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Found 27 papers, 8 papers with code
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack.
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations.
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner.
Quantum-Inspired Machine Learning: a Survey
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks.
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods
In this context, we proposed a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the classifier model.
Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data
In this research, we trained and evaluated our proposed LSTM-AE model on reflection-based DDoS attacks (DNS, LDAP, and SNMP).
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels
The resulting radio frequency fingerprints (RFFs) are distorted, leading to low device detection and classification accuracy.
An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability
In this paper we propose a novel end-to-end deep learning-based approach to identify the vulnerability-relevant code statements of a specific function.
Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network
We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame.
An Additive Instance-Wise Approach to Multi-class Model Interpretation
A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.
Transferable Graph Backdoor Attack
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being.
Transformer-Based Language Models for Software Vulnerability Detection
The large transformer-based language models demonstrate excellent performance in natural language processing.
Towards Web Phishing Detection Limitations and Mitigation
However, with an average of 10K phishing links reported per hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of such ML-based solutions are laid bare.
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Biometric data, such as face images, are often associated with sensitive information (e. g medical, financial, personal government records).
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors
We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i. e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i. e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks.
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware
Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the training dataset, resulting in high false-positive rates.
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance
Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning that are gaining attention due to their ability to preserve the privacy of raw data.
Characterizing Malicious URL Campaigns
Seemingly dissimilar URLs are being used in an organized way to perform phishing attacks and distribute malware.
FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation
FedDICE integrates federated learning (FL), which is privacy-preserving learning, to SDN-oriented security architecture to enable collaborative learning, detection, and mitigation of ransomware attacks.
Peeler: Profiling Kernel-Level Events to Detect Ransomware
Based on those characteristics, we develop Peeler that continuously monitors a target system's kernel events and detects ransomware attacks on the system.
Malware Detection
Cryptography and Security
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to `contrast' between data and class representation in the latent space.
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects.
Evaluation of Federated Learning in Phishing Email Detection
For a fixed total email dataset, the global RNN based model suffers by a 1. 8% accuracy drop when increasing organizational counts from 2 to 10.
SplitFed: When Federated Learning Meets Split Learning
SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.
A Channel Perceiving Attack on Long-Range Key Generation and Its Countermeasure
Unfortunately, there is no experimental validation for communications environments when there are large-scale and small-scale fading effects.
