The article proposes a conceptually new approach to assessing cybersecurity in modern communication networks, which is based on taking into account specialized determinants — sociotechnical parameters that reflect the structural and functional complexity of network interactions. Such determinants include the centrality of nodes, the level of mutual support, the intensity of information exchange, the degree of community connectivity, user popularity, and other non-trivial characteristics that traditional protection mechanisms based on static models ignore. The central idea of the study is to formalize the dynamic stability of the cybersecurity system by building a nonlinear mathematical model that takes into account the nonlinear relationships between these determinants and the general state of network security. Methodologically, the problem is reduced to the formulation of a system of ordinary differential equations that describes the evolution of the system state under the influence of external and internal disturbances, in particular, cyberattacks. For the analytical study of stability, the method of exceptions and solution of the corresponding homogeneous characteristic equation was used, which allows for to identification the conditions of asymptotic stability. Numerical modeling was performed in the MATLAB/Multisim environment, where phase portraits were synthesized, which clearly demonstrate the stable behavior of the system even in the maximum load mode and in the presence of large-scale attacks. The obtained quantitative results confirm that the proposed model adequately reproduces the dynamics of cyber defense and provides the ability to predict the state of the system under conditions of variable network parameters. The key scientific contribution is the development of methodological principles that combine the theory of nonlinear dynamical systems, graph theory, and sociotechnical analysis to form an adaptive, predictive architecture of cyber defense, focused on complex, evolutionary communication networks.

This paper presents a Causal Observability Framework designed to enhance the reliability and performance of cloud-native distributed systems through structured integration with the DevOps pipeline. The framework unifies three interdependent components: real-time telemetry collection, dual-domain causal tracing, and probabilistic causal inference. The causal tracing layer combines a time-domain vector autoregressive Granger causality model with a discrete Fourier transform frequency-domain extension. The causal inference layer employs Bayesian network propagation, updated online via the Expectation-Maximisation algorithm, to compute posterior downstream failure probabilities from upstream anomaly observations. Validation was conducted through a controlled, three-replicate experimental study on a seven-service AI-powered recommendation application deployed across a dual-provider six-node Kubernetes cluster (AWS EKS and GCP GKE) under three traffic profiles ranging from 50 to 500 requests per second. Against a conventional threshold-based monitoring baseline, the proposed framework achieved: a 35% reduction in incident response time (70 minutes to 45 minutes), a 40% reduction in mean time to recovery (50 minutes to 30 minutes), a 1.5 percentage-point improvement in system availability (98.0% to 99.5%), a 61% reduction in false-positive alert rate (18% to 7%), and a 63% improvement in root-cause localisation accuracy (54% to 88%). All five improvements were statistically significant at p < 0.05 via paired t-test. A quantified nine-minute early-warning lead time over conventional detection was demonstrated in the fault-injection scenario. Seven formal equations underpin the methodology, spanning Granger vector autoregression, F-test inference, AIC-based lag selection, normalised causality scoring, frequency-domain spectral causality, Bayesian posterior propagation, and expected detection lead time.

The recent information and technology developments have impacted data utilization and showed the importance of storing different data types for various purposes. The huge amount of data exchanged between systems through the web, networks, and data storage systems are prone to third-party attacks and demands an effective data security system irrespective of the application. Researchers and developers have secured data using different steganography and cryptography techniques. Steganography uses different mediums to hide sensitive data such as images, videos, text, and audio. This review study discussed the importance of recent trends in steganography and cryptography systems in data security. Various methods and techniques of steganography and their hybrid systems, along with cryptography, have produced efficient results for data security. These methods and techniques are thoroughly reviewed to understand the development of a secure system based on steganography. The image-based steganography systems are widely used in several studies rather than video and audio-based steganographic systems. This paper aims to review different techniques practiced in steganography secure systems and specifically focused on Advanced Encryption Systems, Elliptic Curve Cryptography, and other hybrid systems since they are primarily used among developers and researchers in data security. Overall, developing an efficient security system based on steganography should be resilient to different types of third-party attacks and consider data integrity and data confidentiality to prevent loss of information.

In this manuscript, A compact geometrical configuration and simplified structure of a monopole antenna is given which is functional over an UWB frequency range (3.1GHz to 10.6GHz). The focus of the study is to design a compact and low-cost antenna that can provide an extended impedance bandwidth while maintaining stable and reliable radiation performance suitable for current wireless applications. The proposed design incorporates modifications to both the radiating patch and the ground plane to enhance impedance matching and improve overall radiation performance. Full-wave electromagnetic simulations are conducted to analyse these improvements, and a prototype is fabricated to validate the design experimentally. The measured results closely correspond with the simulated response, confirming wideband operation, consistent radiation patterns, and satisfactory gain levels required for UWB communication. The Proposed antenna design outperforms previous research due to its small size, wide bandwidth and high gain making it an excellent option for UWB systems. Because of its compact footprint, dependable wideband response, and simple fabrication process, the antenna is well suitable for portable and sensing-based UWB applications.

