As attacks get more co...
With the proliferation of attacks from various Advanced Persistent Threats (APT) groups, it is essential to comprehend the threat actor’s attack patterns to accelerate threat detection and response. The MITRE ATT&CK framework’s Tactics, Techniques, and Procedures (TTPs) help to decipher attack patterns. The APT reports, published by security firms,...
Although state-of-the-art image-based malware classification models give the best performance, these models fail to consider real-world deployment challenges due to various reasons. We address three such problems through this work: limited dataset problems, imbalanced dataset problems, and lack of model generalizability. We employ a prototypical ne...
The critical infrastructure’s (CI) environment is complex and dynamic in nature. The normal behaviour of physical devices changes due to time-dependent operational features and infrastructure component needs. The sensors capturing the changed device behaviour generates measurements in a different operating range due to the time dependent variation...
The current pandemic situation has increased cyber-attacks drastically worldwide. The attackers are using malware like trojans, spyware, rootkits, worms, ransomware heavily. Ransomware is the most notorious malware, yet we did not have any defensive mechanism to prevent or detect a zero-day attack. Most defensive products in the industry rely on ei...
Ransomware is a program used by an attacker or hacker, that locks or encrypts target files or data. The user or the owner of data cannot access these without the explicit assistance of the attacker. After locking or encrypting, the attacker demands ransom generally in the form of cryptocurrencies to permit user to regain access to the locked data....
Questions
Question (1)
Automated cyber threat attribution is critical for identifying the sources of sophisticated cyberattacks, such as Advanced Persistent Threats (APTs). However, existing systems face challenges, including incomplete data integration, limitations in leveraging behavioral patterns, and inaccuracies in distinguishing between similar attack vectors. This question aims to explore the current capabilities, identify gaps, and discuss how emerging technologies, such as AI and advanced graph-based approaches, can enhance attribution accuracy. Insights gained could help guide future research and development in this critical domain.
