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Sabitha, R.
- A Service Package Identifier Based Security Verification Algorithm For Wireless Mobile AD-HOC Network
Abstract Views :258 |
PDF Views:0
Authors
R. Sabitha
1,
G. Kiruthiga
2
Affiliations
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 1 (2022), Pagination: 2650-2655Abstract
In general, the biggest problem with a mobile ad-hoc network is the threat to its security. This is because the mobile ad-hoc network is dismantled after a certain period of time, which spends a lot of time calculating its stability and greatly wastes its security dimensions. Thus, the security features on these temporary networks need to be strengthened as they pose the most threats. In this paper, a security algorithm designed in SID mode is proposed to fix security vulnerabilities in the wireless mobile ad-hoc network module. Its main feature is that its security definitions are defined according to the number of Service Package Identification assigned to it. The definition of numbers based on its importance is to make a list of related devices in order and, accordingly, bring those devices into the security module. Its security features have been improved so that the security modules remain active as long as the network is active.Keywords
Service Package Identification, Ad-hoc Networks, MANET, Security, Stability
References
- S. Kannan, G. Dhiman, and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT-Based Bat Agents for Traffic Management”, Electronics, Vol. 10, no. 7, pp. 785-796, 2021.
- M.E. Ahmed and H. Kim, “DDoS Attack Mitigation in Internet of Things Using Software Defined Networking”, Proceedings of International Conference on Big Data Computing Service and Applications, pp. 6-9, 2017.
- S. Banerjee and S. Khuller, “A Clustering Scheme for Hierarchical Control in Multi-Hop Wireless Networks”, Proceedings of IEEE International Conference on Computer and Communications Societies, pp. 102801037, 2001.
- L. Atzori and A. Iera, “The Internet of Things: A Survey”, Computer Networks, Vol. 54, No. 15, pp. 2787-2805, 2010.
- M.U. Bokhari, H.S.A. Hamatta and S.T. Siddigui, “A Review of Clustering Algorithms as Applied in MANETs”, International Journal Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 11, pp. 364369, 2012.
- R. Agarwal and M. Motwani, “Survey of Clustering Algorithms for MANET”, International Journal on Computer Science and Engineering, Vol. 1, No. 2, pp. 98104, 2009.
- B. Gobinathan, M.A. Mukunthan, S. Surendran, and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-7, 2021.
- N. Arivazhagan, K. Somasundaram, D. Vijendra Babu and V. Prabhu Sundramurthy, “Cloud-Internet of Health Things (IOHT) Task Scheduling using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems”, Scientific Programming, Vol. 2022, pp. 1-8, 2022.
- P. Krishan, “A Study on Dynamic and Static Clustering based Routing Schemes for Wireless Sensor Networks”, International Journal of Modern Engineering Research, Vol. 3, No. 2, pp. 1100-1104, 2013.
- M. Malik and Y. Singh, “Analysis of Leach Protocol in Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 2, pp. 178-184, 2013.
- T. Kothmayr, C. Schmitt, W. Hu, M. Br and G. Carle, “DTLS based Security and Two-Way Authentication for the Internet of Things”, Ad Hoc Networks, Vol. 11, No. 8, pp. 2710-2723, 2013.
- M. Wazid, A.K. Das, V. Odelu and N. Kumar, “Design of Secure User Authenticated Key Management Protocol for Generic IoT Networks”, IEEE Internet of Things, Vol. 5, No. 1, pp. 269-282, 2017.
- G. Dhiman, K. Somasundaram and K. Sharma, “Nature Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking”, Mathematical Problems in Engineering, Vol. 2021, pp. 121, 2021.
- S. Soni and B. Dey, “Dynamic Selection of Cluster Head in Cluster of Cluster Heads within the Cluster in Heterogeneous Wireless Sensor Network”, Proceedings of IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 877-881, 2014.
- N. Sabor, S. Sasaki, M. Abo Zahhad and S.M. Ahmed, “A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions”, Wireless Communications and Mobile Computing, Vol. 2017, pp. 119, 2017.
- X. Liu, “A Typical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review”, IEEE Sensors, Vol. 15, No. 10, pp. 5372-5383, 2015.
- S. Anjali and M. Sharma, “Wireless Sensor Networks: Routing Protocols and Security Issues”, Proceedings of International Conference on Computer Communication Networking Technologies, pp. 3-7, 2014.
- T. Bhatia, S. Kansal, S. Goel and A.K. Verma, “A Genetic Algorithm based Distance-Aware Routing Protocol for Wireless Sensor Networks”, Computer and Electrical Engineering, Vol. 56, pp. 441-455, 2016.
- P. Sivakumar and M. Radhika, “Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN”, Procedia Computer Science, Vol. 125, pp. 248-256, 2018.
- A. Peiravi, H.R. Mashhadi and S. Hamed Javadi, “An Optimal Energy-Efficient Clustering Method in Wireless Sensor Networks using Multi-Objective Genetic Algorithm”, International Journal of Communication Systems, Vol. 26, No. 1, pp. 114-126, 2013.
