arXiv subjects

Vinod P

Publications and source records attributed to Vinod P.

14 recordsLinked to original sources

arXiv2026

CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors

In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Although databases like CVE and NVD provide detailed technical information, they often lack links to attacker behaviors such as tactics and techniques, limiting effective threat interpretation and response. This work bridges this gap by connecting vulnerabilities with behavioral patterns from the MITRE ATT&CK framework. We construct a CVE-TTP Knowledge Graph that links CVEs to tactics and techniques using classification and relation extraction. Transformer-based models are developed for behavior identification, with CySecBERT achieving macro F1-scores of 87.71% (techniques) and 96.16% (tactics). Also, we created an annotated dataset with 24,820 entities and 43,608 relations for entity and relation extraction. The pipeline-based approach achieves macro F1-scores of 0.86 (entity extraction) and 0.99 (relation extraction), while a span-based joint model achieves 0.78. These outputs are integrated into a Neo4j-based Cyber Threat Knowledge Graph, enabling structured visualization of vulnerabilities.

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arXiv2026

An AI-Based Solution for Secure Service Provisioning in IoT

As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this evolving landscape, securing service provisioning is crucial to ensure the proper functioning, security, and reliability of the IoT ecosystem. Service provisioning encompasses key tasks such as device registration, configuration, authentication, authorization, and software deployment, all of which are essential for seamless and secure IoT operations. In this paper, we present a comprehensive framework designed to select the most suitable smart objects to deliver a target service within a given IoT environment while also monitoring the behavior of the entities involved during the service provisioning phase. To achieve this, we employ a Deep Reinforcement Learning (DRL) approach in which an intelligent agent learns, through interaction with a complex, dynamic environment, how to adapt to changes while adhering to predefined security constraints. For behavioral monitoring, we leverage Federated Learning (FL) to develop a global Behavioral Fingerprinting (BF) model that is fully distributed and can analyze how IoT devices interact within the network. In addition, the BF is used to compute a reliability score for each service provider, reflecting its degree of compliance with the defined security constraints. This score is then incorporated into the service provisioning process, allowing smart objects to select providers not only according to functional suitability but also to their reliability level. Finally, we conduct an extensive experimental evaluation to assess the robustness and scalability of our approach. The results demonstrate that our solution can be effectively deployed even on resource-constrained IoT devices, making it a viable and scalable security-enhancing mechanism for modern IoT ecosystems.

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arXiv2026

Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks

Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address this vulnerability, we propose a certifiably robust malware detection framework based on randomized smoothing through feature ablation and targeted noise injection. During evaluation, our system analyzes an executable by generating multiple ablated variants, classifies them by using a smoothed classifier, and identifies the final label based on the majority vote. By analyzing the top-class voting distribution and the Wilson score interval, we derive a formal certificate that guarantees robustness within a specific radius against feature-space perturbations. We evaluate our approach by comparing the performance of the base classifier and the smoothed classifier on both clean executables and ablated variants generated using PyMetaEngine. Our results demonstrate that the proposed smoothed classifier successfully provides certifiable robustness against metamorphic evasion attacks without requiring modifications to the underlying machine learning architecture.

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arXiv2025

Protecting Deep Neural Network Intellectual Property with Chaos-Based White-Box Watermarking

The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Trained DNNs represent valuable assets, often developed through significant investments. However, the ease with which models can be copied, redistributed, or repurposed highlights the urgent need for effective mechanisms to assert and verify model ownership. In this work, we propose an efficient and resilient white-box watermarking framework that embeds ownership information into the internal parameters of a DNN using chaotic sequences. The watermark is generated using a logistic map, a well-known chaotic function, producing a sequence that is sensitive to its initialization parameters. This sequence is injected into the weights of a chosen intermediate layer without requiring structural modifications to the model or degradation in predictive performance. To validate ownership, we introduce a verification process based on a genetic algorithm that recovers the original chaotic parameters by optimizing the similarity between the extracted and regenerated sequences. The effectiveness of the proposed approach is demonstrated through extensive experiments on image classification tasks using MNIST and CIFAR-10 datasets. The results show that the embedded watermark remains detectable after fine-tuning, with negligible loss in model accuracy. In addition to numerical recovery of the watermark, we perform visual analyses using weight density plots and construct activation-based classifiers to distinguish between original, watermarked, and tampered models. Overall, the proposed method offers a flexible and scalable solution for embedding and verifying model ownership in white-box settings well-suited for real-world scenarios where IP protection is critical.

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arXiv2025

Enhancing Android Malware Detection with Retrieval-Augmented Generation

The widespread use of Android applications has made them a prime target for cyberattacks, significantly increasing the risk of malware that threatens user privacy, security, and device functionality. Effective malware detection is thus critical, with static analysis, dynamic analysis, and Machine Learning being widely used approaches. In this work, we focus on a Machine Learning-based method utilizing static features. We first compiled a dataset of benign and malicious APKs and performed static analysis to extract features such as code structure, permissions, and manifest file content, without executing the apps. Instead of relying solely on raw static features, our system uses an LLM to generate high-level functional descriptions of APKs. To mitigate hallucinations, which are a known vulnerability of LLM, we integrated Retrieval-Augmented Generation (RAG), enabling the LLM to ground its output in relevant context. Using carefully designed prompts, we guide the LLM to produce coherent function summaries, which are then analyzed using a transformer-based model, improving detection accuracy over conventional feature-based methods for malware detection.

