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HybridRobustNet: enhancing detection of hybrid attacks in IoT networks through advanced learning approach
The proliferation of Internet of Things (IoT) devices has revolutionized various domains, but it has also brought forth numerous security challenges....
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An advanced security model for fog computing to tackle various attacks on the data link layer using the QQL-FHES scheme
Fog computing provides maximum flexibility and low latency by divesting the usage of cloud resources and amenities to the end users. In rendering the...
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Variational quantum attacks threaten advanced encryption standard based symmetric cryptography
We propose a variational quantum attack algorithm (VQAA) for classical advanced encryption standard (AES)-like symmetric cryptography, as exemplified...
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Modern Cryptanalysis Methods, Advanced Network Attacks and Cloud Security
As we all know, modern cryptography is “the scientific study of strategies for safeguarding digital information, transactions, and distributed...
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Adequate responses to cyber-attacks
Cybersecurity has become a prominent issue for companies in all sectors. Businesses need to ensure that they have taken the necessary measures and...
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Real-time detection of deception attacks in cyber-physical systems
Detection of deception attacks is pivotal to ensure the safe and reliable operation of cyber-physical systems (CPS). Detection of such attacks needs...
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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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Cybersecurity for autonomous vehicles against malware attacks in smart-cities
Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems...
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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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Channel-augmented joint transformation for transferable adversarial attacks
Deep neural networks (DNNs) are vulnerable to adversarial examples that fool the models with tiny perturbations. Although adversarial attacks have...
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T&TRS: robust collaborative filtering recommender systems against attacks
In recent years, the Internet has had a main and important contribution to human life and the amount of data on the World Wide Web such as books,...
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Enhancing IoT Security: A Blockchain-Based Mitigation Framework for Deauthentication Attacks
The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT...
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A survey on privacy-preserving federated learning against poisoning attacks
Federated learning (FL) is designed to protect privacy of participants by not allowing direct access to the participants’ local datasets and training...
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A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection
Medical image protection against tampering attacks remains an ongoing issue, despite the numerous watermarking-based solutions developed thus far....
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Spoofing attacks against vehicular FMCW radar
The safety and security of the passengers in vehicles in the face of cyber attacks is a key concern in the automotive industry, especially with the...
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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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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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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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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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Timely detection of DDoS attacks in IoT with dimensionality reduction
The exponential growth of IoT devices and their interdependency makes the technology more vulnerable to network attacks like Distributed Denial of...
