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Found 19 papers, 4 papers with code
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models.
Treating Crowdsourcing as Examination: How to Score Tasks and Online Workers?
Thus, we first score workers' ability mainly on the medium difficult tasks, then reducing the weight of answers from sloppy workers and modifying the answers from spammers when inferring the tasks' ground truth.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networks
To bypass the detection of NIDS, the generated network traffic and benign traffic are classified by a black-box NIDS.
Decision Making
Image Generation
+1
Cryptography and Security
Machine Learning based Malicious Payload Identification in Software-Defined Networking
In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection.
Networking and Internet Architecture
An Adversarial Attack via Feature Contributive Regions
Recently, to deal with the vulnerability to generate examples of CNNs, there are many advanced algorithms that have been proposed.
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems
As the number of cyber-attacks is increasing, cybersecurity is evolving to a key concern for any business.
Deep Graph Matching and Searching for Semantic Code Retrieval
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers
We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.
Cryptography and Security
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks.
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
An Unsupervised Framework for Anomaly Detection in a Water Treatment System.
Current Cyber-Physical Systems (CPSs) are sophisticated, complex, and equipped with networked sensors and actuators.
Generalized Transformation-based Gradient
The reparameterization trick has become one of the most useful tools in the field of variational inference.
Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.
Enhanced Cyber-Physical Security through Deep Learning Techniques
Nowadays that various aspects of our lives depend on complex cyber-physical systems, automated anomaly detection, as well as attack prevention and reaction have become of paramount importance and directly affect our security and ultimately our quality of life.
V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing
Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of the software are more likely to be vulnerable.
Cryptography and Security
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection.
