Babylonian Journal of Networking <p style="text-align: justify;">The Babylonian Journal of Networking (EISSN: 3006-5372) focuses on cutting-edge advancements in networking technologies. This specialized publication invites researchers to share high-quality contributions covering computer networks, wireless communication, network security, and the Internet of Things (IoT). Through rigorous review, it aims to disseminate innovative insights and foster collaboration in this dynamic field</p> en-US Sun, 21 Jan 2024 04:05:25 +0000 OJS 60 Evaluation of OFDM system in terms of PAPR and BER using PAPR Reduction Techniques : Windowing and Clipping <p>The issue of transferring signals in a non-intrusive and efficient manner is highly intriguing. Consequently, the utilisation of the Orthogonal Frequency Division Multiplexing (OFDM) transmission technology is employed due to its enhanced resilience against multipath fading and its ability to achieve better efficiency compared to alternative wireless communication methods. Although the PAPR issue offers certain benefits, it poses undesirable consequences for OFDM, leading to a significant decrease in data transmission rate. Consequently, the mitigation of peak-to-average power ratio (PAPR) issues leads to an improvement in the quality of service. The reduction of PAPR can be achieved by the implementation of clipping and windowing techniques. These approaches arrange the signal into discrete levels of similar magnitude and apply clipping to minimise the bit error rate (BER), resulting in a drop in PAPR. The proposed approach is implemented using the MATLAB software.</p> Mounika Siluveru, Dharavath Nanda, Madhavi Kesoju Copyright (c) 2024 Mounika Siluveru, Dharavath Nanda, Madhavi Kesoju Wed, 10 Jan 2024 00:00:00 +0000 Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks <p>This paper is devoted to the use of AI managed to contribute to security of the MANETs (Mobile Ad-hoc Networks), decentralized and mobile wireless networks, that are fully dynamic in nature. The intention of the research is to audit the dangers of cyber and to spot the variety of cyber threats types, including Distributed Denial of Service (DDoS) attacks, malware intrusions, leakages or data breaches, or unauthorized access attempts, using AI-powered algorithms and models. The purpose is to obtain higher degree of veracity of defining and classifying these threats and as result puts more security and reliability to MANET networks. Anomaly detection addressed as a secondary line of defense specific for MANET hardware and network traffic. The monitoring method is needed here to find abnormal behavior that might anyhow signify the possible security flaws or the attacks of the MANET environments. This ultimate goal is penetrated with the timely detection Peculiarities, which makes possible to reinforce MANET security capabilities that require to be well-developed against cyber threats. Experimental results reveal a clear trend of Fleet Grid Algorithm Improvements along with Detection Accuracy (Digital Signals and Anomaly) by means of training AI models (CNN and RF) with algorithms like Random forest and Convolutional neural networks. The machine learning based algorithms often present remarkable results comprising efficiency in detecting and effectively categorizing different cyber threats existing such as DDoS attacks, malware infiltrations and attempted unauthorized access. This method of anomaly detection is able to accurately detect robot anomalies and malicious activities in network traffic in addition to we preventing system vulnerabilities or threats from occurring prematurely. Besides, the findings of this study wide relatively efficient AI-based cybersecurity systems for dynamic decentralized MANET systems, which are developed for street-view switching and path finding, self healing and self configuration.</p> Rasha Hameed Khudhur Al-Rubaye , AYÇA KURNAZ TÜRKBEN Copyright (c) 2024 Rasha Hameed Khudhur Al-Rubaye , AYÇA KURNAZ TÜRKBEN Tue, 30 Apr 2024 00:00:00 +0000 Mining Utilities Itemsets based on social network <p>Mining utility item sets based on social network data involves extracting meaningful patterns and associations from user interactions. In this paper, the process begins by collecting and preprocessing data from platforms like Facebook, Twitter, or LinkedIn. Utility measures are defined based on frequency of occurrence, user engagement metrics, or other domain-specific criteria. Itemsets that meet certain thresholds are identified using techniques like frequent itemset mining or advanced algorithms like Apriori or FP-growth. Additional analyses, such as association rule mining, uncover relationships between different itemsets or user segments, providing valuable insights for personalized recommendations, targeted advertising, and decision-making processes.