- Steganalysis:Multi-Class Classification of Images Using Linear Support Vector Machine
Abstract Views :244 |
PDF Views:3
Authors
B. Yamini
1,
R. Sabitha
2
Affiliations
1 Department of CSE, Sathyabama University, Chennai -119, TamilNadu, IN
2 Department of IT, Jeppiaar Engineering College, Chennai-119, TamilNadu, IN
1 Department of CSE, Sathyabama University, Chennai -119, TamilNadu, IN
2 Department of IT, Jeppiaar Engineering College, Chennai-119, TamilNadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 6, No 6 (2014), Pagination: 234-236Abstract
Steganographic techniques have been used to embed covert messages inside a piece of unsuspicious media and sending it without anyone’s knowledge about the survival of the covert message. Steganalysis is the process of detecting the presence of concealed information from the stego image and it can lead to the prevention of terrible security incidents. Steganalysis consist of two stages, the first stage is to identify the existence of the hidden message and the second stage is to retrieve the content of the message. In the existing method, for identifying the existence of the message, two-class classification using Support Vector Machine is used to differentiate the cover and stego images. In this paper, a new technique called multi-class classification using Linear Support Vector Machine is used to differentiate the cover and different type’s stego images.
Keywords
Steganography, Steganalysis, Stego Image, Two-Class Classification, Multi-Class Classisification and Linear Support Vector Machine.- Plant Disease Recognition and Clustering Using Fuzzy Algorithm on Data Mining
Abstract Views :247 |
PDF Views:2
Authors
R. Sabitha
1,
G. Kiruthiga
2
Affiliations
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2429-2432Abstract
Due to large size and intensive processing needs, deep learning models are not suited for mobile and handheld devices. Our goal is to develop a process that begins with pre-processing, diagnoses diseased leaf areas, uses the GLCM to choose and classify features, and culminates in a conclusion. We developed fuzzy decision methods for assigning photos of common rust to various severity levels, using data on diseased leaf regions isolated by threshold segmentation. The outcomes of these experiments were determined by six different colour and texture attributes. In plant disease clustering, the Fuzzy Algorithm is utilised. The test results demonstrate that the new method is more efficient than the conventional approaches and ranks first for feature extraction techniques. This appears to say that plant disease diagnosis using leaves should be utilised. Additional disease classifications or crop/disease classifications can be added to define these capabilities.Keywords
Plant Disease, Plant Leaf, Recognition, Clustering.References
- N.V. Kousik, M. Sivaram and R. Mahaveerakannan, “Improved Density-Based Learning to Cluster for User Web Log in Data Mining”, Proceedings of International Conference on Inventive Computation and Information Technologies, pp. 813-830, 2021.
- H. Azath, M. Mohanapriya and S. Rajalakshmi, “Software Effort Estimation using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network”, Journal of Intelligent Systems, Vol. 29, No. 1, pp. 251-263, 2018.
- H. Azath and R.S.D. Wahidabanu, “Function Point: A Quality Loom for the Effort Assessment of Software Systems”, International Journal of Computer Science and Network Security, Vol. 8, No. 12, pp. 321-328, 2008.
- N.V. Kousik, “Privacy Preservation between Privacy and Utility using ECC-based PSO Algorithm”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 567-573, 2021.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, Proceedings of International Conference on Distributed Artificial Intelligence, pp. 277-290, 2020.
- K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.
- K.M. Baalamurugan and S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, pp. 303-307, 2018.
- Enhancing Medical Imaging for Diagnosis and Treatment using Neuro Fuzzy Systems
Abstract Views :52 |
Authors
Affiliations
1 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
2 Department of Computer Science and Engineering, SNS College of Technology, IN
1 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
2 Department of Computer Science and Engineering, SNS College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 15, No 1 (2024), Pagination: 3465-3472Abstract
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Accurate and early diagnosis is critical for effective treatment. Traditional methods of medical imaging analysis often lack precision and efficiency. The challenge lies in enhancing the accuracy and efficiency of medical imaging analysis for CVD diagnosis using advanced computational methods. This study proposes a novel approach that integrates extreme learning machines (ELM) for feature extraction with neuro-fuzzy systems for classification. The ELMs efficiently extract relevant features from medical images, while the neuro-fuzzy systems classify these features with high accuracy. Experimental results demonstrate a significant improvement in diagnosis accuracy. The proposed method achieved a classification accuracy of 95.7%, sensitivity of 94.3%, and specificity of 96.2%. These results outperform several existing methods in terms of both accuracy and computational efficiency.Keywords
Cardiovascular Disease, Medical Imaging, Extreme Learning Machines, Neuro-Fuzzy Systems, Feature Extraction- Implementation of Smartphone Activated Doorlock System Using Wireless Fidelity [WiFi] and CCTV Camera
Abstract Views :249 |
PDF Views:1
Authors
R. Sabitha
1,
S. Sridhar
1
Affiliations
1 Department of Master of Computer Application, S. A. Engineering College, Chennai-600 077, IN
1 Department of Master of Computer Application, S. A. Engineering College, Chennai-600 077, IN
Source
Wireless Communication, Vol 10, No 9 (2018), Pagination: 173-177Abstract
WiFi system plays a major role in this smart world now-a-days. As internet connects a wide range of people together, here WiFi is used to share those networks with each other. Smartphone with WiFi involves in many aspects of monitoring. In this paper, the Smartphone activated door lock system is implemented using wireless fidelity technology and CCTV camera. Now-a-days the door lock system is used to secure our private places like home, apartments etc.. The CCTV camera will be set in front of the home. If any person arrives, the camera will capture the photo of the visitor. Immediately the camera transfers it wirelessly to the Smartphone that has been connected to the camera via WiFi. So now the owner of the home can check the photo that has been received through WiFi and if he/she is willing to open the door, he/she can press the push button specified in the Smartphone, so that door will open automatically. As the distance between the owner and the specific place (home, apartment etc..) increases, the opening time of the door also increases as well.
