Cybercriminals have long depended on domain names for phishing, spam, malware distribution, and botnet operation. To facilitate the malicious activities, they continually register new domain names for exploitation. Previous work revealed an abnormally high concentration of malicious registrations in a handful of registrars and TLDs. However, no existing study systematically analyzed the factors driving abuse, leaving a critical gap in understanding how different variables influence malicious registrations. In this paper, we carefully distill the inclinations and aversions of malicious actors during the registration of new phishing domain names. Having compiled a list of 14.5 k malicious and 15.4 k benign domains, we collect a comprehensive set of 73 features for all the domains encompassing three main latent factors: registration attributes, proactive verification, and reactive security practices. With a GLM regression analysis, we found that each dollar reduction in registration fees corresponds to a 49% increase in malicious domain registrations. The availability of free bundled services, such as web hosting, drives an 88% surge in phishing activities. Conversely, stringent registration restrictions cut down abuse by 63%, while registrars providing API access for domain registration or account creation experience a staggering 401% rise in malicious domains. The results enable intermediaries involved in domain registration to develop tailored anti-abuse practices, yet aligning them with their economic interests.

In this paper, we focus on improving planning efficiency for ground vehicles in navigation and exploration tasks where the environment is unknown or partially known, leading to frequent updates of the navigational goal as new sensory information is acquired. Asymptotically optimal motion planners like RRT* or FMT* can be used to plan the sequence of actions the robot can follow to achieve its current goal. Frequent replanning of the whole action sequence becomes computationally demanding when actions are not executed precisely because of limited information about the foreground terrain. The decoupled approach can decrease the computational burden with separated path planning and path following; however, it might lead to suboptimal solutions. Therefore, we propose a novel approach based on generating a reusable reward function that guides a fast sampling-based motion planner. The proposed method provides improved results in navigation scenarios compared to the former approaches, and it led to about 7% faster autonomous exploration than the decoupled approach. The present results support the suitability of the proposed method in navigation tasks with continuously updated navigation goals.

In this paper, we address the sensitivity of the 3D LiDAR-based localization to environmental structural ambiguity. Although existing approaches employ additional sensors, such as cameras and inertial measurement units, to account for such ambiguities, multi-sensor localization is still an open problem. Limitations are from the need to tune fusion parameters to compensate for limited ambiguity detection manually. Therefore, we propose a feature-based localization method that learns the fusion parameters using ground truth and thus supports autonomous mobile robotic systems in new locations. The method combines planar surface LiDAR features with close and far camera features, and its further advantage is an online adjustment of the feature weights based on the measured environment ambiguity. The evaluation has been performed on the existing M2DGR dataset and custom dataset with geometrical ambiguities. The proposed method is competitive to or outperforms the existing LiDAR-based methods F-LOAM and LIO-SAM and the Visual-Inertial localization method VINS-Mono. Based on the reported results, the proposed method is a vital combination of LiDAR-based and visual features.

Mobile robots operating in convoys have a limited view of the terrain to be traversed if it is occluded by the preceding vehicle. Furthermore, the preceding vehicle might change the terrain geometry and eventually significantly alter its traversability by driving over the terrain. When the following vehicles do not consider such changes, they can use spurious terrain appearance and geometry to decide whether to follow in the tracks of the previous vehicle or to avoid them since the preceding vehicle’s tracks can make the terrain untraversable. We propose to predict the terrain changes induced by the robot traversal on the traversed terrain and thus support the decision-making of the following vehicles. The developed model projects the robot wheel footprint along the planned robot path and combines the projection with the terrain appearance and prior terrain elevation. The coupled model is used in a convolutional neural network that predicts the elevation after traversal. The footprint projection component is designed so that learned networks can be transferred to vehicles with different wheel footprints without relearning the model. The proposed model is verified using a dataset captured using a real, one-ton, six-wheel robot traversing rigid roads and vegetated fields.