With the increasing number of vehicles, urbanization and the constant rise in road usage, traffic violations have become hugely problematic in today's transportation situations. Some of the most common dangerous driving behaviors that lead to road accidents and traffic delays are as follows: Not wearing a helmet, running a red light, and breaking lanes, breaking seatbelt, using a cell phone while driving and triple riding. Maintaining consistent observation, precise detection, scalability, and speedy identification of traffic infractions in complex road situations are all challenges faced by current traffic violation monitoring methods. Factors such as high traffic levels, uneven lighting, environmental interference, and requiring human supervision limit the effectiveness of current monitoring methods. Therefore, it becomes essential to have a sophisticated automated system that can efficiently do real-time traffic infraction analysis. The proposed study utilizes a novel Traffic violations identification method based on YOLOv8 to detect many traffic violations in the surveillance photos and videos. To achieve the system execution, a Traffic Rule-net Dataset was developed from a set of traffic data collected from different scenarios of city transport, highways and crossroads. The quality of the features and the robustness of the suggested model were enhanced using a number of data pre-processing techniques, including normalization, image size modification, data augmentation, and filtering. The new framework was applicable in all environmental conditions, allowing for efficient object localization and classification of different breaches. From the experimental evaluation it is clear that there was a reduction in false detection, performance, and detection efficiency. Infrancements of the traffic rules may be easily and efficiently detected for traffic control through the use of intelligent monitoring.
Selvam L, G. Aninthitha, P. M et al.· 2026 7th International Confe...· 0 citations
The fast-growing interconnectivity of networks and digital communication platforms, along with extensive information exchange, has made cybersecurity issues in modern computing environments more severe. The expansion of networking infrastructures always results in a massive flow of traffic, thus rendering traditional monitoring techniques and security tools ineffective. Modern cybersecurity systems often have problems detecting the changing nature of cyber threats since some of these have behaviors different from those associated with cyber attack signatures. This not only limits their effectiveness but also causes high rates of false alerts and late responses to complex threats like phishing, ransomware communications, distributed denial of service attacks, key logging activities, bot attacks, and packet sniffing. These all highlight the need for smart cybersecurity solutions that can analyze network activities effectively and detect any kind of threats. The designed model comprises two major layers, which are referred to as the data collection layer and the analysis layer. Network traffic details and system logs are collected using simulated or live data from either simulated or live environment and transformed into structured datasets to undergo the subsequent process steps. Preprocessing involves the removal of unnecessary data instances, cleansing of noise, and feature extraction to enable successful attack detection. This framework uses a combination of signature-based detection and anomaly detection methods to detect attacks through analysis of patterns that are consistent with an attack and patterns that are abnormal in terms of network behavior. This allows better detection of both current and new types of attacks. If a suspicious activity is observed in the network, then the design generates security alerts, stores the incident logs, and automatically responds by blocking the suspicious IP address.
D.NirmalaDevi, J. K. Jeevitha, P. M et al.· 2026 7th International Confe...· 0 citations
