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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Muthumarilakshmi, Surulivelu
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Computer Science & IT Electrical & Electronics Engineering

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Network intrusion detection system by applying ensemble model for smart home Amru, Malothu; Jagadeesh Kannan, Raju; Narasimhan Ganesh, Enthrakandi; Muthumarilakshmi, Surulivelu; Padmanaban, Kuppan; Jeyapriya, Jeyaprakash; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3485-3494

Abstract

The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.
A low-cost localization method in autonomous vehicle by applying light detection and ranging technology Kannan, Raju Jagadeesh; Amru, Malothu; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Aghalya, Stalin; Muthukumaran, Dhakshnamoorthy; Murugan, Subbiah
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.pp1739-1749

Abstract

The autonomous platform uses global positioning system (GPS) to localize the vehicle. In addition, light detection and ranging (LIDAR) and the high precision camera help to identify the turns in the road. The proposed system can help to determine the road turns with higher accuracy without utilizing LIDAR and high-precision camera technology. This research aims to implement a cost-effective simultaneous localization system that can reduce the cost by half for any autonomous vehicle. The existing system is more complex due to the inclusion of LIDAR technology. In contrast, the proposed approach uses beacon communication between vehicles and infrastructure and long-range (LoRa) for vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) communication. The simulation result illustrates that the proposed approach provides better performance.
Auto digitization of aerial images to map generation from UAV feed Kannan, Raju Jagadeesh; Yadav, Karunesh Pratap; Sreedevi, Balasubramanian; Chelliah, Jehan; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1338-1346

Abstract

Nowadays the rapid growth of unmanned aerial vehicles (UAVs) bridges the space between worldly and airborne photogrammetry as well as allow flexible acquisition of great solution images. In the case of natural disasters such as floods, tsunamis, earthquakes, and cyclones, their effects are most often felt in the micro-spaces and urban environments. Therefore, rescuers have to go around to get to the victims. This paper presents an auto digitization of aerial images to map generation from UAV feed at night time. In case of a power outage and an absence of alternative light sources, rescue operations are also slowed due to the darkness caused by the lack of electricity and the inability to light additional sources. In other words, to save lives, we need to know about all essential large-scale feature spaces in the dark so that we can use this information in times of disaster. The research proposed a soft framework for crisis mapping to aid in mapping the state of the aerial landscape in disaster-stricken areas, allowing strategic rescue operations to be more effectively planned.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2427-2435

Abstract

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
SVM algorithm-based anomaly detection in network logs and firewall logs Jesudasan Peter, John Benito; Rakesh, Nitin; Rekha, Puttaswamy; Sreelatha, Tammineni; Sujatha, Velusamy; Muthumarilakshmi, Surulivelu; Sujatha, Shanmugam
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.pp1642-1651

Abstract

The purpose of many advanced forms of cyberattack is to deceive the monitors, and as a result, these attacks often involve several kinds, levels, and stages. Existing anomaly detection systems often examine logs or traffic for indications of attacks, ignoring any additional analysis regarding attack procedures. This is done to save time. For example, traffic detection technologies can only identify the attack flows in a general sense. Still, they cannot reconstruct the attack event process or expose the present condition of the network node. In addition, the logs kept by the firewall are significant sources of evidence; nevertheless, they are still challenging to decipher. This paper introduces support vector machine algorithm-based Anomaly detection (SVMA) in network logs and firewall logs to provide robust security against cyberattacks. This mechanism consists of three modules: preprocessing, feature selection and anomaly detection. The genetic algorithm (GA) selects the better feature from the input. Finally, the support vector machine (SVM) isolates an anomaly powerfully. The investigational outcomes illustrate that the SVMA minimizes the required time to select the features and enhances the detection accuracy.
Co-Authors Aghalya, Stalin Amarnath, Raveendra N. Amru, Malothu Chelliah, Jehan Gurulakshmanan, Gurumoorthi Jagadeesh Kannan, Raju Jesudasan Peter, John Benito Jeyapriya, Jeyaprakash Kannan, Raju Jagadeesh Lebaka, Sivaprasad Mohankumar, Nagarajan Murugan, Subbiah Muthukumaran, Dhakshnamoorthy Narasimhan Ganesh, Enthrakandi Padmanaban, Kuppan Rakesh, Nitin Reddy, Munnangi Koti Rekha, Puttaswamy Sreedevi, Balasubramanian Sreelatha, Tammineni Srinivasan, Chelliah Sujatha, Shanmugam Sujatha, Velusamy Yadav, Karunesh Pratap