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Found 15 papers, 1 papers with code
Real-time Network Intrusion Detection via Decision Transformers
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture
Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability.
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving
We posit that we can use the satisfiability modulo theory (SMT) solvers as deductive reasoning engines to analyze the generated solutions from the LLMs, produce counterexamples when the solutions are incorrect, and provide that feedback to the LLMs exploiting the dialog capability of instruct-trained LLMs.
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems.
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation
The knowledge gained from our study on the adversary's ability to make specific evasive perturbations to different types of malicious packets can help defenders enhance the robustness of their NIDS against evolving adversarial attacks.
Measuring Classification Decision Certainty and Doubt
Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes.
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary.
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System
Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges.
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework
The current approaches are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation.
Cybersecurity Anomaly Detection in Adversarial Environments
Inherent to the IoBT operating environment is the practice of adversarial machine learning, which attempts to circumvent machine learning models.
Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities
Artificial intelligence (AI) and machine learning (ML) have become increasingly vital in the development of novel defense and intelligence capabilities across all domains of warfare.
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually.
Adversarial Machine Learning in Network Intrusion Detection Systems
Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output.
Intelligent Systems Design for Malware Classification Under Adversarial Conditions
The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention.
The Spatially-Conscious Machine Learning Model
Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation.