</p> <p>&nbsp;</p> Sara salman Qasim, Lubna Mohammed Hasan Copyright (c) 2024 Sara salman Qasim, Lubna Mohammed Hasan Sun, 03 Mar 2024 00:00:00 +0000 Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review <p>Distributed denial-of-service assaults, often known as DDoS attacks, pose a significant danger to the stability and security of the internet, particularly in light of the increasing number of devices that are linked to the internet. Intelligent detection systems are absolutely necessary in order to lessen the impact of distributed denial of service assaults. In this study, a comprehensive overview of recent research on intelligent approaches, such as Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI), is presented. The review focuses on the application of these techniques in the detection of Distributed Denial of Service (DDoS) assaults. In addition to providing a taxonomy and conceptual framework for DDoS mitigation, the study places particular emphasis on the application of time series data analysis for the detection of distributed denial of service attacks. A number of different intelligent techniques are investigated in this paper. Some of these techniques include clustering, deep reinforcement learning, graph neural networks, support vector machines, and others. For the purpose of performance evaluation, real datasets are utilized, and prospective future research areas in this area are explored.</p> Sara salman Qasim, Sarah Mohammed NSAIF Copyright (c) 2024 Sara salman Qasim, Sarah Mohammed NSAIF Sat, 20 Jan 2024 00:00:00 +0000 Integration Between Network Intrusion Detection and Machine Learning Techniques to Optimizing Network Security <p>In an increasingly linked world beset with cybersecurity risks, the necessity for powerful intrusion detection systems (IDS) is paramount. This thesis proposes a fresh approach to IDS development. using modern ma-chine learning algorithms and feature selection techniques to boost detection accuracy and resistance. Draw-ing upon lessons from earlier research, we address fundamental flaws in existing IDS approaches. emphasis on scalability and susceptibility to advanced assaults. Our suggested hybrid model, incorporating Random Forest, Gradient Boosting Machines, and Neural Networks, obtains a remarkable accuracy rate of 96% in identifying network intrusions. Utilizing the Intrusion Detection Evaluation Dataset (CIC-IDS2017), Our trials illustrate the efficacy of the proposed technique in real-world circumstances. This research contributes to the evolution of cybersecurity techniques by delivering practical insights for strengthening the security and resilience of digital infrastructures.</p> Khalid Elzaridi, Sefer Kurnaz Copyright (c) 2024 Khalid Elzaridi, Sefer Kurnaz Sun, 05 May 2024 00:00:00 +0000 Using a Fuzzy Approach as an Assessment Method to Extend the Lifespan of Wireless Sensor Networks using the LEACH Protocol <p>Wireless sensor network is the term used to describe a network where network nodes are wirelessly configured to collect data from the real world. Node sensors depend on finite energy sources, such as batteries, because of the wireless configuration they have. If the battery-operated sensor of the node is not charged, it will be unable to carry out its intended function. If a specific amount of nodes fail, the network will cease to function. Several energy-efficient protocols were developed for Wireless Sensor Networks (WSN), including the LEACH Protocol. The LEACH protocol demonstrates a single cluster-based protocol by dividing available sensor nodes into sets and interacting with each set individually. The shape of an energy can be altered by compressing or expanding it, based on the cluster's configuration. We are comparing the network lifespans of three distinct versions of the LEACH protocol that utilize fuzzy techniques for cluster selection with the lifespan of WSNs generated by a previous version of the protocol.</p> Janan Farag Yonan, Ayser Hadi Oleiwi Copyright (c) 2024 Janan Farag Yonan, Ayser Hadi Oleiwi Sun, 10 Mar 2024 00:00:00 +0000 A ELECTRIC VEHICLE BLOCKCHAIN: PROBLEMS AND OPPORTUNITIES <p>These days, we are observing a very rapid spread of the electric vehicle<br>industry. This means a significant increase in the data and energy exchanged between<br>these vehicles. The existing centralized approach is less secure and more vulnerable<br>to data destruction and manipulation by intruders. Therefore, it became necessary to<br>search for an alternative that provides excellent protection for this massive amount<br>of data and energy. Although blockchain technology and cryptocurrencies are closely<br>associated, they also have many other potential applications in fields including energy<br>and sustainability, the Internet of Things (IoT), smart cities, smart mobility, and<br>more. In the Internet of Vehicles (IoV) idea, blockchain can provide security for<br>electric vehicle (EV) transactions, enabling electricity trading to be carried out in<br>a decentralized, transparent, and secure manner. . This paper will explain the use of<br>blockchain in this field and how it can handle the trade of transmitted and received<br>energy between electric vehicles. The advantages of using blockchain with electric<br>cars and how it can secure the transactions of energy trading will be shown too. A<br>group of researchers in this field and the challenges that face this technology in energy<br>trading will be discussed too; the studies will be looked at, and recommendations for<br>investments and security will be made. Additionally, the future implications of various<br>blockchain technologies will be highlighted.</p> sahar Mohammed, Thaaer kh.Asman, Hadeel M Salih, Alaa Mohammed Mahmood Copyright (c) 2024 sahar Mohammed, Thaaer kh.Asman, Hadeel M Salih, Alaa Mohammed Mahmood Mon, 19 Feb 2024 00:00:00 +0000