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Found 30 papers, 4 papers with code
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments
These semantically steered features are used to briefly fine-tune the detection head of the teacher model.
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
Experimental results on the CIC-IDS-2017 dataset show that ODXU outperforms traditional neural models across six evaluation metrics, including classification accuracy and false omission rate.
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
By capturing high-level environmental context such as as darkness, rain, and camera blurring, the VLM guides the model to dynamically adjust modality weights based on the current scene.
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
Deep neural networks (DNNs) are highly susceptible to adversarial examples--subtle, imperceptible perturbations that can lead to incorrect predictions.
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Given that using a grounding model adds its own uncertainty in the pipeline, we apply temperature scaling - a widely accepted parametric calibration technique - to calibrate the grounding model's confidence in the accuracy of generated responses.
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which impact factual accuracy in high-stakes applications such as defense and healthcare.
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
By bridging advanced machine learning techniques and practical cybersecurity needs, PacketCLIP provides a foundation for scalable, efficient, and interpretable solutions to tackle encrypted traffic classification and network intrusion detection challenges in resource-constrained environments.
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
We include six datasets under different simulated weather conditions along with six baseline object-detection models, as well as several test datasets that are a mix of weather conditions that we show have significant differences from the training data.
Probabilistic Foundations for Metacognition via Hybrid-AI
Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems.
Deceptive Sequential Decision-Making via Regularized Policy Optimization
Autonomous systems are increasingly expected to operate in the presence of adversaries, though an adversary may infer sensitive information simply by observing a system, without even needing to interact with it.
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Models (LLMs).
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world applicability, making it difficult for human experts to interpret and understand DRL policies.
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model
This paper introduces "XG-NID," a novel framework that, to the best of our knowledge, is the first to fuse flow-level and packet-level data within a heterogeneous graph structure, offering a comprehensive analysis of network traffic.
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor interpretability.
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models
One key challenge in backdoor attacks against large foundation models is the resource limits.
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.
