One of the key aspects of 5G networks is the implementation of massive MIMO (Multiple Input Multiple Output) technology combined with adaptive beamforming. This study explores the use of a linear array antenna to manage and reduce unwanted signals such as jamming, interference, and noise, while also boosting the signal strength towards the intended user or device. The main challenge lay in optimizing the weights of the antenna elements, which was tackled by employing adaptive algorithms like LCMV (Linearly Constrained Minimum Variance) and RLS (Recursive Least Squares). To simplify the optimization process, two soft computing techniques—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—were utilized. The performance of the beamforming weights and radiation patterns was assessed in terms of minimizing unwanted signals and maximizing the desired signal. To check how well the proposed methods work, some commonly used algorithms like MVDR (Minimum Variance Distortionless Response) and LCMV are also applied. The outcomes were compared to those from other algorithms. A Differential Beamforming method is applied to examine how effectively the system can focus the signal in the target direction while minimizing unwanted interference from other directions. Additionally, the fminsearch algorithm, which is a basic local search method, is used to compare how well it can adjust the beamforming weights compared to the more advanced global optimization techniques. The results indicate that PSO and GA produce highly similar performance levels.

The new and emerging challenges posed by the convergence of cyber threats and socio-political tensions have risen as one of the core formidable threats to the present global security landscape. This paper proposes a hybrid predictive model intended to act against these real-world multidimensional attack vectors. The model integrates cyber threat hunting techniques with socio-political risk assessment methodologies to comprehensively forecast consequent cybersecurity threats to social unrest scenarios. Cyber threat data is collected from sources such as the Offensive Defensive-Intrusion Detection System (OD-IDS2022) and the Aegean Wi-Fi Intrusion Dataset (AWID3), and social terror attack information is gathered from the Global Database of Events, Language, and Tone (GDLET) Project and Armed Conflict Location & Event Data (ACLED) to comprise the bidirectional dataset for the model that contains views from both cyber and socio-political risk landscapes. The model adopts a holistic, robust predictive capability through k-fold cross-validation and feature importance evaluation implementation techniques. This multidisciplinary approach offers a synoptic understanding of emerging and future security threats and enables the execution of proactive measures to secure national and transnational borders.

This research aims to evaluate the security risks associated with open wireless networks, especially Man-in-the-Middle (MITM) attacks that exploit the Address Resolution Protocol (ARP) and Domain Name System (DNS). The penetration testing process was conducted by creating a secure and controlled laboratory and using a set of tools available in Kali Linux to demonstrate how attackers can exploit these vulnerabilities to gain unauthorized access to victims' devices, steal their sensitive data, and control their devices remotely The research focused on analyzing the effectiveness of social engineering (phishing) attacks under MITM attacks, where a fake web page was created to trick victims into entering their personal data and another web page was created to try to trick victims into downloading malware consisting of an attack payload that aims to create a Reverse Transmission-Control-Protocol (TCP) shell that enables the hacker to explore the target device and execute code using the Metasploit framework. The research results showed the effectiveness of combining ARP and DNS spoofing with phishing attacks and malware injection attacks. The results also showed that open wireless networks are highly vulnerable to attacks and that end users are the weakest link in the security chain. The research also emphasizes the need to develop more effective security solutions to protect core protocols such as ARP and DNS.

The aim of this research is to comparatively evaluate and optimize the performance of active and passive Reconfigurable Intelligent Surfaces (RIS) in Terahertz (THz) Ultra Massive MIMO(UM-MIMO) systems for 6G Wireless Communication. The Primary objective is to analyze the impact of beamforming and adaptive modulation schemes on system capacity, computational complexity and power consumption. The study employs MATLAB based simulations under realistic wireless channel models including Rician fading channel and free space path loss to model the propagation behavior at THz Frequencies. Both active and passive RIS configuration are assessed using hybrid beamforming and multi antenna transmission techniques. Simulation results demonstrate that active RIS improves system capacity compared to passive RIS. Particularly at higher SNR level, while incurring more power consumption. Conversely, passive RIS offers better energy efficiency and lower complexity, making it suitable for low-power scenarios. These findings highlight critical design trade-offs and support the development of hybrid RIS assisted UM-MIMO architectures for efficient and scalable 6G THz communication systems.  

The growing adoption of Internet of Things (IoT) devices has amplified the need for robust security mechanisms, particularly against Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper proposes a deep learning-based detection system using a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) model to effectively capture both spatial and temporal patterns of malicious activity. The CICDDoS2019 dataset is employed for training and evaluation, with preprocessing steps including Boruta-based feature selection and data rebalancing using SMOTE. A user-friendly GUI developed in Python (Tkinter) facilitates real-time input and prediction. The proposed model, Cyber Guard, demonstrates high accuracy and efficiency, offering a practical solution for IoT attack detection and future deployment.