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arXiv2025

When Forgetting Triggers Backdoors: A Clean Unlearning Attack

Machine unlearning has emerged as a key component in ensuring ``Right to be Forgotten'', enabling the removal of specific data points from trained models. However, even when the unlearning is performed without poisoning the forget-set (clean unlearning), it can be exploited for stealthy attacks that existing defenses struggle to detect. In this paper, we propose a novel {\em clean} backdoor attack that exploits both the model learning phase and the subsequent unlearning requests. Unlike traditional backdoor methods, during the first phase, our approach injects a weak, distributed malicious signal across multiple classes. The real attack is then activated and amplified by selectively unlearning {\em non-poisoned} samples. This strategy results in a powerful and stealthy novel attack that is hard to detect or mitigate, highlighting critical vulnerabilities in current unlearning mechanisms and highlighting the need for more robust defenses.

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arXiv2025

Security through the Eyes of AI: How Visualization is Shaping Malware Detection

Malware, a persistent cybersecurity threat, increasingly targets interconnected digital systems such as desktop, mobile, and IoT platforms through sophisticated attack vectors. By exploiting these vulnerabilities, attackers compromise the integrity and resilience of modern digital ecosystems. To address this risk, security experts actively employ Machine Learning or Deep Learning-based strategies, integrating static, dynamic, or hybrid approaches to categorize malware instances. Despite their advantages, these methods have inherent drawbacks and malware variants persistently evolve with increased sophistication, necessitating advancements in detection strategies. Visualization-based techniques are emerging as scalable and interpretable solutions for detecting and understanding malicious behaviors across diverse platforms including desktop, mobile, IoT, and distributed systems as well as through analysis of network packet capture files. In this comprehensive survey of more than 100 high-quality research articles, we evaluate existing visualization-based approaches applied to malware detection and classification. As a first contribution, we propose a new all-encompassing framework to study the landscape of visualization-based malware detection techniques. Within this framework, we systematically analyze state-of-the-art approaches across the critical stages of the malware detection pipeline. By analyzing not only the single techniques but also how they are combined to produce the final solution, we shed light on the main challenges in visualization-based approaches and provide insights into the advancements and potential future directions in this critical field.

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arXiv2025

XBreaking: Understanding how LLMs security alignment can be broken

Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and medical institutions. For this reason, commercial LLMs typically undergo a sophisticated censoring mechanism to eliminate any harmful output they could possibly produce. These mechanisms maintain the integrity of LLM alignment by guaranteeing that the models respond safely and ethically. In response to this, attacks on LLMs are a significant threat to such protections, and many previous approaches have already demonstrated their effectiveness across diverse domains. Existing LLM attacks mostly adopt a generate-and-test strategy to craft malicious input. To improve the comprehension of censoring mechanisms and design a targeted attack, we propose an Explainable-AI solution that comparatively analyzes the behavior of censored and uncensored models to derive unique exploitable alignment patterns. Then, we propose XBreaking, a novel approach that exploits these unique patterns to break the security and alignment constraints of LLMs by targeted noise injection. Our thorough experimental campaign returns important insights about the censoring mechanisms and demonstrates the effectiveness and performance of our approach.

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arXiv2025

How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks

As Machine Learning (ML) evolves, the complexity and sophistication of security threats against this paradigm continue to grow as well, threatening data privacy and model integrity. In response, Machine Unlearning (MU) is a recent technology that aims to remove the influence of specific data from a trained model, enabling compliance with privacy regulations and user requests. This can be done for privacy compliance (e.g., GDPR's right to be forgotten) or model refinement. However, the intersection between classical threats in ML and MU remains largely unexplored. In this Systematization of Knowledge (SoK), we provide a structured analysis of security threats in ML and their implications for MU. We analyze four major attack classes, namely, Backdoor Attacks, Membership Inference Attacks (MIA), Adversarial Attacks, and Inversion Attacks, we investigate their impact on MU and propose a novel classification based on how they are usually used in this context. Finally, we identify open challenges, including ethical considerations, and explore promising future research directions, paving the way for future research in secure and privacy-preserving Machine Unlearning.

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arXiv2024

SeCTIS: A Framework to Secure CTI Sharing

The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. As a growing number of organizations include smart, interconnected devices in their systems to automate their processes, the attack surface becomes much bigger, and the complexity and frequency of attacks pose a significant threat. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to leaks of both proprietary and confidential data. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality, and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. An extensive experimental campaign demonstrates our framework's correctness and performance, and the detailed attack model discusses its robustness against attacks in the context of data and model quality.

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arXiv2024

Privacy-Preserving in Blockchain-based Federated Learning Systems

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.

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arXiv2021

GANG-MAM: GAN based enGine for Modifying Android Malware

Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that could produce successful adversarial samples. The interest towards this technology is quickly growing. In this paper, we propose a system that produces a feature vector for making an Android malware strongly evasive and then modify the malicious program accordingly. Such a system could have a twofold contribution: it could be used to generate datasets to validate systems for detecting GAN-based malware and to enlarge the training and testing dataset for making more robust malware classifiers.

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arXiv2020

Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach

The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples to estimate statistical difference in-order to create adversarial examples. Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps). By doing this, the classification algorithm inappropriately fits the discriminant function on the set of data points, eventually yielding a higher misclassification rate. Further, to distinguish the adversarial examples from benign samples, we propose two defense mechanisms to counter attacks. To validate our attacks and solutions, we test our model on three different benchmark datasets. We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix. Promising results show that generated adversarial samples can evade detection with a very high probability. Additionally, evasive variants generated by our attack models when used to harden the developed anti-malware system improves the detection rate up to 50% when using the Generative Adversarial Network (GAN) method.

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arXiv2018

FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.

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