ASPG

An Improved K-Means Clustering Process Solicitation for Mine Blood Donors Information

Anil Audumbar Pise , Ganesh Shivaji Pise , Saurabh Singh , Hemachandran K. , Jude Imuede , Sandip Shinde

The exponential rise in accidents and the introduction of new, supposedly trendy ways of living have contributed to the dire need for the needy to have an organ or blood transfusion. Circumstances refer to a circumstance where proper care should be taken when collecting the necessary blood or original parts for transfusion, typically in dire circumstances. To determine the distance at which the interested and qualified donors are located, a thorough investigation must be conducted. People are often first categorized according to their blood type, eligibility, and region. Following that, people group together according to locality. A healthy person can safely donate blood twice within 56 days, as this is the minimal time between successful donations that has been established as a norm. The decision to donate an organ is often made after careful consideration of the severity of the situation, the donor's health, and the health of the recipient. Knowledge data finding tasks can be made easier with the help of KEEL, an open-source programme. The graphs that are produced show clearly how the proposed algorithm varies from the standard K-means method. Therefore, it will be quite useful in the present day and could end up saving lots of lives. The necessity to decide ahead of time on the total number of groups is just one of the issues with the K-means clustering method. In practise, it is difficult to anticipate the precise number of clusters. When the number of clusters is small, incongruous clustering is more common, but when the number of clusters is large, like clustering is more common. Thanks to a method called Active Cluster with Modified k-means clustering, which finds the right number of clusters on the fly, the issue is now resolved.

Vol. 13 Issue. 1 PP. 08-16, (2024)

Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance

L. Bhagyalakshmi

  The SecureDigitalGuard framework gets recognition for its all-encompassing strategy, which combines strict consumer data protection laws with state-of-the-art security safeguards with ease. This all-encompassing approach is designed to guarantee the longevity of digital marketing in the face of constantly changing cyberthreats. This cutting-edge system is based on three key strategies: the Behavioural Threat Detection (BTD) algorithm, the Adaptive Access Control (AAC) algorithm, and the Homomorphic Privacy Guard (HPG) programme. The vital task of dynamically controlling user access levels in response to continuing risk evaluations is taken on by the AAC algorithm. This dynamic control technique improves the framework's capacity to adjust to constantly shifting security circumstances. However, the BTD algorithm is proactive in spotting abnormalities in user behaviour, allowing for quick reactions to any dangers. The SecureDigitalGuard architecture gains an additional degree of protection from this preventive method. In addition, the HPG programme is responsible for doing analytics while maintaining user privacy. This careful approach shows a dedication to finding a fine balance between user protection and data analysis, making sure the framework complies with the strictest privacy regulations. Test results provide empirical evidence that SecureDigitalGuard is effective and that it can keep up with the dynamic and often changing nature of cyber threats. As a result, the architecture makes traditional cybersecurity techniques outdated. In an increasingly complex and dynamic cybersecurity world, SecureDigitalGuard provides a strong solution for protecting digital marketing through the seamless integration of state-of-the-art technology and strict adherence to privacy regulations.

Vol. 13 Issue. 1 PP. 17-27, (2024)

A Context-Aware Internet of Things (IoT) founded Approach to Scheming an Operative Priority-Based Scheduling Algorithms

Vandana Roy

  In recent years, smart computing has emerged as a promising and rapidly expanding field of technology. It senses the environment in real time and gives powerful analytics to perform intelligent decisions. Creating a scheduling algorithm based on priorities in order to decrease IoT process latency was the primary emphasis of the study challenge. The constraints of existing scheduling algorithms were investigated in order to build a scheduling algorithm that is based on priorities. We provide a context-based priority scheduling method to get around these restrictions. In order to determine which steps of the IoT process were crucial, we developed context attributes. Once the criticality has been identified, the proposed scheduling technique is used to schedule the IoT processes. The outcomes of the algorithms were confirmed using a variety of evaluation indicators. As demonstrated by the experimental results, the suggested scheduling algorithms outperformed the state-of-the-art techniques. Smart ATM uses a Case Study technique to analyse the algorithm. We identified the sensors that are part of the ATM and the settings in which they are relevant. We determined the priority value for each sensor. The processes are subsequently categorized according to their priority values. Then, a priority-based FCFS scheduling algorithm is used, and its performance is assessed using metrics like Average TAT, Cost, Energy, and the High critical process TAT ratio.