This study explores the rising threat of spear phishing attacks enabled by artificial intelligence (AI) tools like ChatGPT, combined with public data platforms such as Numberbook. By leveraging these technologies, attackers can create highly personalized and convincing phishing messages, drastically improving their success rates compared to traditional methods. This research investigates how AI-generated content enhances the effectiveness of phishing campaigns and proposes a defense framework to combat these advanced threats. The study adopts a multi-faceted approach to cybersecurity, encompassing AI-driven detection models, regulatory measures to limit data exploitation, and comprehensive user education. Survey results indicate that most respondents recognize the effectiveness of AI detection models in identifying phishing attempts. However, the findings also highlight significant gaps in data protection regulations and user awareness programs, which remain critical vulnerabilities. By presenting empirical evidence and practical solutions, this research contributes to the field of cybersecurity, emphasizing the need for advanced detection technologies, stricter regulatory oversight, and enhanced public awareness. The insights offered are pivotal for organizations aiming to fortify defenses against increasingly sophisticated phishing attacks, ensuring a proactive and resilient approach to emerging cyber threats.

This paper presents the automated design and optimization of a compact circular microstrip patch antenna for C-band applications using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Microstrip patch antennas inherently suffer from narrow impedance bandwidth, making systematic optimization essential for wideband wireless applications. The antenna is implemented on an FR4 substrate (24 × 24 mm2, εr = 4.4, h = 1.6 mm) and optimized through ANSYS HFSS using the PyAEDT Python interface. Three key design parameters were tuned to enhance impedance bandwidth and minimize return loss GA achieved the best performance among the considered optimization methods, with an optimized bandwidth of 3.74 GHz and a minimum S11 of –37 dB, while the optimized PSO method reduced computation time by approximately 49% compared to manual tuning and 31% compared to GA. The final optimized design exhibits consistent gain performance (2.3–2.8 dB) and stable radiation patterns across the operational band, confirming reliable C-band operation. The results demonstrate that metaheuristic optimization integrated with HFSS automation provides a powerful and efficient antenna design framework, which can be extended toward hybrid algorithms and intelligent machine-learning-assisted antenna prediction models.

Wireless Sensor Networks have emerged as a key technology enabling real time data collection and monitoring across various domains, including environmental monitoring, industrial control, healthcare, and security applications. However, despite their growing relevance, energy efficiency remains a fundamental design challenge due to the limited power supply of sensor nodes, which directly impacts overall network lifetime and reliability. This paper proposes an Adaptive Energy-Aware Clustering Protocol (EACP) designed to improve energy efficiency and extend the operational lifetime of homogeneous WSNs. The proposed protocol integrates three main mechanisms: Residual Energy-based Cluster Head Selection, to ensure balanced energy distribution; Mobility-Aware Cluster Head Reassignment, to maintain stable communication under node mobility; and Base Station Proximity Based Direct Transmission, which allows nodes near the BS to bypass CHs, thereby minimizing redundant energy use. These mechanisms allow the network to dynamically adapt to changing energy conditions and communication distances. The protocol was evaluated through extensive MATLAB simulations and compared with benchmark protocols including LEACH, HAC, and HSA. Simulation results demonstrate that the proposed EACP significantly improves network performance. Specifically, it achieves 50% to 94% improvement in network lifetime, reduces energy consumption by approximately 20% to 25%, and increases throughput by more than 2.5 times compared to the benchmark protocols. These results demonstrate that EACP offers a scalable, energy-efficient communication strategy well suited for large scale WSNs deployments.

This paper introduces Attribute-Adaptive Noise Injection (AANI), a novel approach to enhance differential privacy in Cyber-Physical Systems (CPS). AANI addresses the privacy-utility trade-off by dynamically adjusting noise injection based on individual data attribute sensitivity, correlation, and utility needs. This tailored approach allows for fine-grained privacy control, adapting to the diverse data generated by CPS components. The paper outlines AANI's framework, proposes efficient algorithms for attribute-specific noise calculation, and demonstrates its effectiveness through simulations. Results show AANI outperforms traditional differential privacy methods by improving both privacy protection and data utility in CPS.

