Framework detects cyber threats from Twitter tweets.
problem Time-consuming manual extraction of cyber threat intelligence.
method Novelty detection model trained on CVE data.
result F1-score of 0.643 for classifying cyber threat tweets.
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48 results for “Threat Model”
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learningcs.LG
Enhanced network threat detection using KG, LLM, and imbalanced learning.
problem Challenges in network threat detection due to complex attack patterns and limited historical data.
method Integrated framework combining Knowledge Graph, Imbalanced Learning, and Large Language Model.
result Improved threat capture rate by 3%-4% and increased interpretability of risk predictions.
We improve image perturbation defenses using a better-defined Wasserstein threat model.
problem Real-world image perturbations are not pixel-independent, unlike threat models.
method We rectify flaws in the Wasserstein threat model and explore stronger attacks and defenses.
result Current Wasserstein-robust models are ineffective against real-world perturbations.
New method defends against unseen threat models using perceptual adversarial training.
problem Lack of precise mathematical characterization of human perception in adversarial attacks.
method Adversarial training against the set of all imperceptible adversarial examples approximated by deep neural networks.
result Perceptual Adversarial Training (PAT) achieves state-of-the-art robustness against multiple diverse adversarial attacks.
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streamscs.NE
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective fi…
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
This article reviews recent advances in secure distributed and decentralized inference and learning against Byzantine threats.
problem Securing distributed and decentralized inference and learning against malicious attacks.
method Review of recent algorithmic approaches under Byzantine threat model.
result A plethora of robust algorithmic approaches have been developed.
This paper uses deep learning to improve network threat detection in finance.
problem Detecting unknown threats in large-scale data applications.
method Uses deep learning for advanced threat detection.
result Improves protective measures in the financial industry.
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full …
New snooping attacks exploit deep RL without access to environment.
problem Security vulnerabilities in deep reinforcement learning.
method Proposes snooping threat models and attacks on RL agents.
result Adversaries can launch attacks without interacting with the environment.
Study defenses against data poisoning attacks in online learning.
problem Data poisoning attacks on machine learning models.
method Rigorous study of four standard defenses in both a powerful and a realistic threat model.
result The effectiveness of defenses depends on the ease of the learning problem.
CCAT improves model robustness to various adversarial attacks.
problem Robustness to adversarial attacks does not generalize to unseen threat models.
method CCAT biases models towards low confidence predictions on adversarial examples.
result CCAT increases robustness against multiple adversarial attack norms and types.
Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inferencecs.LG
This paper explores security threats in ML systems and proposes mitigation techniques.
problem Security vulnerabilities in ML-based systems during training and inference.
method Overview of security threats, demonstrations using LeNet and VGGNet, proposed attack.
result Demonstrated security threats and proposed mitigation techniques.
Most of the existing solutions to enterprise threat management are preventive approaches prescribing means to prevent policy violations with varying degrees of success. In this paper we consider the complementary scenario where a number of security violations have already occurred, or security threats, or vulnerabiliti…
TinyML models detect RF and cyber threats in spacecraft with low latency.
problem Detecting cyber-RF threats in autonomous spacecraft with low latency.
method Analysis of classical models (RF, LR, SVM, MLP) for latency-accuracy trade-offs.
result Logistic Regression achieves microsecond-level inference with minimal accuracy loss.
Bayesian model assesses criminal threat escalation.
problem Evaluate threats posed by potential violent criminals.
method Customised three-level Bayesian hierarchical model.
result Model calibrates to expert judgments and updates in real time.
Survey on security and privacy in decentralized federated learning.
problem New threats in decentralized federated learning due to the removal of the server.
method Thorough security analysis and overview of defense mechanisms.
result Challenges and threats in decentralized federated learning.
This study improves detection of insider threats using machine learning.
problem Detecting insider threats with anticipation and accuracy.
method Empirically tested 88 machine learning algorithms on CERT dataset, focusing on employee sentiment.
result Random forest algorithms offer the best overall performance, with high accuracy and interpretable rules.
