This article compares EVE-NG, a network emulation software, to other commonly used network simulation tools such as Cisco Packet Tracer and GNS3. EVE-NG’s features, benefits, and drawbacks are assessed in terms of system requirements, support platforms, web interface, console access, topology management, and advanced lab functionalities. EVE-NG is tested by the students in the real-world application, and feedback from a group of students is gathered regarding their satisfaction and suggestions for improvement. The results of this study show that EVE-NG provides a robust and versatile tool for simulating complex network scenarios, despite compatibility, reliability, and performance issues. It is concluded that EVE-NG has the potential to improve student learning outcomes in computer networks; however, ongoing development and refinement are necessary to meet user expectations and needs.

The primary focus of the study is to investigate the importance of data collection and analysis in IP networks for detecting, identifying, and responding to potential cyber attacks. It examines the use of the Suricata system’s integration with the process of sending detected anomalies to the non-relational Elasticsearch database. The research also looks into the use of Data Lakes, which are centralized storage systems capable of securely storing and analyzing massive amounts of IP traffic data in their native format. An experimental environment is presented, featuring the Elasticsearch database, REDIS cache, and Suricata IDS tool, to conduct experiments. The findings show that combining Suricata with Elasticsearch and Redis cache results a suitable combination, leading to enhanced performance and increased analysis accuracy. In conclusion, by leveraging the strengths of these technologies, it is possible to establish a robust and efficient infrastructure that effectively assists network administrators to safeguard networks against various cyber threats in the network environment.

This work addresses the challenge of video semantic segmentation, a critical component in applications such as autonomous driving. The primary aim was to explore the role of temporal awareness in video sequences and its impact on split computing. To achieve this, we analyzed existing deep neural networks for semantic segmentation and their computational demands and proposed a split computing architecture that leverages high-accuracy segmentation results from a remote server to enhance performance on mobile devices. To validate the proposed approach, we developed and tested four U-Net modifications on the CamVid dataset. Our results demonstrate that incorporating segmentation masks from previous frames significantly improves accuracy in split computing scenarios. In particular, masks warped using optical flow yielded the best results, increasing segmentation accuracy from 81.1% to 84.1% with minimal additional computational cost. These findings highlight the potential of time-aware split computing to enhance video semantic segmentation performance in resource-constrained IoT environments.

Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are poorly represented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA. The approach combines YOLOv8-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, and an automatic emergency braking (AEB) override. Fused observations are classified as semantic-geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios, comprising 19253 frames. On a manually annotated subset of 1200 frames, the full pipeline achieved 93.7% precision, 94.7% recall, and a 94.2% F1-score. The CPU implementation processed one frame in 34.7 ms on average, remaining within the 50 ms budget of a 20 Hz simulation tick.

Rare and safety-critical traffic situations remain challenging for autonomous driving (AD) because they are underrepresented in common training data and may include objects outside standard detector classes. This paper presents a real-time RGB-LiDAR fusion framework for detecting and reacting to rare traffic situations in CARLA (Car Learning to Act), a reproducible simulator for AD research. The approach combines YOLOv8n-based RGB perception, bird’s-eye-view (BEV) LiDAR clustering, decision-level fusion, an interpretable rule-based safety agent with hysteresis, Time-to-Collision (TTC)-aware escalation, and an automatic emergency braking (AEB) override above the CARLA autopilot. Fused observations are classified as semantic–geometric detections, semantic-only detections, or geometric-only obstacle candidates, where unmatched LiDAR clusters are treated conservatively as candidate-level physical evidence rather than confirmed rare objects. The framework was evaluated on three CARLA maps and 3CSim-inspired corner-case scenarios comprising 19,253 frames, with additional weather/lighting stress tests and a public nuScenes mini cross-platform check. On a manually annotated subset of 4800 CARLA frames, corresponding to approximately $24.9\%$ of the recorded CARLA log, the full framework achieved $96.2\%$ precision, $97.3\%$ recall, and a $96.7\%$ F1-score for safety-relevant threat detection. The control experiments show that the fusion-based safety agent reduced unnecessary braking to $1.7\%$ compared with $8.6\%$ for the LiDAR-only baseline and achieved event-level success on the annotated critical intervals. The proposed CPU-only implementation maintained real-time performance, with an average processing time of $34.7\mathrm{ms}$.