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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ananthan, Bhuvanesh
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Electrical & Electronics Engineering

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Secured web application based on CapsuleNet and OWASP in the cloud Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1924-1932

Abstract

The tremendous use of sensitive and consequential information in the advanced web application confronts the security issues. To defend the web application while it processing the information must requires the security system. The detection of attacks of web is made by the payload or HTTP request-based detection in association with the scholars. Some of the scholars provide secured attack model detection; however, it fails to achieve the optimal detection accuracy. In concern with these issues, we propose an innovative technique for the attack detection the web applications. The proposed attack detection is based on the novel deep CapsuleNet based technique and the process begins with pre-processing steps known as decoding, generalization, tokenization/standardization and vectorization. After the pre-processing steps the information are passed to deep CapsuleNet for extracting the features for attaining the temporal dependencies from the sequential data. The subtle patterns in the information also detected using the proposed work. Simulation is effectuated to demonstrate the effectiveness of the proposed work and compared with other existing works. Our proposed system provides better accuracy in detecting the attacks than the state-of-art works.
Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1950-1959

Abstract

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.
Detection of cyberattacks using bidirectional generative adversarial network Vallabhaneni, Rohith; Vaddadi, Srinivas A.; Somanathan Pillai, Sanjaikanth E Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1653-1660

Abstract

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.
An efficient convolutional neural network for adversarial training against adversarial attack Vaddadi, Srinivas A.; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Addula, Santosh Reddy; Vallabhaneni, Rohith; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1769-1777

Abstract

Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like fast gradient descent (FGS), recursive-FGSM (I-FGS), Deep-Fool, and Carlini and Wagner (CW) techniques are utilized that generate adversarial input by means of the CNN acknowledged to the attacker. In the experimentation process, the MNIST dataset comprising 60K and 10K training and testing grey-scale images are utilized. In the experimental section, the adversarial training model reduces the attack accuracy rate (ASR) by an average of 29.2% for different malicious inputs, when preserving the accuracy of 98.9% concerning actual images in the MNIST database. The simulation outcomes show the preeminence of the model against adversarial attacks.
MobileNet based secured compliance through open web application security projects in cloud system Vallabhaneni, Rohith; Vaddadi, Srinivas A; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1661-1669

Abstract

The daunting issues that are promptly faced worldwide are the sophisticated cyber-attacks in all kinds of organizations and applications. The development of cloud computing pushed organizations to shift their business towards the virtual machines of the cloud. Nonetheless, the lack of security throughout the programmatic and declarative levels explicitly prone to cyber-attacks in the cloud platform. The exploitation of web pages and the cloud is due to the uncrated open web application security projects (OWASP) fragilities and fragilities in the cloud containers and network resources. With the utilization of advanced hacking vectors, the attackers attack data integrity, confidentiality, and availability. Hence, it’s ineluctable to frame the application security-based technique for the reduction of attacks. In concern to this, we propose a novel Deep learning-based secured advanced web application firewall to overcome the lack of missing programmatic and declarative level securities in the application. For this, we adopted the MobileNet-based technique to ensure the assurance of security. Simulations are effectuated and analyzed the robustness with the statistical parameters such as accuracy, precision, sensitivity, and specificity and made the comparative study with the existing works. Our proposed technique surpasses all the other techniques and provides better security in the cloud.
Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Vaddadi, Srinivas A; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp488-497

Abstract

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission.
Improved Bi-GRU for parkinson’s disease severity analysis Arunachalam, Malathi; Ramar, Ramalakshmi; Gandhi, Vaibhav; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1140-1149

Abstract

Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities. For diagnosing clinical set of PDs, medical experts utilize medical observations. These observations are highly based on the expert’s experience and can vary among clinicians due to its subjective nature, leading to differences in evaluation. The gait patterns of individuals with PD typically exhibit distinctions from those of adults. Evaluating these gait malformations not only aids in diagnosing PD but can also enable the categorization of severity stages with respect to symptoms of motor movement. Therefore, this paper introduces a classification of gait model based on the optimized deep learning (DL) model bidirectional gated recurrent unit-artificial hummingbird optimizer (BI-GRU-AHO). The training and testing involved the sequential segmentation of the right and left instances from the signals of vertical ground reaction force (VGRF) based on the identified gait cycle. The outcomes of the proposed BI-GRU-AHO exhibits reliable and accurate assessment of PD and achieved better accuracy of 98.7 %. The proposed model is trained and tested satisfactorily; hence it can be implemented in a real-time environment by integrating the model into a software application or system capable of receiving real-time data from PD patients.
Enhancing fruit recognition with robotic automation and salp swarm optimization for random forest classification Chakravarthy Malineni, Sai; Mytheen Basari Kodi, Kaja; Sakkarai, Jeevitha; Nallasivan, Gomathinayagam; Geetha, Mani; Ananthan, Bhuvanesh
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8917

Abstract

In response to the growing demand for automation and labor-saving solutions in agriculture, there has been a noticeable lack of advancements in mechanization and robotics specifically tailored for fruit cultivation. To address this gap, this work introduces a novel method for fruit recognition and automating the harvesting process using robotic arms. This work employs a highly efficient and accurate model utilizing a single shot multibox detector (SSD) for detecting the precise fruit position. Once the fruit's position is identified, the angles of the robot arm's joints are calculated using inverse kinematics (IK). Finally, the optimal path planning is ensured by the salp swarm optimization (SSO) assisted random forest (RF) classification. This approach enables the precise management of robotic arms without any interference with either the fruits themselves or other robotic arms. Through meticulous consideration of these factors, our method ensures seamless operation in agricultural environments. Experimental validation demonstrates the effectiveness of these techniques in detecting apple fruits outdoors and subsequently automating their harvesting using robotic arms. This successful implementation underscores the potential for widespread application of our approach in enhancing efficiency and productivity in fruit cultivation.
Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection Kalluri, Venkata Saiteja; Malineni, Sai Chakravarthy; Seenivasan, Manjula; Sakkarai, Jeevitha; Kumar, Deepak; Ananthan, Bhuvanesh
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8757

Abstract

In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
Vulnerability detection in smart contact using chaos optimization-based DL model Vaddadi, Srinivas A; Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1793-1803

Abstract

This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) for enhancing detection of vulnerability. The evaluation of the suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, the vulnerability detection process utilizes BiLSTM, with the optimization of the loss function achieved through LogT. Results indicate promising performance in identifying vulnerabilities in SC, highlighting the efficacy of the LogT-BiLSTM approach.
Co-Authors Addula, Santosh Reddy Arunachalam, Malathi Chakravarthy Malineni, Sai Deepak Kumar Gandhi, Vaibhav Geetha Mani, Geetha Geetha, Mani Jayamurugan, Prabhu Jeyakumar, Murugachandravel Kaja Mytheen, Basari Kodi Kalluri, Venkata Saiteja Malineni, Sai Chakravarthy Mytheen Basari Kodi, Kaja Nallasivan, Gomathinayagam Ramar, Ramalakshmi Ramasamy, Kannan Sakkarai, Jeevitha Seenivasan, Manjula Somanathan Pillai, Sanjaikanth E Vadakkethil Somanathan Pillai, Sanjaikanth E. Vadakkethil Vaddadi, Srinivas A Vaddadi, Srinivas A. Vallabhaneni, Rohith