Survey on threats to federated learning models.
problem Vulnerabilities in federated learning protocols.
method Taxonomy of threat models and attacks.
result Important future research directions.
ADSAGE detects anomalies in graph edge sequences for insider threat detection.
problem Detecting insider threats in fine-grained audit logs using graph and text features.
method Anomaly detection at edge level, supporting numeric, categorical, and text attributes.
result ADSAGE detects anomalies in authentications and email communications effectively.
AnyThreat detects insider threats with minimal false positives.
problem High false positives in detecting insider threats.
method Opportunistic knowledge discovery system with four components: feature engineering, oversampling, class decomposition, and classification.
result Detects 87.5% of malicious insider threats with minimal false positives.
This work introduces a new threat model for adversarial attacks using perceptual metrics.
problem Insufficient threat models for capturing imperceivable adversarial examples.
method Leverage quantitative perceptual metrics (LPIPS, SSIM) to define a novel threat model.
result Combined attacks retain perceptual distortion but induce higher misclassification rates.
Evaluates SHIELD's effectiveness against adaptive adversaries in various threat models.
problem Evaluating SHIELD's efficacy against adaptive adversaries in different threat models.
method Empirical analysis of SHIELD's robustness against adaptive attacks using Projected Gradient Descent (PGD) attacks in various threat models (white-box, gray-box).
result The targeted PGD attack success rate drops from 64.3% to 48.9% when models are trained from scratch instead of retrained.
Bayesian system helps identify and thwart terrorist plans.
problem Identifying and thwarting terrorist plans before they occur.
method Develops a Bayesian decision support system to integrate member and group activities.
result Estimates the combined threat posed by a terrorist group.
A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a …
Temporal threat model defends against data poisoning with timestamps.
problem Adversaries can poison more samples than expected, rendering existing defenses ineffective.
method Leverage timestamps to define earliness and duration metrics for temporal robustness.
result Temporal aggregation provides provable temporal robustness against data poisoning.
Defense against Wasserstein adversarial attacks using randomized smoothing.
problem Certified robustness against Wasserstein adversarial attacks.
method Randomized smoothing applied to the space of flows between images, bounding Wasserstein distance by L_1 distance.
result Significantly improved accuracy under Wasserstein adversarial attacks compared to unprotected models.
Research creates a taxonomy to bridge AI security and regulatory gaps.
problem Disciplinary disconnect between technical and legal teams in AI risk assessment.
method Developed an AI System Threat Vector Taxonomy with 9 domains and 53 sub-threats.
result Empirically validated and aligned with ISO/IEC 42001 controls and NIST AI RMF functions.
We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models…
Localized uncertainty attacks target uncertain regions to create imperceptible adversarial examples.
problem Adversarial examples that are imperceptible to humans and strong under deterministic classifiers.
method Localized uncertainty attacks by perturbing uncertain regions, using predictive uncertainty or surrogate models.
result Localized uncertainty attacks produce strong adversarial examples that retain input similarity.
New approach detects and ranks novel and developing cyber threats in Twitter.
problem Detecting and ranking novel and developing cyber threats in Twitter streams.
method Unsupervised machine learning approach focusing on novelty and trendiness.
result Ranking of cyber threat events based on importance score using extracted terms.
New method protects whistleblowers from retaliation by ensuring their reports remain private.
problem Whistleblowers face retaliation, and current protections are insufficient.
method Formalizes protection against strong-adversary threat model as per-report -differential privacy, and provides a generic mechanism to reduce private auditing to private continual counting.
result Demonstrates a reduction in selection error and improved utility over randomized response.
