Yongsheng Zhu^1,2,*, Chong Liu^3,4, Chunlei Chen⁵, Xiaoting Lyu^3,4, Zheng Chen^3,4, Bin Wang⁶, Fuqiang Hu^3,4, Hanxi Li^3,4, Jiao Dai^3,4, Baigen Cai¹, Wei Wang^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1305-1325, 2024, DOI:10.32604/cmes.2024.054820 - 27 September 2024

Abstract The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency. Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data. However, despite its privacy benefits, federated learning systems are vulnerable to poisoning attacks, where adversaries alter local model parameters on compromised clients and send malicious updates to the server, potentially compromising the global model’s accuracy. In this study, we introduce PMM (Perturbation coefficient Multiplied by Maximum value), a new poisoning attack method that perturbs model More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Guangwei Sun^1,2,#, Xuanhao Zhang^3,#, Yi Liu³, Liguang Chai², Daisong Liu², Zhenguo Chen^1,*, Shiyou Lü^3,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 641-659, 2024, DOI:10.32604/phyton.2024.049285 - 29 April 2024

Abstract The Nicotiana genus, commonly known as tobacco, holds significant importance as a crucial economic crop. Confronted with an abundance of herbivorous insects that pose a substantial threat to yield, tobacco has developed a diverse and sophisticated array of mechanisms, establishing itself as a model of plant ecological defense. This review provides a concise overview of the current understanding of tobacco’s defense strategies against herbivores. Direct defenses, exemplified by its well-known tactic of secreting the alkaloid nicotine, serve as a potent toxin against a broad spectrum of herbivorous pests. Moreover, in response to herbivore attacks, tobacco enhances… More >

Kai Chen^*, Jinwei Wang, Jiawei Zhang

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 15-24, 2022, DOI:10.32604/jihpp.2022.029006 - 17 June 2022

Abstract In recent years, machine learning has become more and more popular, especially the continuous development of deep learning technology, which has brought great revolutions to many fields. In tasks such as image classification, natural language processing, information hiding, multimedia synthesis, and so on, the performance of deep learning has far exceeded the traditional algorithms. However, researchers found that although deep learning can train an accurate model through a large amount of data to complete various tasks, the model is vulnerable to the example which is modified artificially. This technology is called adversarial attacks, while the More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Ahmad Alauddin Bin Arifin², Muhammad Shahid Bhatti³, Abid Sohail³, Rohail Hassan⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.020111 - 07 December 2021

Abstract These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely More >

TARYN E. SYMON¹, RAMÓN GAXIOLA-ROBLES^1,2, CLAUDIA J. HERNÁNDEZ-CAMACHO³, TANIA ZENTENO-SAVÍN^1,*

BIOCELL, Vol.45, No.6, pp. 1621-1630, 2021, DOI:10.32604/biocell.2021.016302 - 01 September 2021

Abstract Marine mammals undergo cycles of tissue ischemia and reperfusion during the dive response. Reperfusion injury can result in oxidative tissue damage and the activation of a pro-inflammatory immune response. The risk of oxidative damage is reduced by antioxidants. Our hypothesis is that the reported higher antioxidant defenses within marine mammal tissues provide additional protection in situations that produce oxidative stress, like inflammation, in comparison to terrestrial mammal tissues. Leukocytes were isolated from the whole blood of Pacific bottlenose dolphins (Tursiops truncatus gilli) and humans (Homo sapiens) and were exposed to lipopolysaccharides (LPS, 10 µg/mL) in vitro to simulate… More >