Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate traffic in order to evade the rules applied, several machine learning-based IDSs have been developed. In this study, we focused on one such model involving several algorithms and used the NSL-KDD dataset as a benchmark to train and evaluate its performance. We demonstrate a way to create adversarial instances of network traffic that can be used to evade detection by a machine learning-based IDS. Moreover, this traffic can be used for training in order to improve performance in the case of new attacks. Thus, a generative adversarial network (GAN)—i.e., an architecture based on a deep-learning algorithm capable of creating generative models—was implemented. Furthermore, we tested the IDS performance using the generated adversarial traffic. The results showed that, even in the case of the GAN-generated traffic (which could successfully evade IDS detection), by using the adversarial traffic in the testing process, we could improve the machine learning-based IDS performance.

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks.

cryptocurrencies are increasingly used by investors as assets, mainly for short term. Therefore, in the financial sector, prediction is important and can be done using Deep Learning techniques. This paper presents results on the prediction of three cryptocurrencies that see high volumes but are discussed in very few papers. In this paper we used LSTM (Long Short-Term Memory) models and also GRU (Gated Recurrent Unit). These are applied to three important cryptocurrencies, namely DOGECoin, Ripple (XRP) and OMG Network. The results presented prove that LSTM and GRU can be successful in predicting the daily closing values for these cryptocurrencies.

Logging network traffic offers valuable insights into data flow, enabling the proactive analysis and troubleshooting of issues as they arise. Moreover, it provides a means to access and examine the exchanged information among network users that would otherwise be inaccessible. To enhance network traffic analysis, the integration of innovative technologies that facilitate real-time querying and pattern matching proves indispensable. This research paper presents a system that exemplifies such advancements—an innovative network traffic logging tool. The tool specifically focuses on performing real-time network packet transfer to Apache Kafka and ksqlDB, leveraging their capabilities to ensure swift and dependable storage of network packets in Apache Kafka topics. By showcasing this solution, the paper demonstrates the benefits and effectiveness of employing modern technologies for network traffic analysis and management.

The widespread use of GPS tracking devices has made them an indispensable solution in various sectors such as transportation, logistics, and security. However, the complexity of cyber attacks such as denial of service (DoS) attacks have made these devices vulnerable, thereby compromising the security of the data and devices. This has a significant impact on business and applicable legislation related to essential services. In this paper, we propose the integration of security mechanisms and algorithms into the Teltonika IoT GPS tracking device’s firmware, including DoS protection to detect, prevent, and secure these devices against DoS cyber attacks.

Capturing traffic and processing its contents is a valuable skill that when put in the right hands makes diagnosing and troubleshooting network issues an approachable task. Apart from aiding in fixing common problems, packet capture can also be used for any application that requires getting a deeper understanding of how things work under the hood. Many tools have been developed in order to allow the user to study the flow of data inside of a network. This paper focuses on documenting the process of creating such tools and showcasing their use in different contexts. This is achieved by leveraging the power of the C++ programming language and of the libtins library in order to create custom extensible sniffing tools, which are then used in VoIP (Voice over IP) and IDS (Intrusion Detection System) applications.