The escalating complexity of cyberattacks necessitates advanced strategies for their detection and mitigation. This study presents a hybrid model that integrates Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) networks to reconstruct and predict attack vectors in cybersecurity. GNNs are employed to analyze the structural relationships within the MITRE ATT&CK framework, while LSTM networks are utilized to model the temporal dynamics of attack sequences, effectively capturing the evolution of cyber threats. The combined approach harnesses the complementary strengths of these methods to deliver precise, interpretable, and adaptable solutions for addressing cybersecurity challenges. Experimental evaluation on the CICIDS2017 dataset reveals the model’s strong performance, achieving an Area Under the Curve (AUC) of 0.99 on both balanced and imbalanced test sets, an F1-score of 0.85 for technique prediction, and a Mean Squared Error (MSE) of 0.05 for risk assessment. These findings underscore the model’s capability to accurately reconstruct attack paths and forecast future techniques, offering a promising avenue for strengthening proactive defense mechanisms against evolving cyber threats.

Cryptanalysis the SHA-256 Hash Function using Rainbow Tables

Designing and implementing a fail-safe, real-time telecommunications network is complex. In modern networks, traditional quality of service (QoS) methods for monitoring and analyzing data have some problems, such as accuracy and efficient processing of big data in real time. To solve this problem, should use an appropriate intelligent crash classification system to detect and diagnose run-time errors. The article proposes to use a comprehensive fault detection system that includes QoS and machine learning technologies using information about the state of a point-to-point protocol over ethernet (PPPoE) session on PPPoE active discovery termination (PADT) virtual local area network (VLAN) routes. This intelligent system is built using the machine learning method and is independent of the main real-time system. Demonstrated the operation of seven machine learning algorithms and presented the results of training and fault detection. Based on the received information about the state of the PPPoE session, the PADT type allows you to control the behavior of the real-time system.

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage.

Cyber threats are becoming increasingly sophisticated, especially in distributed infrastructures where systems are deeply interconnected. To address this, we developed a framework that automates how organizations discover their digital assets and assess which ones are the most at risk. The approach integrates diverse public information sources, including WHOIS records, DNS data, and SSL certificates, into a unified analysis pipeline without relying on intrusive probing. For risk scoring we applied Gradient Boosted Decision Trees, which proved more robust with messy real-world data than other models we tested. DBSCAN clustering was used to detect unusual exposure patterns across assets. In validation on organizational data, the framework achieved 93.3% accuracy in detecting known vulnerabilities and an F1-score of 0.92 for asset classification. More importantly, security teams spent about 58% less time on manual triage and false alarm handling. The system also demonstrated reasonable scalability, indicating that automated OSINT analysis can provide a practical and resource-efficient way for organizations to maintain visibility over their attack surface.

This paper presents a certificateless group signature scheme designed specifically for Unmanned Aerial Vehicle (UAV) communications in resource-constrained environments. The scheme leverages Physical Unclonable Functions (PUFs) and elliptic curve cryptography (ECC) to provide a lightweight security solution while maintaining essential security properties including anonymity, unforgeability, traceability, and unlikability. We describe the cryptographic protocols for system setup, key generation, signing, verification, and revocation mechanisms. The implementation shows promising results for UAV applications where computational resources are limited, while still providing robust security guarantees for group communications. Our approach eliminates the need for computationally expensive certificate management while ensuring that only legitimate group members can create signatures that cannot be linked to their identities except by authorized group managers.