Search Results for « AI/ML Security »

This paper focuses on supporting AI/ML Security Workers -- professionals involved in the development and deployment of secure AI-enabled software systems. It presents AI/ML Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) framework to enable AI/ML Security Workers intuitively to explore offensive and defensive tactics.

Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface: publishers, brokers, subscribers, topics, schemas, and temporal ordering can each be abused without a single component observing the whole behavior. This paper proposes SECUREVENT, a hybrid AI/ML security-monitoring architecture for distributed event-based systems. The architecture combines traditional protections such as authenticated transport, topic-level authorization, and signed events with online anomaly detection, graph-aware behavioral features, complex-event policy rules, federated learning, and adversarial-ML governance. A deterministic prototype study over synthetic event-stream attacks illustrates how a hybrid AI/CEP monitor can improve recall over static rules while retaining a low false-positive rate. The central claim is not that machine learning replaces cryptographic and access-control mechanisms, but that model-based security monitoring is necessary when event flows, identities, schemas, and timing relationships are too dynamic for static controls alone.

In this case study, we describe the design and assembly of a cyber security testbed at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide agile reconfigurations to facilitate a wide variety of experiments for evaluations of cyber security tools -- particularly those involving AI/ML. In particular, the testbed provides realistic test environments while permitting control and programmatic observations/data collection during the experiments. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized networks. These evaluations will be described as examples providing motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K file samples (benignware and malware) chosen across a range of file types. The second is an evaluation of network intrusion detection systems efficacy in identifying multi-step adversarial campaigns -- involving reconnaissance, penetration and exploitations, lateral movement, etc. -- with varying levels of covertness in a high-volume business network. The scale of each of these challenges requires automation systems to repeat, or simultaneously mirror identical the experiments for each ML tool under test. Providing an array of easy-to-difficult malicious activity for sussing out the true abilities of the AI/ML tools has been a particularly interesting and challenging aspect of designing and executing these challenge events.

Artificial Intelligence (AI) and Machine Learning (ML) are altering the landscape of public safety and security by offering advanced solutions for crime prediction, disaster management, surveillance, cyber security, and law enforcement. These technologies leverage large datasets, powerful algorithms, and real-time analytics to predict potential threats, enhance decision making, and optimize resource allocation. As the world becomes increasingly interconnected, the integration of AI and ML into public safety systems has become essential for preventing crimes, responding efficiently to emergencies, and ensuring the overall well-being of society. One of the significant solicitations of AI and ML in public safety is crime prediction and prevention. Predictive policing systems use historical crime data, socio-economic factors, and environmental variables to identify high risk areas and forecast criminal activities. Machine learning algorithms can notice patterns and anomalies in huge datasets that may go unnoticed by human analysts. For instance, AI-driven analysis of surveillance footage can help identify suspicious behaviors, enabling law enforcement agencies to respond proactively. Moreover, natural language techniques can processing (NLP) monitor social media platforms for potential threats, helping authorities prevent incidents before they occur.

When the next-generation networks in the 6G era are closely coupled with intelligent network orchestration and management, the role of Artificial Intelligence (AI) is immense. In this chapter we give some insights about how to consolidate and solidify the role of AI in securing 6G networks with more research directions.

Abstract: The AI/ML-Based Network Security Application presented in this study provides an intelligent, multi-layered defense system for detecting and mitigating modern cyber threats. By combining traditional network monitoring tools with artificial intelligence (AI) and machine learning (ML) models, the proposed framework achieves proactive and adaptive threat detection. The system integrates Python libraries such as Scapy, python-nmap, and Scikit-learn to perform real-time packet inspection, vulnerability scanning, and phishing classification. Trained ML algorithms, including Random Forest and Logistic Regression, are employed to enhance the accuracy of phishing and anomaly detection tasks. A modular architecture with multi-threaded execution ensures concurrent processing of security functions without compromising graphical responsiveness. Experimental evaluation demonstrates the system’s efficiency in identifying phishing attempts, scanning vulnerabilities, and analyzing live network packets. The proposed model provides a scalable and intelligent platform for automated network protection, bridging the gap between conventional rule-based defenses and adaptive AI-driven security systems. Key words: Network Security, Artificial Intelligence, Machine Learning, Intrusion Detection, Phishing Detection, Packet Analysis, Vulnerability Scanning.

Today’s cyber defense capabilities in many organizations consist of a diversity of tools, products, and solutions, which are very challenging for Security Operations Centre (SOC) teams to manage in current advanced and dyna... | Find, read and cite all the research you need on Tech Science Press

The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automated security testing within DevOps environments, offering a comprehensive approach to identifying, predicting, and mitigating security vulnerabilities in real time. Automated security testing leverages AI and ML algorithms to analyze code, detect anomalies, and predict potential security threats. These technologies enable the continuous monitoring of codebases, allowing for the early identification of vulnerabilities before they are exploited. By incorporating AI-driven security testing into the CI/CD pipeline, organizations can ensure that security is not an afterthought but a continuous process integrated into every stage of the software development lifecycle. AI and ML models can be trained to recognize patterns associated with security risks, such as code injection, unauthorized access, and data leakage. These models continuously learn from new data, improving their accuracy over time and adapting to evolving threats. The dynamic nature of AI-driven security testing makes it particularly suited for DevOps environments, where frequent code changes and updates can introduce new vulnerabilities. Moreover, AI and ML can assist in automating complex tasks, such as threat modeling, risk assessment, and the prioritization of security issues, enabling security teams to focus on higher-order tasks that require human expertise.

PHI data privacy has increasingly become a matter of concern in the era of large public digital repositories of data.This is particularly true in healthcare where data can be misused if traced back to patients, and brings with itself a myriad of possibilities.Digital data in healthcare is a double-edged sword.While on the one hand, digitalization has allowed for a wide variety of advancements, including teleconsultations, easy retrieval and duplication of data for records and development of applications such as machine learning, it has also allowed for the possibility that the personal medical records of a patient can be accessed by a number of individuals.