Enhancing the Performance of Cryptographic Hash Function Using 2080 Bits Proposed Secure Hash Algorithm 160

An on-way hash code or message authentication code is generated using the cryptographic hash functions. It used to be password storage, electronic data integrity, and check verification. Cryptographic hashing algorithms, which employ beginning value and key constant to boost algorithm complexity, have been proposed by a number of academics. It is well known that they have a very high temporal complexity due to the quantity of steps and memory space needed to store the beginning value and key constants. Consequently, we are improving the cryptographic hash function\u27s performance by using 2080 bits as a block of the input message and avoiding the need for the key constant. By doing this, we are generating 160-bit fixed-length hash code, and the amount of time spent on the function proposal will be reduced in comparison to previous hash algorithms. The outcome will be compared using the amount of time in seconds that the cryptographic hash algorithms consumed during computation

Real-Time Intrusion Detection in Controller Area Networks: An Evaluation of Current Methods and Future Directions

Controller Area Networks (CANs) are critical components of modern vehicles and industrial systems, facilitating communication between various electronic control units. However, the widespread connectivity and lack of inherent security measures make CANs vulnerable to cyber-attacks. Intrusion detection systems (IDS) safeguard CANs by detecting and mitigating potential attacks. This paper presents a comprehensive analysis of current methods for the real-time detection of attacks in CANs. The IDSs based on different input data modalities are evaluated based on their effectiveness, accuracy, and efficiency. The analysis highlights the strengths and limitations of each method, providing valuable insights for researchers and practitioners in developing robust and reliable intrusion detection systems for CANs. The findings suggest that the lightweight strategy in IDS is widely accepted for real-time application due to its computational simplicity and model structure. Furthermore, the paper identifies future directions to enhance the security of CANs and ensure their resilience against evolving threats

Grey Wolf Optimizer with Multiple Objectives for Wireless Network Base Station Location for Optimal Coverage

This study proposes a Grey Wolf Optimizer (GWO)-based framework for optimal base station (BTS) placement in wireless networks, minimizing infrastructure costs while maximizing coverage and Quality of Service (QoS). A multi-objective function simultaneously addresses: (1) minimal BTS deployment, (2) population coverage maximization, and (3) call failure reduction via reserved channel allocation. We introduce a novel binary-array solution encoding scheme and a weighted fitness function for GWO. Simulations across randomized and grid-based scenarios demonstrate superior performance over Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), reducing BTS nodes by 25–30% and call failures by 50–60%. The framework offers a scalable solution for 5G/6G network planning

Privacy-Preserving Deep Reinforcement Learning for Secure Resource Orchestration in Cyber-Physical Systems

This research addresses the critical challenge of secure and efficient resource allocation in Cyber-Physical Systems (CPS) by introducing a Deep Reinforcement Learning (DRL) framework integrated with privacy-preserving federated learning. Unlike traditional methods, our approach ensures that raw data remains localized, thereby mitigating privacy risks and enhancing trust within the CPS ecosystem. A custom-designed reward function is proposed to optimize both resource utilization and privacy assurance, balancing performance and security goals. To strengthen data confidentiality, we incorporate a variant of Differential Privacy, which increases the privacy budget without significantly compromising data utility—achieving a privacy guarantee of 0.8 while maintaining over 92% model accuracy. Experimental validation on a smart grid test bed demonstrates the efficacy of the proposed model, achieving a 17.6% improvement in resource allocation efficiency, a 23% reduction in communication overhead, and a 12% increase in system throughput compared to baseline DRL models without privacy constraints. Overall, the framework demonstrates state-of-the-art performance in optimizing resources in complex, distributed CPS environments while upholding stringent privacy requirements. The proposed method offers a scalable and secure solution for next-generation CPS applications in smart infrastructure

Evaluating the Impact of Denial-of-Service (DoS) Attacks on Enterprise Networks Using Optimized Network Engineering Tools (OPNET 14.5) and Machine Learning

The research conducts a network performance analysis of enterprise systems under Denial-of-Service (DoS) attacks through machine learning modeling with OPNET 14.5. Service interruptions along with financial losses result from Denial-of-Service attacks which seriously reduce network performance. The implementation of multiple defense measures has not resolved the persistent problem with real-time detection and response for enterprises. Through OPNET 14.5 simulation the research evaluates multiple DoS attack situations alongside their effects on performance metrics by measuring latency and achieving throughput and packet loss statistics. Two machine learning models with decision trees and support vector machines serve to detect normal and attack-related traffic patterns. The simulation demonstrates that networks experience severe degradation when under DoS attacks which leads to longer delays and packet drops. The machine learning detection systems show excellent attack pattern recognition abilities which indicates their practical use in preventing attacks. The authors suggest security frameworks should implement machine learning detection systems as part of their enterprise security infrastructure for better DoS protection. The research provides final proof about integrating network simulation along with machine learning technologies to stop DoS attacks which will enable further cybersecurity defense system development

