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Blockchain-based immunization against kleptographic attacks
Adversarial implementations of cryptographic primitives called kleptographic attacks cause the leakage of secret information. Subliminal channel...
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DE2RA-RPL: detection and elimination of resource-related attacks in IoT RPL-based protocol
Resource Attacks in the Internet of Things (IoT) target to attack resource-related things. It affects the memory, processing, energy, and battery of...
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Detection of adversarial attacks based on differences in image entropy
Although deep neural networks (DNNs) have achieved high performance across various applications, they are often deceived by adversarial examples...
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Phishing attacks: risks and challenges for law firms
Law firms have become prime targets for cybercriminals. This is also because the volume of sensitive data handled by the average law firm has...
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Defense against Adversarial Attacks on Image Recognition Systems Using an Autoencoder
AbstractAdversarial attacks on artificial neural network systems for image recognition are considered. To improve the security of image recognition...
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State of the art on adversarial attacks and defenses in graphs
Graph neural networks (GNNs) had shown excellent performance in complex graph data modelings such as node classification, link prediction and graph...
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Method for Detecting Manipulation Attacks on Recommender Systems with Collaborative Filtering
Abstract —The security of recommendation systems with collaborative filtering from manipulation attacks is considered. The most common types of...
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Local imperceptible adversarial attacks against human pose estimation networks
Deep neural networks are vulnerable to attacks from adversarial inputs. Corresponding attack research on human pose estimation (HPE), particularly...
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A novel approach detection for IIoT attacks via artificial intelligence
The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as...
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Targeted adversarial attacks on wind power forecasts
In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of...
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Black-box attacks on face recognition via affine-invariant training
Deep neural network (DNN)-based face recognition has shown impressive performance in verification; however, recent studies reveal a vulnerability in...
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Maxwell’s Demon in MLP-Mixer: towards transferable adversarial attacks
Models based on MLP-Mixer architecture are becoming popular, but they still suffer from adversarial examples. Although it has been shown that...
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Time series adversarial attacks: an investigation of smooth perturbations and defense approaches
Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while...
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Optimizing Rectangle and Boomerang Attacks: A Unified and Generic Framework for Key Recovery
The rectangle attack has shown to be a very powerful form of cryptanalysis against block ciphers. Given a rectangle distinguisher, one expects to...
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Leveraging blockchain and machine learning to counter DDoS attacks over IoT network
The paper presents an approach for detecting Distributed Denial of Service (DDoS) attacks using machine learning and blockchain technology. With the...
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Secure Voice Processing Systems against Malicious Voice Attacks
This book provides readers with the basic understanding regarding the threats to the voice processing systems, the state-of-the-art defense methods...
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MuChat against active attacks, passive attacks, and traffic analysis methods: a free convert chat application for instant communication on mobile
In recent years, Censorship and anti-censorship technology is develo** rapidly. The censorship and surveillance systems track traffic on the...
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Adversarial attacks in computer vision: a survey
Deep learning, as an important topic of artificial intelligence, has been widely applied in various fields, especially in computer vision...
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Data Poisoning Attacks and Mitigation Strategies on Federated Support Vector Machines
Federated learning is a machine learning approach where multiple edge devices, each holding local data samples, send a locally trained model to the...
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Leveraging deep learning-assisted attacks against image obfuscation via federated learning
Obfuscation techniques (e.g., blurring) are employed to protect sensitive information (SI) in images such as individuals’ faces. Recent works...