Performance of nuclear threat detection systems based on gamma-ray spectrometry often strongly depends on the ability to identify the part of measured signal that can be attributed to background radiation. We have successfully applied a method based on Principal Component Analysis (PCA) to obtain a compact null-space m…
Neural networks predict substructures from mass spectra to identify chemical threats.
problem Identifying unknown chemical threats from mass spectra and formulas.
method Data-driven approach using neural networks to rank and match substructures.
result Substructure classifiers achieve over 90% micro F1-score and correctly identify structures in 88-71% of cases.
Confidential Guardian prevents model abstention from being used to discriminate.
problem Dishonest institutions can exploit machine learning model abstention to unfairly deny services.
method Confidential Guardian uses zero-knowledge proofs to verify model confidence and detect suppression.
result Confidential Guardian effectively prevents the misuse of cautious predictions.
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.
Quantum computing poses a threat to Bitcoin and Ethereum, but only to spending and not mining.
problem Quantum computing threat to Bitcoin and Ethereum
method Separation of Shor's and Grover's quantum algorithms
result Quantum algorithms' impact on Bitcoin and Ethereum
Survey on adversarial attacks and defenses in deep learning.
problem Vulnerability of deep learning systems to adversarial examples.
method Discussion on different types of adversarial attacks and countermeasures.
result Challenges and inefficiencies in current countermeasures.
Modelo de maturidade em gerenciamento de riscos em projetos (Project Risk Management Model Maturity)q-fin.RM
The globalization feeded by the technology explosion that begans in the end of the last century, started the world to change faster every day. The only today's certain is the tomorrow's uncertain. Risk is defined as uncertain where one or many causes composed of ocurrence probality can generate an impact or consequence…
Model for optimal cybersecurity investment considering clustered cyberattacks.
problem Optimal investment in cybersecurity to reduce system vulnerability under clustered cyberattacks.
method Developed a continuous-time stochastic model using a Hawkes process, extended Gordon-Loeb model, solved as a Markovian stochastic optimal control problem.
result Investment policies that account for attack clustering lead to more effective and responsive strategies, improving upon static and Poisson-based approaches.
A new attack method shows small changes can compromise distributed learning models.
problem Small but well-crafted changes can compromise distributed learning models.
method Demonstrates a novel non-omniscient attack on distributed learning.
result 20% of corrupt workers can degrade model accuracy by 50% and introduce backdoors.
Study finds stocks with higher cyber risk scores outperform others, indicating a market-wide cyber risk premium.
problem Identifying and quantifying firms' cyber risks and their impact on stock performance.
method Machine learning algorithm to analyze disclosures and a dedicated cyber corpus.
result High cyber risk stocks significantly outperform others, indicating a market-wide cyber risk premium.
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classificationcs.CV
Crowdsourcing insect observations helps assess demographic shifts.
problem Difficulty in collecting sufficient scale insect census data.
method Gather observations from bystanders and enthusiasts.
result Interest exists for collaboration between enthusiasts and researchers.
This paper benchmarks time-series adversarial defenses and attacks.
problem Adversarial attacks on time-series data.
method Detailed benchmarking of adversarial defense methods in the threat model.
result Adversarial defenses offer robustness against both strong white-box and black-box attacks.
Extends randomized smoothing to certify robustness against various threat models and adversarial perturbations.
problem Certifying robustness of classifiers against adversarial perturbations.
method Develops a method to certify robustness against any () minimized adversarial perturbation.
result Randomized smoothing suffers from the curse of dimensionality, reducing effective radius as increases.
We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables. Unlike CCA, which finds correlations between two sets of data where the rows are matched exactly but the columns represent separate sets of variables, th…
Paper shows data poisoning and Byzantine attacks are equivalent, impacting federated learning security.
problem Resilience of federated learning systems to adversarial attacks.
method Proved equivalence between data poisoning and Byzantine gradient attacks.
result Equivalence between data poisoning and Byzantine attacks in federated learning.
New model evaluates how well models handle input faults.
problem Fault tolerance of models to input variations.
method Evaluates fault tolerance using information-based characteristic for arbitrary valid inputs.
result Proposes a new way to measure model robustness.