Adversarial Machine Unlearning for Privacy-Preserving Threat Intelligence Sharing

Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications

A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system.

Secure AI Systems Protecting Machine Learning Models from Emerging Cyber Threats

The rapid adoption of artificial intelligence and machine learning across critical sectors has introduced cybersecurity challenges that traditional security frameworks were never designed to address. As machine learning models increasingly shape decision-making in finance, healthcare, autonomous systems, and national security, they have become attractive targets for sophisticated adversaries. This paper examines the evolving threat landscape surrounding AI systems, with particular attention to vulnerabilities that arise at each stage of the machine learning lifecycle, including data poisoning, adversarial manipulation, model extraction, and inference-based privacy attacks. It explores how attackers exploit weaknesses in training data, model architectures, and deployment pipelines to manipulate model behavior or exfiltrate sensitive information, and it reviews the defense strategies that have emerged in response, from adversarial training and robust model design to secure deployment practices and privacy-preserving techniques such as differential privacy, federated learning, and homomorphic encryption. The discussion also situates these technical measures within the broader governance frameworks and secure-by-design principles that organizations are beginning to adopt across the AI development lifecycle. By synthesizing current research and practical defense approaches, this paper provides a comprehensive overview of how machine learning systems can be safeguarded against a threat landscape that continues to evolve as quickly as the technology itself. The overarching conclusion is that securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.

Federated Learning Approaches for Cross-Institutional Threat Detection Without Data Sharing Collaborative ML models for Financial Cybersecurity that Preserve Privacy

The sophistication of cyber threats targeting financial institutions continues to grow, necessitating the need for collaborative intelligence-sharing mechanisms that cross organizational boundaries but with a strict focus on protecting data confidentiality. Conventional centralized machine learning methods involve moving sensitive transactional and behavioral data, which creates unacceptable privacy risks, regulatory conflicts, and competitive vulnerabilities between financial entities. This paper introduces a federated learning framework leveraged in the cross-institutional cyber threat detection scenario, allowing several financial entities to collaboratively learn strong adversarial models while keeping their raw data within their local premises. Our proposed architecture utilizes differential privacy, secure multi-party computation, and homomorphic encryption guaranteeing cryptographic assurances upon model gradient aggregation. The local models are trained on the proprietary datasets of each participating institution, while only privacy-preserved (after applying differential privacy mechanisms) gradient updates will be sent to a secure aggregation server that synthesizes a globally optimized threat detection model. Specifically, the framework combines adversarial robustness strategies to reduce poisoning attacks based on federated training dynamics with Byzantine-fault-tolerant aggregation protocols that preserve model integrity in the presence of malicious participants. Based on our evaluation over simulated, multi-institutional financial environments, we show that the federated approach achieves both threat detection accuracy within 3.2% of centralized baselines while mitigating 100% risk of data exposure. This system has demonstrated the ability to generalize well across heterogeneous data distributions and has successfully detected zero-day fraud patterns, anomalous network intrusions, and insider threats. This work lays the foundation for a practical, scalable, and regulatory-compliant approach for the financial sector to leverage collaborative intelligence on cybersecurity without sacrificing institutional data sovereignty.

SecureFedShield: An Adaptive Privacy-Preserving Federated Defense Framework Against Adversarial Attacks in Financial Fraud Detection

The rapid digital transformation of financial services has significantly increased the volume and complexity of electronic transactions, making automated fraud detection an essential component of modern banking systems. Machine learning (ML) techniques have demonstrated remarkable success in identifying fraudulent activities by learning complex transaction patterns from historical financial data. However, conventional centralized machine learning approaches require organizations to consolidate sensitive customer information into centralized repositories, increasing the risk of privacy breaches, unauthorized access, and regulatory non-compliance with frameworks such as the General Data Protection Regulation (GDPR) and other financial data protection standards [2, 4]. Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables multiple organizations to collaboratively train machine learning models without exchanging raw data [1,4]. Although FL significantly improves data privacy, recent studies have demonstrated that federated learning remains vulnerable to adversarial attacks, including model poisoning, data poisoning, backdoor attacks, membership inference, and gradient inversion attacks, all of which can compromise model integrity and reveal confidential information [7,13]. This paper proposes SecureFedShield, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments. The proposed framework integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture. By combining these complementary mechanisms, SecureFedShield aims to improve resilience against malicious participants while preserving high fraud detection accuracy. The framework is intended to be evaluated using publicly available financial fraud datasets and compared with state-of-the-art federated learning aggregation methods. The proposed architecture provides a practical foundation for deploying secure collaborative machine learning in privacy-sensitive financial institutions.

Adversarial Robustness in ML Models for Detecting Synthetic Identity Fraud in Credit Risk

Synthetic identity fraud represents a growing threat in credit risk management, where attackers create fictitious identities by combining real and fabricated information to bypass traditional detection systems. Machine learning models have demonstrated effectiveness in detecting such fraudulent activities; however, they remain vulnerable to adversarial attacks that can manipulate input data to evade detection. This paper investigates the adversarial robustness of various machine learning models applied to synthetic identity fraud detection in credit risk settings. We simulate diverse adversarial attack strategies on benchmark datasets and evaluate the impact on model performance, highlighting critical vulnerabilities. Furthermore, we propose and assess defense mechanisms, including adversarial training and robust feature engineering, to enhance model resilience. Our results reveal significant trade-offs between accuracy and robustness, underscoring the need for balanced solutions tailored to financial fraud contexts. The study provides actionable insights for practitioners aiming to deploy more secure and trustworthy fraud detection systems, contributing to improved credit risk management in an increasingly adversarial environment.

SecShield: A Privacy-Preserved Federated Learning Model to Detect Zero-Day Malware Attack