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Found 37 papers, 3 papers with code
Generating Synthetic Time Series Data for Cyber-Physical Systems
Data augmentation is an important facilitator of deep learning applications in the time series domain.
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses.
AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research.
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way.
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems
The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
In this work, we present LOCALINTEL, a novel automated knowledge contextualization system that, upon prompting, retrieves threat reports from the global threat repositories and uses its local knowledge database to contextualize them for a specific organization.
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
Use of Graph Neural Networks in Aiding Defensive Cyber Operations
In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information.
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security.
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths.
Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems.
Impacts and Risk of Generative AI Technology on Cyber Defense
To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense.
REGARD: Rules of EngaGement for Automated cybeR Defense to aid in Intrusion Response
In this paper, to enable this control functionality over an IRS, we create Rules of EngaGement for Automated cybeR Defense (REGARD) system which holds a set of Rules of Engagement (RoE) to protect the managed system according to the instructions provided by the human operator.
Survey of Malware Analysis through Control Flow Graph using Machine Learning
Malware is a significant threat to the security of computer systems and networks which requires sophisticated techniques to analyze the behavior and functionality for detection.
Explainable Intrusion Detection Systems Using Competitive Learning Techniques
Lastly, we analyze the statistical and visual explanations generated by our architecture, and we give a strategy that users could use to help navigate the set of explanations.
AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce
Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust.
TwinExplainer: Explaining Predictions of an Automotive Digital Twin
Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs.
CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense
Critical servers can be secured against distributed denial of service (DDoS) attacks using proof of work (PoW) systems assisted by an Artificial Intelligence (AI) that learns contextual network request patterns.
A White-Box Adversarial Attack Against a Digital Twin
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs).
Designing an Artificial Immune System inspired Intrusion Detection System
The Human Immune System (HIS) works to protect a body from infection, illness, and disease.
CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models
As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks.
Creating an Explainable Intrusion Detection System Using Self Organizing Maps
We leverage SOM's explainability to create both global and local explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
Explainable Artificial Intelligence (XAI)
Intrusion Detection
A Policy Driven AI-Assisted PoW Framework
Proof of Work (PoW) based cyberdefense systems require incoming network requests to expend effort solving an arbitrary mathematical puzzle.
An Intrusion Response System utilizing Deep Q-Networks and System Partitions
Intrusion Response is a relatively new field of research.
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models
We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters.
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
Enabling and Enforcing Social Distancing Measures using Smart City and ITS Infrastructures: A COVID-19 Use Case
Internet of Things is a revolutionary domain that has the caliber to impact our lives and bring significant changes to the world.
Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS
Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its exposure to external adversaries, making it a lucrative attack vector for malicious actors.
Cyber-All-Intel: An AI for Security related Threat Intelligence
It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence.
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried.
Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports
We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph.
Using Deep Neural Networks to Translate Multi-lingual Threat Intelligence
We create a neural network based system that takes in cybersecurity data in a different language and outputs the respective English translation.
Preventing Poisoning Attacks on AI based Threat Intelligence Systems
As AI systems become more ubiquitous, securing them becomes an emerging challenge.
Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses.
Using Data Analytics to Detect Anomalous States in Vehicles
Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them.
