This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

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.

The rapid deployment of technologies that can share data brings, in addition to the positive aspects, also technologies that an attacker can use to misuse personal data. Vulnerabilities in the network can be divided according to the type of attack into horizontal attacks from the perspective of the attacker and vertical attacks from the perspective of the victim. An important goal of organizations is to successfully defend against such an attack. The search for attacks on the network infrastructure can be ensured by implementing machine learning that can capture the current attack. This work describes methods of searching for attacks using graphical representation of data and also using machine learning of the high count type at different sizes of the investigated segments.

Steganography is considered to be the science and the art of hidden communication through an open communication channel or hiding information using innocent and harmless-looking media. It has been evolving for centuries, but as in many areas of human activity, its new possibilities and effectiveness have been brought about by the development of information and communication technologies. These have not only become a means of simplifying and speeding up the implementation of operations enabling the hiding of information in stegomedia and dynamizing the transmission of such modified media, but they have also brought with them specific types of stegomedia and information hiding algorithms. They are so closely related to information and communication technologies that without their existence the meaning of those algorithms and stegomedia will be lost. They use specific data structures, file formats, communication protocols, etc. and are even adapted to the design of the hardware and related firmware. The following paper is devoted to this area, dealing with hiding information in the domain-specific hierarchical data structure Symmetry-aware Sparse Voxel Directed Acyclic Graphs. It is designed for lossless-compressed representation of voxelized three-dimensional scenes in the field of computer graphics. The paper deals with a brief description of this data structure and proposes a new steganographic algorithm. The algorithm allows information to be hidden through specific features of this data structure using its three-dimensional voxel grids.