Keywords
WiFi Router, Smartphone, Microcontroller, CCTV Camera, Solenoid Door Lock.References
- E.I.S Saadon Pusat Pengajian Diploma, Universiti Tun Hussein Onn Malaysia Johor, “Android-based Home Door Locks Application via Bluetooth for Disabled People”, Nov 2014.
- Adnan Ibrahim, Afhal Paravath, Aswin P. K., Shijin Mohammed Iqbal and Shaeez Usman Abdulla, “GSM Based Digital Door Lock Security System”, 2015 IEEE International Conference on Power, Instrumentation, Control and Computing (PICC).
- Yong Tae Park Pranesh Sthapit Jae-Young Pyun, “Smart Digital Door Lock for the Home Automation”, 2009.
- Siddhi Kavde1, Riddhi Kavde1, Sonali Bodare1, Gauri Bhagat, “Smart digital door lock system using Bluetooth technology ”, International conference on Information communication & Embedded system(ICICES 2017).
- Bhalekar Pandurang1, Jamgaonkar Dhanesh .Prof. Mrs. Shailaja Pede, Ghangale Akshay, Garge Rahul, “Smart lock: A Locking System Using Bluetooth Technology & Camera Verification”, International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 4, Issue 1 (January-February, 2016), PP. 136- 139, 2016.
- RA Ramlee, D. H. Z. Tang, M.M.lsmail, "Smart Home System for Disabled People Via Wireless Bluetooth", in Proc. of IEEE International Conference on System Engineering and Technology, pp. 1-4, 2012
- https://www.google.co.in/search?client=ms-android=cctv+camera+to+phone+connection+using+wifi.
- https://www.google.co.in/search?client=ms-android=wi-fi.
- https://www.google.co.in/search?client=ms-androidoppo&tbm=isch&q=microchip+pic18f66k80&chips=q:microchip+pic18f66k80.
- AkshaykumarPatil, PanjaJadhav, Vinod Magar, OmkarDahyalkar, “Home Security Using Wireless Technology “International Research Journal of Engineering and Technology (IRJET), p-ISSN: 2395-0072, e-ISSN: 2395-0056, Nov-2016.
- JulisahBinti Mohamad Isah (2009),”Main Door Security System using SMS”. Faculty of Electrical and Electronic Engineering, University TunHussein Onn Malaysia: Final year project report.
- Ronald E Pyle, “Home Security System“, US446454A, May1, 1984.
- John Mallory, “Central Monitor for Home Security System “, US4581606A, Apr8, 1986.
- Takashi Murakami, YasuyukiShintani, Kazuhiro Aizu, “Home Security System“, US6759957 B2, Iul 6, 2004.
- Yanbo Zhao, ZhaohuiYe,”A low cost GSM/GPRS based wireless home security system “, Consumer Electronics, IEEE Transaction on (Volume: 54, Issue: 2), May 2008.
- https://www.cargurus.com/Cars/Discussion.
- N. Hashim, N. F. A. M. Azmi, F. Idris and N. Rahim Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia, ”Smart phone activated door lock system using wi-fi”, MARCH 2016.
- Neelam Majgaonkar, Ruhina Hodekar, Priyanka Bandagale, Information Technology, Finolex Academy of Management and Technology, Ratnagiri, India, “Automatic Door Locking System”, 2016 IJEDR.
- Agbo David Odu, Madukwe Chinaza Alice, Otengye Jotham Odinya, American Journal of Engineering Research (AJER) e-ISSN: 2320-0847 p-ISSN: 2320-0936 Volume-6, Issue-7, pp-146-151,”Low Cost Removable (Plug-In) Electronic Password - Based Door Lock”, July-2017.
- Arpita Mishra, Siddharth Sharma, Sachin Dubey, S.K.Dubey,”PASSWORD BASED SECURITY LOCK SYSTEM”, International Journal of Advanced Technology in Engineering and Science www.ijates.com Volume No.02, Issue No. 05, May 2014.
- Pradnya R. Nehete, J. P. Chaudhari, S. R. Pachpande, K. P. Rane,”Literature Survey on Door Lock Security Systems”, International Journal of Computer Applications (0975 – 8887) Volume 153 – No2, November 2016.