Metaheuristic Optimized Ensemble Model for Classification of SMS Spam in Computer Networks

Mohamed; El-Sayed M.; Abdelhameed; Marwa M.; Abdelaziz A.

doi:https://doi.org/10.54216/IJWAC.060205

International Journal of Wireless and Ad Hoc Communication | 2023

Cyber Attack Detection in Wireless Adhoc Network using Artificial Intelligence

A wireless sensor network, also known as a WSN, is made up of thousands of minuscule sensor nodes that are connected to one another in order to monitor, track, and organize data collected in an unattended environment in the most prominent location. Due to its one-of-a-kind qualities, it has, the wireless sensor network is gaining traction in a variety of sectors and put to use in a wide range of applications, including surveillance, healthcare, and industry. These networks exposed to a variety of security flaws and major threats because of their dynamic design and deployment in an unsupervised environment. Cybercriminals prey on individuals who utilize the internet as well as organizations in order to get sensitive information. The hackers were able to access critical data on the company's systems, such as login information, credit card details, and bank account numbers. Phishing attacks are a sort of cyberattack in which hackers trick internet users into believing their websites are authentic in order to collect the users' private information. The purpose of these attacks is to steal this information. Malware assaults begin with the covert installation of malicious software on corporate servers or user PCs via the use of the internet. The attackers then continue to steal every piece of information that kept on the targeted server or computer. Malware used in an ever-increasing number of attacks these days. An incursion into a network is a kind of attack in which the perpetrator seeks to take possession of all of the network's resources. Approaches based on heuristic analysis and visual resemblance used, regardless of whether they are blacklisted or whitelisted.

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Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/IJWAC.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2023

A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks

It is possible to improve software quality by anticipating fault location through the utilization of software metrics within fault prediction models in network. This article provides a comprehensive literature review on the topic of software fault forecasting. The paper also seeks to identify software metrics and evaluate how applicable those metrics are to the process of software fault prediction. It is recommended that additional research be conducted on large industrial software systems to identify metrics that are more pertinent for the industry and to find an answer to the question of which metrics should be employed in a particular setting.

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Preeti Baderiya mail -

Chetan Gupta mail -

Shivendra Dubey mail

link https://doi.org/10.54216/IJWAC.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2023

FLC-NET: Federated Lightweight Network for Early Discovery of Malware in Resource-constrained IoT

In the past few years, billions of Internet of Things (IoT) devices that lacked adequate security procedures were created and deployed, and more of these devices are on the way as a result of the development of Beyond 5G technologies. Because of their susceptibility to malware, there is a pressing need for reliable methods that can identify infected IoT devices within networks. Precise and early identification of IoT malware is inevitable to achieve IoT security. Nevertheless, prevailing studies of IoT malware detection mostly support certain platforms, need complicated deep learning (DL) models to achieve efficiency, and are centrally trained on the device. The purpose of this study is to introduce a new Federated Learning (FL) Framework, which has been given the name FLC-NET, in order to train numerous distributed edge devices to identify malware cooperatively. After the malware binaries have been encoded into image representations using FLC-NET, a lightweight convolutional network known as LC-NET is introduced to model these malware patterns directly from the image data without any data engineering being required. Because of its lightweight design, LC-NET is suited for use in devices with limited resource availability. After that, sophisticated adversarial training will be offered on FLC-NET in order to collect defensive knowledge against adversarial samples from a variety of clients who will be participating. The FLC-NET is experimentally evaluated on the public malware dataset, and it is demonstrated efficient (Accuracy: 96.1%, f1-score: 95.5), effective, scalable, and resistant to adversarial attacks.

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Denis A. Pustokhin mail -

Irina V. Pustokhina mail

link https://doi.org/10.54216/IJWAC.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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American Journal of Business and Operations Research |

Traffic Rule Violation and Accident Detection using CNN

Traffic rule violations and accidents are major sources of inconvenience and danger on the road. In this paper, we propose a convolutional neural network (CNN) based approach for detecting these events in real-time video streams. Our approach uses a YOLO-based object detection model to detect vehicles and other objects in the video, and an IOU-based accident detection module to identify potential accidents.We evaluate the performance of our approach on a large dataset of traffic video footage and demonstrate its effectiveness in detecting traffic rule violations and accidents in real-time. Our approach is able to accurately detect a wide range of traffic rule violations, including wrong-side driving, signal jumping, and over speed. It is also able to accurately track the movements of objects in the video and to identify potential accidents based on their trajectories.In addition to detecting traffic rule violations and accidents, our approach also uses an ANPR module to automatically read the license plate numbers of detected vehicles. This allows us to generate e-challans and punishments for traffic rule violations, providing a potential deterrent to future violations. Overall, our proposed approach shows promise as a tool for detecting and preventing traffic rule violations and accidents in real-time surveillance systems. By combining powerful object detection and motion analysis algorithms with an ANPR module, it is able to accurately and efficiently identify traffic rule violations and accidents, providing valuable information for traffic management and safety.

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Journal of Intelligent Systems and Internet of Things | 2022

Effective Drive an Autonomous Vehicle, The Environment Characteristics Are Extracted Via Intelligent Image Processing

With the development of image handling technology, computerized technology, and the theory of image preparation, it has become clear that image processing is a crucial area of computer application. It is frequently used in many logical and designing applications, such as remote detection, medicine, meteorology, exchanges, and so on.  However, with the swift development of picture preparation technology, it is becoming more and more important to precisely and successfully evaluate the quality of a picture.  Recently, image quality evaluation has grown in importance as a study area in the field of developing picture data, which has attracted a lot of attention from academics.  The importance of picture quality primarily takes into account two aspects: picture loyalty and picture coherence.  picture quality directly depends on depending on the optical characteristics of the imaging equipment, image contrast, instrument clamor, and other factors.  It may provide checking intentions to depict gaining, handling, and various connections through quality assessment.  The evaluation of image quality assessment has become one of the essential breakthroughs of picture data designing to create a meaningful assessment of all components of picture preparation.  People have needed to learn picture loyalty and the understandability of the quantitative estimation strategy using the picture a lot framework plan as the assessment premise for a very long time, but one of the people on the human visual characteristics is still not fully understood, in particular the description methods of psychological characteristics in human vision is also difficult to learn the quantitative evaluation of image quality, so, extensive investigation is required.

