Applying a Deep Neural Network and Feature Engineering to Assess the Impact of Attacks on Autonomous Vehicles by Sara Ftaimi, Tomader Mazri

“…The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. …”
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Principal Component Analysis-Multilinear Perceptron-based model for Distributed Denial of Service Attack Mitigation by Opeyemi Oreoluwa Asaolu, Oluwasanmi Segun Adanigbo, Afeez Adekunle Soladoye, Nnamdi Stephen Okomba

“…The PCA-MLP model proposed in this study demonstrated high performance, accurately identifying different types of DDoS attacks with an overall accuracy of 95.35%. …”
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A comprehensive survey of cyberattacks on EVs: Research domains, attacks, defensive mechanisms, and verification methods by Tawfiq Aljohani, Abdulaziz Almutairi

“…The second is the CIA-based attacks, which examines the threats to the confidentiality, integrity, and availability of EVs' sensitive information and critical systems. …”
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Salient object detection dataset with adversarial attacks for genetic programming and neural networksMendeley Data by Matthieu Olague, Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez

“…Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers' attacks. This dataset is an image repository containing five different image databases to evaluate adversarial robustness by introducing 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. …”
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Enhanced Synchrosqueezing Transform for Detecting Non-Traditional Flight Modes in High Angle of Attack Maneuvers by Seyed Amin Bagherzadeh, Hamed Mohammadkarimi, Mohammad Hossein Alizadeh

“…Due to nonlinear aerodynamics, “non-traditional” flight modes may appear in longitudinal and lateral/directional dynamics once an aircraft experiences a high angle of attack and rapid maneuvers. Signal decomposition techniques are required to uncover these modes since they are hidden in flight characteristics. …”
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Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors by Apeksha Gawande, Swati Sherekar, Ranjit Gawande

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TrapMI: A Data Protection Method to Resist Model Inversion Attacks in Split Learning by Hyunsik Na, Daeseon Choi

“…The proposed method can induce an attacker to generate a class-specific target image that appears different from the original image when inverting the input image. …”
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Experimental and numerical study on the high-speed ship hydrodynamics influenced by an interceptor with varied angle of attack by Arfis Maydino Firmansyah Putra, Hiroyoshi Suzuki

“…Efforts to improve the hydrodynamic performance of high-speed ships have been underway for a long time. There are different approaches, one of which is to take advantage of an interceptor. …”
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DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system by G. Usha, H. Karthikeyan, Kumar Gautam, Nikhil Pachauri

“…The protection of the intelligent transportation system from attacks and continual maintenance is crucial. In this research work, a Distributed Denial of Service attack detection scheme is proposed to protect the Intelligent Transportation System ecosystem, making use of the Adaptive Neuro-Fuzzy Inference System. …”
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Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

“…These systems are susceptible to physical and cyber-attacks, which can seriously affect human life and the economy. …”
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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security by Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

“…We have also created a confusion matrix over different kinds of attacks concerning predicted class and attack class, enabling us to evaluate the performance of a classification model for various attack groups without taking CSO and after applying the CSO approach. …”
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Blind-Folded: Simple Power Analysis Attacks using Data with a Single Trace and no Training by Xunyue Hu, Quentin L. Meunier, Emmanuelle Encrenaz

“…This difference is actually so important that a single sample can be considered for attacking one condition, and no training on other traces is required. …”
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Secure Scan Architecture for Enhanced Testability and Resistance Against Side-Channel Attacks in Cryptographic ICs by A. Swetha Priya, S. Kamatchi, E. Lakshmi Prasad

“…Traditional scan-based Design for Testability (DFT) techniques give full visibility of internal nodes, which makes them vulnerable to side-channel attacks where the confidential data is compromised. …”
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Towards saturation attack detection in SDN: a multi-edge representation learning-based method by Zhangli Ji, Yunhe Cui, Yinyan Guo, Guowei Shen, Yi Chen, Chun Guo

“…Experiments conducted on two different network topologies demonstrate that the proposed method achieves high detection accuracy and strong graph representation capability, highlighting its effectiveness in identifying saturation attack flows.…”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…Detection accuracy exceeds 99% for three different frequency characteristic attacks, with an average detection delay of only 0.12 seconds. …”
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GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations by Oscar A. Tobar-Rosero, Omar A. Roa-Romero, Germán D. Rueda-Carvajal, Alexánder Leal-Piedrahita, Juan F. Botero-Vega, Sergio A. Gutierrez-Betancur, John W. Branch-Bedoya, Germán D. Zapata-Madrigal

“…The dataset highlights the real-world impacts of these attacks, demonstrating the execution of unintended operations under different operational scenarios, including both stable conditions and situations involving system failures. …”
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Breaking and Healing: GAN-Based Adversarial Attacks and Post-Adversarial Recovery for 5G IDSs by Yasmeen Alslman, Mouhammd Alkasassbeh, Mohammad J. Abdel-Rahman

“…Comprehensive experiments are conducted to evaluate the effectiveness of each GAN in exploiting IDS vulnerabilities and examine the transferability of adversarial attacks across different IDSs, considering the quality of the adversarial samples generated. …”
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Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights by Filippo Genuario, Giuseppe Santoro, Michele Giliberti, Stefania Bello, Elvira Zazzera, Donato Impedovo

“…However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based intrusion detection system (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS—Host IDS) or, in general, within the traffic of a network (NIDS—Network IDS). …”
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The usage of power system multi-model forecasting aided state estimation for cyber attack detection by I. A. Lukicheva, A. L. Kulikov

“…Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. …”
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A Novel Centrality-Based Attack Simulation: Evaluating Resilience and Vulnerability in China’s Knowledge Networks by Tianxing Zhu, Jinyang Liu, Changxin Song, Xuan Miao, Sheng Zhu

“…Existing research on the vulnerability of knowledge innovation networks (KINs) primarily falls into two categories: random attacks and deliberate attacks. This study introduces a novel centrality-based attack framework for the deliberate attack group and systematically examines the impact of different attack mechanisms (such as loss of connectivity, propagation delays, and structural fragmentation) on KINs. …”
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