Jyothi Koganti Krishna, Borra Subba Reddy, Srilakshmi Koganti, Balachandran Praveen Kumar, Reddy Ganesh Prasad, Colak Ilhami, Dhanamjayulu C, Chinthaginjala Ravikumar, Khan Baseem
Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, TS, 501301, India.
Department of Information Technology, Malla Reddy Engineering College for Women, Hyderabad, TS, India.
Sci Rep. 2024 Mar 7;14(1):5590. doi: 10.1038/s41598-024-55098-2.
Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.
在当今数字化连接和网络化的社会中，网络安全至关重要。随着技术的发展并在我们的日常生活中变得更加普及，网络安全的重要性无论怎样高估都不为过。网络安全对于人们的保护至关重要。一种被称为“凭证填充”的网络攻击类型，涉及攻击者使用先前获取的用户名和密码未经授权访问多个网站上的用户账户。由于很多人在多个不同网站上使用相同的密码和用户名，所以这是可行的。维护在线账户的安全需要抵御凭证填充攻击。所提出的EWOA-ANN模型可以处理凭证填充攻击、故障检测和预测等问题。在此，一种称为增强鲸鱼优化算法（EWOA）的新型优化方法被用于训练神经网络。所提出的攻击识别模型的有效性已经得到证明，并且将针对具体的安全分析进行实证比较。
