Tomasz Surmacz

In this paper, we show how methods known from Natural Language Processing (NLP) can be used to detect anomalies in HTTP requests and malicious URLs. Most of the current solutions focusing on a similar problem are either rule-based or trained using manually selected features. Modern NLP methods, however, have great potential in capturing a deep understanding of samples and therefore improving the classification results. Other methods, which rely on a similar idea, often ignore the interpretability of the results, which is so important in machine learning. We are trying to fill this gap. In addition, we show to what extent the proposed solutions are resistant to concept drift. In our work, we compare three different vectorization methods: simple BoW, fastText, and the current state-of-the-art language model RoBERTa. The obtained vectors are later used in the classification task. In order to explain our results, we utilize the SHAP method. We evaluate the feasibility of our methods on four different datasets: CSIC2010, UNSW-NB15, MALICIOUSURL, and ISCX-URL2016. The first two are related to HTTP traffic, the other two contain malicious URLs. The results we show are comparable to others or better, and most importantly - interpretable.

Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. However, this requires large volumes of data to be transmitted and may raise privacy issues. This paper presents a dedicated deep learning detection and tracking algorithms that can be run directly on the camera’s embedded system. This method significantly reduces the stream of data from the cameras, reduces the required communication bandwidth and expands the range of communication technologies to use. Consequently, it allows to use short-range radio communication to transmit vehicle-related information directly between the cameras, and implement the multi-camera tracking directly in the cameras. The proposed solution includes detection and tracking algorithms, and a dedicated low-power short-range communication for multi-target multi-camera tracking systems that can be applied in parking and intersection scenarios. System components were evaluated in various scenarios including different environmental and weather conditions.