Vol. 13 Issue. 1 PP. 28-35, (2024)

Pioneering Security: A Hybrid Logic Framework for the Future of IoT Protection

Rajeev Shrivastava

In a complicated situation, the Internet of Things simplifies the process of connecting a wide range of things. Because of its openness and lack of human control, the Internet of Things is open to assaults like denial of service (DoS) and man-in-the-middle attacks. Furthermore, any device that can connect to the internet may be hacked. These attacks put the network connections and the actual equipment at danger. IoT security and privacy will so be compromised. Due to its limited power, bandwidth, and storage, the Internet of Things requires a security solution that does not overload it. This study aims to preserve consumers' trust in the Internet of Things (IoT) by safeguarding their data against unauthorised access and maintaining the privacy of their personal information. With the ultimate objective of presenting a unique hybrid and optimised lightweight logical security architecture to ensure data privacy and integrity while making effective use of available resources, all research are carried out with this purpose in mind. The Hybrid Lightweight Security Framework (HLSF), which this study suggests, offers secrecy and integrity in addition to authentication. The structure consists of three distinct steps. The first step is registration, the second is authentication, and the third is transit security, which protects data while it is being transported. Compared to other current frameworks, the results reveal that HLSF performs better in terms of precision, recall, and accuracy when applied to an IoT situation.

Vol. 13 Issue. 1 PP. 36-45, (2024)

Utilizing Asymmetric Cryptography and Advanced Hashing Algorithms for Securing Communication Channels in IoT Networks Against Cyber Espionage

Anil Audumbar Pise , Saurabh Singh , Hemachandran K. , Shraddhesh Gadilkar , Zakka Benisemeni Esther , Ganesh Shivaji Pise , Jude Imuede

This article describes a massive cryptographic scheme that can safeguard IoT communication paths. A combination of algorithms makes the technique operate. Communication security is handled differently by each algorithm. Elliptic Curve Cryptography (ECC), SHA-256 Secure Data Hashing, HMAC Message Authentication, and Merkle Tree Structures Decryption and Verification are used. Ablation is used to determine how each strategy increases security. The paper emphasizes that the algorithms function effectively together, demonstrating their importance for cyberdefense and surveillance. The recommended strategy is evaluated and found to operate better across key parameters.Top of Form

Vol. 13 Issue. 1 PP. 46-59, (2024)

Optimizing Intrusion Detection Mechanisms for IoT Network Security

Ahmed Aziz , Sanjar Mirzaliev

The ubiquity of interconnected devices within the Internet of Things (IoT) paradigm has revolutionized modern connectivity, simultaneously amplifying the susceptibility of networks to diverse security threats. This study addresses the pressing necessity for robust intrusion detection mechanisms tailored for IoT networks. Utilizing a simulated dataset reflecting a spectrum of network intrusions within a military environment, the research employs sophisticated methodologies, notably harnessing Decision Tree (DT) algorithms optimized via Grey Wolf Optimization (GWO) for hyperparameter tuning. The investigation meticulously evaluates and refines intrusion detection mechanisms, emphasizing the pivotal role of feature importance analysis in fortifying network security. Results demonstrate the efficacy of the optimized DT algorithm in the precise classification of network traffic, illuminating key attributes instrumental for intrusion detection. These findings underscore the significance of adaptive and interpretable detection strategies in mitigating evolving threats within IoT networks, advocating for resilient approaches to bolster network security.

Vol. 13 Issue. 1 PP. 60-68, (2024)

A Comprehensive Approach to Cyberattack Detection in Edge Computing Environments

Khder Alakkari , Alhumaima Ali Subhi , Hussein Alkattan , Ammar Kadi , Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy

This research is concerned with the critical domain of cybersecurity in edge computing environments, which aims to strengthen defenses against increasing cyber threats that target interconnected Internet of Things (IoT) devices. The widespread adoption of edge computing introduces vulnerabilities that necessitate a strong framework for detecting cyberattacks. This study utilizes Long Short-Term Memory (LSTM) networks to present a comprehensive approach based on stacked LSTM layers for detecting and mitigating cyber threats in the dynamic landscape of edge networks. Using the NSL-KDD dataset and rigorous experimentation, this model demonstrates its ability to detect subtle anomalies in network traffic, which can be used to accurately classify malicious activities while minimizing false alarms. The findings highlight the potential of LSTM-based approaches to enhance security at the edge, providing promising avenues for strengthening IoT ecosystems’ integrity and resilience against emerging cyber threats.

Vol. 13 Issue. 1 PP. 69-75, (2024)

Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization

Ahmed Mohamed Zaki , Abdelaziz A. Abdelhamid , Abdelhameed Ibrahim , Marwa M. Eid , El-Sayed M. El-Kenawy

The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusion process. Through an iterative process of incremental adjustments and evaluations, the algorithm fine-tunes its parameters, resulting in a refined KNN regression model. The enhanced model exhibits substantial improvements, as indicated by the notable reduction in root mean square error (RMSE) and mean absolute error (MAE), accompanied by a strengthened correlation between variables. The favorable outcomes underscore the efficacy of the SFS-PSO optimization technique in augmenting the KNN algorithm's performance within wireless sensor networks. In simpler terms, the application of SFS-PSO in conjunction with KNN leads to a significant decrease in RMSE, reaching a value as low as 0.00894, demonstrating the notable effectiveness of this optimization approach in refining the predictive capabilities of the KNN algorithm in the context of WSNs.

Vol. 13 Issue. 1 PP. 76-84, (2024)

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