As the interconnection of digital infrastructures grows at a fast pace, businesses are facing ever-evolving cyber threats. Efficient detection of intrusions is required due to the growing complexity of cyber threats. Classical Network Intrusion Detection Systems (NIDS) have a good capability to detect intrusion events based on certain signatures; nevertheless, they produce lots of unfiltered alerts, increasing the workloads of SOC analysts. In this regard, this research suggests a NIDS with a built-in capability of alert generation and priority assignment based on the Exploit Prediction Scoring System (EPSS). The suggested NIDS can perform real-time monitoring of the network and offline PCAP file analysis for discovering such intrusion events as port scans, brute force, unauthorized access attempts, and protocols misuses. Generated alerts will be sent through an HTTPS connection and will be supplemented with CVE-related vulnerability information. The EPSS scores will be employed to determine actual exploitability of identified vulnerabilities and assign alerts to particular priority categories. Besides, the introduced method includes explainable alert generation and SOC-style visualizing tool. Experiment shows that such NIDS will help to optimize the process of alert management and provide a better user experience for SOC analysts.
R. K, V. V, Ajay Poojith S R et al.· 2026 International Conferenc...· 0 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