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M. Sumithra mail -

G. Naveen Sundar mail -

B. Buvaneswari mail -

K. Sridharan mail -

V. D. Ambeth Kumar mail

link https://doi.org/10.54216/JISIoT.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Survey on Design of Digital FIR Filters using Optimization Models

As the discipline of Digital Signal Processing develops, digital filters play an increasingly vital role in modern technology (DSP). The FIR filter, which stands for "finite impulse response," is the most common type of filter. As a result of its versatility, FIR filters find widespread application in many fields, including image filtering, frequency modulation, precision arithmetic, and many more. For this reason, digital FIR filters are designed using various optimization techniques. Using various optimization strategies yields the best results when optimizing for different filter coefficients (concerning control parameters, dependence, premature convergence, etc.). They're advantageous due to several factors, including their straightforward implementation, low error function, high-quality searching ability, and rapid convergence. In this paper, we have covered the topic of designing efficient digital filters for signal, image, and video processing using various optimization techniques.

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Mohamed Saber mail -

Mohamed E. Ghoneim mail -

Sunil Kumar mail

link https://doi.org/10.54216/JAIM.020102

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Energy Efficiency Modeling Using Whale Optimization Algorithm and Ensemble Model

machinery enterprises can benefit greatly from including energy efficiency models into their energy management and conservation efforts. Due to a lack of theoretical formulations, this paper integrates machining parameters and configuration parameters into energy efficiency models, with ML methods applied to increase generality. A three-year data set from a manufacturing facility serves as the basis for a comparison examination of two scenarios, with an emphasis on evaluating forecast precision, stability, and computing efficiency. To estimate future energy efficiency in Scenario 1, only cross-sectional data is utilized, completely discounting the wear and tear on spindle motors and cutting tools. In this study, we use five error measures to compare and contrast three classic ML algorithms: artificial neural networks, support vector regression, and Gaussian process regression. In Case 2, we build the a voting ensemble model in a more realistic setting, taking into account the dynamic characteristics of the aging spindle motor and tool wear. It is clear from the comparison that all of the Case 1 models experience performance erosion, but the proposed voting ensemble model is able to produce a sustainable increase in accuracy.

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Adel Oubelaid mail -

M. Y. Shams mail -

Mostafa Abotaleb mail

link https://doi.org/10.54216/JAIM.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Classification of Student Performance Based on Ensemble Optimized Using Dipper Throated Optimization

Forecasting student performance, sorting students into groups according to their strengths, and working to improve future test scores are all crucial for any institution in today's competitive world. It is important to give students ample notice before a school year begins if they are to be coached to improve their grades by focusing on a certain subject area. Examining this can helps a school significantly reduce its dropout rate. This analysis predicts how well students will do in a given course based on how they did in previous, similar courses. Discovering previously unknown relationships among vast stores of data is the goal of data mining. Insights and forecasts might be gained from these recurring structures. The term "education data mining" describes the assortment of data mining programs used in the educational sector. The primary focus of these tools is on analyzing the information gathered from classrooms and educators. Potential applications of this research include classification and forecasting. It looks into several machine learning methods, including Naive Bayes, ID3, C4.5, and SVM. The experimental analysis uses data collection containing UCI machinery students' grades and other outcomes. Accuracy and error rate are two metrics used to evaluate algorithms.

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Marwa M. Eid mail -

Rokaia M. Zaki mail

link https://doi.org/10.54216/JAIM.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Credit Card Clients Classification Using Hybrid Guided wheel with Particle Swarm Optimized for Voting Ensemble

Credit card use is rapidly increasing as a result of the widespread availability of these cards, the ease of making electronic transfers, and the ubiquity of online shopping. But credit card debt poses a serious risk to businesses and governments alike, not to mention individual savers and investors. Consequently, the need for efficient, timely, and reliable ways to anticipate credit card risk has grown. In this study, we offer a framework that combines three classifiers, namely, support vector machines, multilayer perceptron and decision trees, to improve the network's accuracy. The proposed strategy is shown to be very competitive with others through simulation.

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Khadija Shazly mail -

Nima Khodadadi mail

link https://doi.org/10.54216/JAIM.020105

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Metaheuristic Optimized Ensemble Model for Classification of SMS Spam in Computer Networks

By use of electronic communication, we are able to communicate a message to the recipient. In this digital age, a collaboration between several people is possible thanks to a variety of digital technologies. This interaction may take place in a variety of media formats, including but not limited to text, images, sound, and language. Today, a person's primary means of communication is their smart gadget, most commonly a cell phone. Spam is another side effect of our increasingly text-based modes of communication. We received a bunch of spam texts on our phones, and we know they're not from anyone we know. The vast majority of businesses nowadays use spam texts to advertise their wares, even when recipients have explicitly requested not to receive such messages. As a rule, there are many more spam emails than genuine ones. We apply text classification approaches to define short messaging service (SMS) and spam filtering in this study, which effectively categorizes messages. In this paper, we use "machine learning algorithms" and metaheuristic optimization to determine what percentage of incoming SMS messages are spam. This is why we used the optimized models to evaluate and contrast many classification strategies for gathering data.

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Mohamed Saber mail -

El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail -

Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/IJWAC.060205

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new