Implementation of a CNN-Based Model for Soybean Leaf Diseases

Soybean ranks among the most vital crops grown in India, especially in Madhya Pradesh, where it significantly contributes to the agricultural economy and supports nutritional security. Nonetheless, the yield of soybean crops is greatly affected by several leaf diseases, including bacterial blight, downy mildew, soybean rust, southern blight, and powdery mildew. Timely and precise detection of these diseases is crucial to reduce crop damage. Conventional disease identification techniques are often slow, require considerable manual effort, and are susceptible to human error. To address this issue, this study proposes the implementation of a Convolutional Neural Network (CNN)-based deep learning model to detect and classify common soybean leaf diseases using image data. A total of 5,917 images were utilized in the dataset, divided into five disease categories and one healthy category. The dataset was preprocessed and augmented to enhance model performance and divided into training (70%), validation (10%), and testing (20%) sets. The CNN model was trained over 20, 40, and 50 epochs to assess its performance across varying training durations. It demonstrated high classification accuracy, highlighting its effectiveness as a dependable method for the early detection of soybean leaf diseases

Sustainable AI and Green Computing: Reducing the Environmental Impact of Large-Scale Models with Energy-Efficient Techniques

Artificial intelligence (AI) has become an integral part of modern technology, driving advances across numerous sectors, including healthcare, finance, transportation, and entertainment. However, the rapid growth in AI model complexity particularly the rise of large language models has sparked concerns over their substantial energy consumption and associated carbon emissions. This paper explores the intersection of green computing and sustainable AI, focusing on the carbon footprint of large-scale models, energy-efficient algorithmic solutions, and emerging tools and frameworks designed to measure and mitigate environmental impact. We review current approaches such as model pruning, quantization, knowledge distillation, and efficient hardware, and discuss prominent tools like CodeCarbon and Carbontracker that enable researchers to track and reduce emissions. The paper also highlights ongoing challenges related to standardization, transparency, and policy, while outlining future research directions for creating an environmentally responsible AI ecosystem. By advancing sustainable AI practices, the research community can align innovation with environmental stewardship, ensuring that technological progress supports global climate goals

DC-T : Data Transfer between Data centers using Elastic Optical Fiber Considering Path failure

The DC-T algorithm is proposed to optimize inter-data center traffic in optical fiber networks, emphasizing survivability, fault tolerance, and efficiency. The primary objectives include minimizing the number of active data centers while maintaining network resiliency, dynamically selecting paths based on latency and failure probability, and recovering data through redundancy mechanisms such as erasure coding. By integrating the Minimization of Data Centers in Survivable Dynamic SDM-EONs technique, DC-T enhances resource utilization and ensures scalable performance across diverse network topologies. The algorithm is specifically designed to improve data transfer efficiency by dynamically rerouting traffic, reducing latency, and maximizing throughput. The proposed approach is evaluated across three major network infrastructures—COST239, NSFNET, and USNET—demonstrating its superiority over existing methodologies. Experimental results indicate that DC-T effectively balances network efficiency and fault tolerance, outperforming traditional techniques in ensuring seamless and resilient data transfer. This work contributes to the advancement of survivable optical data center networks by providing a cost-effective and adaptive solution to dynamic traffic management

A Security Based Perspective of Internet of Things

Information technology is offering many technologies to all of us and among such systems and technologies IoT, Big Data, Cloud Computing etc. are considered as important and vital. The advancement and escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives by different sorts. The deployment of a large number of objects adhered to the internet has unlocked the vision of developing Digital Society and simply smart world around us, thereby paving a road towards automation and humongous data generation and collection. This intelligent Internet systems supported by the automation and continuous explosion of information to the digital world provides a healthy ground to the adversaries to perform numerous IT based Services and making our lives easy and it also helps in adhering cyber systems and information enriched society. The Security related aspects are important in emerging systems and here IoT based systems play a perfect role. Timely detection and prevention of such threats are pre-requisites to prevent serious consequences. Here in this work the survey conducted provides a brief insight into the technology with core attention to various attacks and anomalies including their detection based on the intelligent intrusion detection system(IDS). Further here comprehensive look-presented which provides an in-depth analysis as well as assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Moreover in this work aspects of healthcare in IoT is presented. This study also deals about the architecture, security, and privacy issues including their utilizations of learning paradigms in this sector. The research assessment here finally concluded by the listing of the results derived from the knowledge sources and literature. The paper also discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.&nbsp

HSSA Framework for Secure and Efficient Spectrum Allocation in SDM-EONs

Space Division Multiplexing-Elastic Optical Networks (SDM-EONs) have emerged as a viable solution to address the exponential growth in data traffic by enhancing spectral efficiency and network scalability. However, spectrum allocation in SDM-EONs presents significant challenges, including spectral fragmentation, latency overhead, and security vulnerabilities. Traditional spectrum allocation methods, such as First Fit (FF) and Machine Learning (ML)-based techniques, fail to effectively integrate security constraints into the allocation process, making networks susceptible to attacks such as eavesdropping, jamming, and route hijacking. This paper introduces a Heuristic Secure Spectrum Allocation (HSSA) algorithm, which employs a multi-metric optimization framework incorporating attack probability, network reliability, and spectrum availability to enhance security-aware spectrum assignment. The proposed method utilizes a modified Dijkstra’s algorithm to compute optimal paths with a security-centric weight function, ensuring minimal fragmentation and efficient spectrum utilization. Extensive simulations on USNET and COST239 network topologies validate the efficiency of HSSA, demonstrating a 95% spectrum utilization rate, 30 ms latency, and enhanced security robustness compared to conventional approaches. The results substantiate the efficacy of HSSA in mitigating spectral inefficiencies and cyber threats while maintaining high resource utilization. Future research will focus on integrating AI-driven dynamic spectrum adaptation, cryptographic security enhancements, and energy-efficient spectrum assignment strategies to further improve SDM-EON performanc