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Found 11 papers, 0 papers with code
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks
A common service provision involves the input data from the client and the model on the analyst's side.
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models
In this paper, we perform experiments to show that generic removal/insertion XAI evaluation methods are not suitable for deepfake detection models.
Few-shot Weakly-supervised Cybersecurity Anomaly Detection
In this paper, we propose an enhancement to an existing few-shot weakly-supervised deep learning anomaly detection framework.
Feature Mining for Encrypted Malicious Traffic Detection with Deep Learning and Other Machine Learning Algorithms
Currently, research on encrypted malicious traffic detection without decryption has focused on feature extraction and the choice of machine learning or deep learning algorithms.
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers
In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers.
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection
Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet.
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning
Our solution achieved 1st place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.
Intrusion Detection in Internet of Things using Convolutional Neural Networks
As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques.
Clustering based opcode graph generation for malware variant detection
Hence, for each malware family, a group of signatures is generated to represent the family.
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets.
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
In the following experiments, we compare the explainability and fidelity of our proposed method with state-of-the-arts, respectively.
