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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Rao, Kunjam Nageswara
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Improving the performance of IoT devices that use Wi-Fi Razzaq, Ali Ahmed; Rao, Kunjam Nageswara
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp748-757

Abstract

Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.
Improving the performance of the fuzzy-internet of things joint system by using an efficient service deployment algorithm Razzaq, Ali Ahmed; Rao, Kunjam Nageswara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp172-181

Abstract

The aim of this paper to present research proposes a quality of service (QoS)-fog service placement algorithm, which is in the context of providing better performance when there are requests for high-priority (delay-sensitive) services. Our proposed algorithm attempts to maintain the implementation of delay-sensitive services within a fog environment. Its performance was evaluated by comparing it with two placement strategies (cloud only, edge-ward), using the MobFogSim simulator. The results showed that the proposal achieved better performance than the two mentioned strategies, from two perspectives (according to the scenarios). The first is at the service level, where the algorithm was able to achieve a lower average response time and the other is at the system level, as it was able to reduce the total energy consumption by adopting a mechanism to save energy when there is a low network load.
CNN-GRU based cyber-attack classification and detection with the CICIDS-2017 dataset using optimization algorithm for honey badger Mahesh, Katikam; Rao, Kunjam Nageswara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1765-1775

Abstract

The sheer volume of data exchanged has grown through information and communications technology (ICT) swiftly growing importance since the attackers benefit from illegal access to network data and introduce possible dangers for data theft or alteration. It is considered a significant barrier to monitor the network traffic for cyber-attack detection and classification with alarm ring to inform to network administrator. With KDD-CUP99, conventional machine learning methods like deep neural network (DNN), a kind of artificial neural network (ANN), cannot detect and classify novel attacks types and lacks clarity regarding accuracy. The CICIDS 2017 dataset, which is improved in this study, serves as training data for the model and useful framework that combines a hybrid convolutional neural network (CNN) with the gated recurrent unit (GRU) technique. The primary aim of this effort is to classify different security attacks and classify cyberthreats with honey badger optimization algorithm (HBOA). To strengthen the performance criteria for various assault types, such as F1-score, recall, precision, and others, the HBOA is utilized to modify the model parameters high-level features ought to be extracted from the network data using the hybrid model assessed and verified by simulation studies. The detection and classification output from the CNN-GRU model, which detects different security threats with greater accuracy of 94%.
Mechanized network based cyber-attack detection and classification using DNN-generative adversarial model Mahesh, Katikam; Rao, Kunjam Nageswara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1755-1764

Abstract

These days almost everything is internet. Cyberattacks are the world's most pressing issues. Due to these attacks, Computer systems can be rendered inoperable, disrupted, destroyed or controlled via cyberattacks. Additionally, they can be used to steal, modify, erase, block, or alter data. Most organizations are facing this Issue and lose financially as well as in data security, there are numerous conventional intrusion detection systems (IDS) and firewalls are illustrations for network security tools which are not able to classify and detect different types of attacks in network. With machine learning approach using the Dataset KDD_CUP 99 as input, the synthetic minority oversampling technique (SMOTE) is one of the most often used oversampling methods for addressing imbalance issues. The proposed hybrid deep neural network (DNN), generative adversarial network (GAN), and exhaustive feature selection (EFS) can detect and classify several attack types including R2L, U2R, Probe, denial of service (DoS), and normal attacks types and inform to administrator to ring alarm sound to control and monitor network traffic in dynamically typed networks.
Co-Authors Mahesh, Katikam Razzaq, Ali Ahmed