The solution of the modal equation of a planar optical waveguide is a cumbersome job and usually incident angle of successful modes is determined by a graphical solution. In this research work, we applied two computational intelligence methods: Particle Swarm Optimization (PSO) and Genetic algorithm (GA) in a segment-wise approach to solving the modal equation of the tangent function. The motivation for employing Computational Intelligence (CI) lies in its ability to optimize functions without requiring high-level mathematics or complex statistical models, as opposed to traditional analytical methods. This strategic use of computational intelligence significantly reduces the overall computational cost, more nature inspired and probabilistic, providing an efficient alternative. Particularly for functions with complex solutions, the utilization of computational intelligence or soft computing methods becomes imperative to obtain an approximate solution compared to classical numerical optimization methods like Newton-Raphson, bisection etc. that generally deterministic and aim to find the exact optimal solution. In terms of using probability (a core component of chosen algorithm’s searching mechanism) we can incorporate distributions that will enhance the performance. Therefore, while classical root-finding methods are computationally simpler for isolated cases, the use of PSO and GA is motivated by their global search capability, robustness to initialization, and ease of automation, which are advantageous in generalized or large-scale modal solution frameworks. The outcomes derived from both methods (PSO and GA) are meticulously compared with the results obtained through the traditional graphical solution. We have found accuracy of 99.95% for PSO and 99.87% for GA. Notably, the findings reveal a close correlation between the computational intelligence approaches and the graphical method offering a promising avenue for advancing the field with a more computationally feasible approach.

Unregulated accessibility to the latest deepfake technologies presents escalating, unprecedented threats to the personal security, public trust, and democratic integrity, owing to the ever-increasing sophistication and realism of these forgeries. The biggest challenge is the inability of human verification to ascertain the original from the forgeries. Therefore, this research aims to establish an initial framework of detection and verification. The Chromatic Gradient Anomaly Network (ChrGAN) is an architecture that will be built and tested to capture changes of the various components of a video over time in order to reveal patterns of inconsistency between the spatiotemporal levels of a video and the changes of its chromatic components. One of the most important contributions of this research is the analysis of the second order derivatives (in this case, the Chromatic Gradient Fields) of the Spatiotemporal Chromatic Energy Distributions, leaving the synthesis boundary of the temporally sparse flickers and the physically implausible discontinuities of the blend exemplified by the gap. The results for the CrGAN show the highest level of diagnostic confidence, reporting a detection rate of 97.9%, and most importantly a level of pixel-wise localized mapping of the region detected that is statistically differentiated from the other detection models for a state of the art performance measurement in a machine learning model for the detection only. In conclusion, this study validates how targeting the second-order spatiotemporal inconsistencies using chromatic gradients, not only acts as an efficient detection mechanism, but also as an interpretable tool in the combat against digital deception by identifying the how and where of video forgery.

This paper presents an analytical and simulation-based treatment of the far-field radiation characteristics of a Multiple Log-Periodic Dipole Array (MLPA) antenna using Green’s function–based magnetic vector potential formulations. The study consolidates established electromagnetic theory by explicitly combining log-periodic element scaling, cumulative spatial phase delays, and array-level superposition into a single, transparent analytical workflow. The scalar free-space Green’s function is employed to derive the magnetic vector potential, from which far-field electric field expressions are obtained under standard approximations. Radiation characteristics such as half-power beamwidth, peak directivity, and sidelobe levels are extracted from MATLAB-based simulations and compared with representative theoretical LPDA performance ranges reported in the literature. The results demonstrate consistency with expected broadband LPDA behavior and serve primarily to illustrate the applicability of Green’s function methods to hierarchically structured log-periodic arrays. The work is intended as a reproducible analytical reference and pedagogical baseline for MLPA modeling, rather than as a replacement for full-wave numerical solvers or experimental validation.

SQL injection is a hacking attack where malicious code is inserted into database queries through user inputs like search boxes, login forms, or URL parameters. These attacks pose a significant threat to web applications and ERP systems, making early detection crucial. Traditional detection methods, such as rule-based and signature-based approaches, rely on known SQL injection patterns. However, they often fail to identify novel, obfuscated, or zero-day attacks, highlighting the need for more adaptive and intelligent detection mechanisms. This research proposes FusionNetSQL, a fusion-based deep learning model that combines Convolutional Neural Networks, Long Short-Term Memory networks, and Transformers to detect SQL injection attacks. By integrating these architectures, FusionNet-SQL gains a comprehensive understanding of SQL queries, enabling it to differentiate between legitimate interactions and malicious injections. The CNN captures local patterns, the LSTM models sequential dependencies, and the Transformer enhances global context understanding. The model achieves high performance, with 98.02% accuracy, 99.39% precision, 96.79% recall, 98.07% F1-score, and 98.07% AUC-ROC. With its robust performance and adaptability, FusionNet-SQL offers a powerful solution for securing web applications and ERP systems against SQL injection attacks. Its ability to detect both straightforward and sophisticated attacks makes it well-suited for real-world deployment, reinforcing database security and protecting critical data. This research marks a significant step forward in combating evolving cybersecurity threats.

