Abstract This work introduces a highly effective method for identifying harmful network activity by leveraging the power of artificial neural networks. These networks, particularly adept at analyzing deep packet inspection-based systems for detecting intrusions, have been the cornerstone of our research. The method was rigorously tested using a diverse range of harmless network data, including images, dynamic link library files, logs,... This work introduces a highly effective method for identifying harmful network activity by leveraging the power of artificial neural networks. These networks, particularly adept at analyzing deep packet inspection-based systems for detecting intrusions, have been the cornerstone of our research. The method was rigorously tested using a diverse range of harmless network data, including images, dynamic link library files, logs, music files, word processing documents, and malicious shell code files from the exploit and vulnerability database, exploitdb. The proposed artificial neural network design successfully distinguished between harmless and harmful network traffic. The developed neural network consistently achieved a 99% accuracy rate, a 0.99 average area under the receiver operator characteristic curve (AUC-ROC), and a false positive rate of less than 2% across various 10-fold cross-validation tests. These results underscore our classification method’s reliability, accuracy, and effectiveness. This innovative strategy for identifying harmful network traffic holds significant potential for enhancing intrusion detection systems in both traditional network traffic analysis and cyber-physical systems analysis, such as smart grids. Furthermore, we present a new intrusion detection system (IDS) that combines a multilayer perceptron (MLP) network with an artificial bee colony (ABC) and fuzzy clustering algorithms. The MLP network distinguishes between standard and irregular network data packets, while the ABC method fine-tunes the connections and prejudices of the MLP during its training phase. We used the CloudSim model and the NSL-KDD data set to validate our method, evaluating it with criteria including mean absolute error (MAE), root mean square error (RMSE), and the kappa coefficient. Our findings demonstrate that our proposed technique surpasses current leading methods in the field. Read More Read Less

Abstract In contemporary culture, individuals increasingly need to manage an ever-expanding array of personal profiles and online accounts. Yet, the level of understanding about the importance of strong password security continues to be a major weakness in cybersecurity. Despite the availability of various tools for password protection, the necessity of teaching users and expanding their awareness about the significance of password... In contemporary culture, individuals increasingly need to manage an ever-expanding array of personal profiles and online accounts. Yet, the level of understanding about the importance of strong password security continues to be a major weakness in cybersecurity. Despite the availability of various tools for password protection, the necessity of teaching users and expanding their awareness about the significance of password strength and the associated cybersecurity threats cannot be overstated. Moreover, the popularity of gamification as a method for teaching and overcoming obstacles across various areas, particularly those linked to motivation and focus, has increased recently. This paper delves into the idea of a two-dimensional game where participants confront specific challenges designed to replace outdated, weak passwords with new, more robust ones while navigating to avoid losing access to multiple platforms. The game’s addition of time limitations and simulated cyber threats enhances the educational experience. It highlights the critical nature of the subject, illustrating the potential of gamification in boosting awareness about password security. Read More Read Less