Replacement of physical labor and repetitive tasks by the agents is an attractive issue in the Smart Environment (SE). SE is distinguished by its ability to be controlled from a distance, to facilitate the connection between devices through middleware, to gather and share data from sensors, to improve the intelligence of devices, and to make decisions. To be effective, SE design must make use of information and networks that already exist in the actual world. Effective SE design is complicated by several difficulties, including monitoring, data collecting, assessment, evaluation, prediction of important data, and meaningful presentation. For SE, the most important step is gathering information from a variety of sensors in various locations. Wireless sensor networks provide an underlying architecture for the coordinated collection of data from many sensors that have common characteristics (WSN). An essential aspect of sensor networks is their inability to function in the currently complicated environment for wireless network security. In the realm of remote sensor businesses, cryptology is an essential part of safety measures. Several of the prevalent cryptographic methods have significant flaws that prevent them from being fully reliable. In this paper, we provide a unified, three-stage cryptographic procedure that combines public-key and secret-key techniques for maximum security. Due to consideration of Public-key management and high degree of security, Rijndael Encryption Approach (REA), Horst Feistel's Encryption Approach (HFEA), and the more sophisticated Rivest-Shamir-Adleman (e-RSA). Time spent in both execution and decoding of the suggested approach was utilized to rank the quality of displays. The suggested set of rules uses a single evaluation boundary or computation time, which is different from the methodologies used before. Low Encryption Time (LET) and Low Unscrambling Time (LDT) values of 1.12 and 1.26 were observed on texts ranging in size from 6 to 184 MB, respectively. Comparisons show that the suggested hybrid form is 2.9% more efficient than AES+RSA, 1.36 times more efficient than ECC+RSA+MD-5, 1.36 times more efficient than AES+ECC, and 1.36 times more efficient than AES+ECC+RSA+MD-5.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060201
Vol. 6 Issue. 2 PP. 08-17, (2023)
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.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060202
Vol. 6 Issue. 2 PP. 18-33, (2023)
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.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060203
Vol. 6 Issue. 2 PP. 34-42, (2023)
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.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060204
Vol. 6 Issue. 2 PP. 43-55, (2023)
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.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060205
Vol. 6 Issue. 2 PP. 56-64, (2023)
The most unique and important design considerations in 5G cloud computing are the delay, energy consumption, and throughput. Therefore, most recent studies focused on boosting delay and energy consumption, and throughput using edge computing. The active queue management-based green cloud model (AGCM) is one of the most recent green cloud models that decreases the delay and sustains a stable throughput. Also, Mobile edge computing (MEC) is an essential cloud computing model for mobile users to meet the continuous growth of data requests. Thus, we offer a handoff scenario between the AGCM and MEC to assess the possible benefits of such collaboration and enhance its effects on the fundamental cloud restrictions such as delay and throughput. Accordingly, the proposed algorithm is named Enhanced Active queue management-based green cloud model (EAGCM). The proposed EAGCM regards incorporation between Kmeans and AGCM. The simulation results indicate that the proposed EAGCM serves mobile users efficiently, enhances the throughput, and reduces latency compared to AGCM and the cloud for 5G systems.
Read MoreDoi: https://doi.org/10.54216/IJWAC.060206
Vol. 6 Issue. 2 PP. 65-72, (2023)