Wireless Sensor Networks (WSNs) are fundamental to security and surveillance applications such as military defense, disaster management, and intrusion monitoring. The performance of these networks depends largely on the efficiency of routing protocols. This paper examines the Ad hoc On-Demand Distance Vector (AODV) routing protocol in multi-hop WSN environments for target tracking, evaluating critical metrics including Packet Delivery Ratio (PDR), End-to-End Delay, Energy Consumption, and Network Lifetime. Simulation results illustrate the impact of node depletion due to transmission loss, affecting network stability and robustness in target detection. In-network detection in WSNs presents trade-offs between real-time data transmission, energy efficiency, and trajectory lifespan. Inefficient routing optimization may result in increased latency, packet loss, and premature node failure, ultimately reducing localization accuracy. While AODV’s reactive path-based approach offers manageable overhead, its performance degrades under increased energy consumption and route rediscovery delays. This study systematically evaluates AODV’s strengths and limitations in time-sensitive detection scenarios. Findings indicate that AODV ensures reliable data transmission in early network stages but suffers significant performance deterioration as node energy declines, impacting coverage and responsiveness. To enhance AODV’s target tracking capabilities, this paper proposes adaptive energy-saving techniques and hybrid routing schemes. These strategies contribute to ongoing research aimed at optimizing routing protocols to balance accuracy and node longevity for real-time WSN applications.

Distributed Denial-of-Service (DDoS) attacks continue to pose a significant threat to digital infrastructures, often resulting in degraded service availability and financial losses. Traditional detection systems, which depend on static rule sets, struggle to adapt to evolving traffic patterns, leading to increased false positives and undetected attacks. This paper presents a real-time, machine learning-based framework for DDoS detection and mitigation. The framework incorporates supervised learning algorithms, including Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), trained on the CIC-DDoS2019 dataset using carefully selected network traffic features to enhance detection accuracy. The system architecture integrates Scapy for traffic capture, Apache Kafka for message queuing, and Flask with Plotly for dynamic monitoring. Evaluation results demonstrate superior performance compared to legacy methods across precision, recall, F1-score, false positive rate (FPR), and false negative rate (FNR). Additionally, adaptive models such as Passive-Aggressive and Stochastic Gradient Descent (SGD) enhance robustness against evolving attack vectors. The proposed solution delivers an effective and scalable real-time defense mechanism suitable for banking, cloud, and enterprise systems. However, the system’s performance remains influenced by the characteristics of the training dataset and may introduce computational overhead during high-throughput traffic analysis. Future work will explore improved computational efficiency and responsiveness to rare or emerging DDoS patterns.

Currently, valuable information requires security because cyber threats have escalated. The CIA Triad is one of the core concepts in information security which defines three main objectives of any security program: Confidentiality, Integrity, and Availability. Each component of the triad covers a different aspect to ensure proper protection and management of information. Confidentiality ensures that sensitive details are only obtained by authorized personnel or corporations. Maintaining integrity involves ensuring data accuracy and reliability by preventing unauthorized changes. Availability guarantees that information and related systems can be used as needed. This paper presents an innovative hybrid form of security system aiming at selecting the best cryptographic and steganographic techniques. Additionally, the Huffman encoding scheme is employed to increase the embedding capacity of the proposed mechanism. Thus, cryptography and steganography are taken as measures in the field of communication and information security. Cryptography and steganography are two different but connected areas within the broader information security domain. Both methods share some common features in securing information although they differ in their functions and performance characteristics. In this paper, an integrated method that combines Cryptography, compression, steganography, and the InterPlanetary File System has been presented as a basis for information security. Consequently, this system is implemented by using a Python Tkinter module that makes it possible to be used in real-life situations without much difficulty. This application enables its users to encrypt messages, compress them, and hide them inside other files like they were of no significance at all. The interface design ensures that users can move around different functions easily without demanding technological expertise or knowledge. A flexible framework is offered through which sensitive data can be secured against various digital landscapes subjecting to current threats in data security. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records. The novelty of this work lies in the unified implementation of ECC-based key exchange, AES-GCM encryption, Huffman compression, LSB steganography, and IPFS decentralized storage in a real-world deployable GUI framework. Unlike prior studies that address these methods separately, this system integrates them into a streamlined pipeline that enhances embedding efficiency, encryption robustness, and secure data retrieval over decentralized platforms. The holistic approach and practical usability make it distinct from existing security models. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined, thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records.