Qualification: 
Ph.D, BE
s_lakshmi@blr.amrita.edu

Dr. S. Santhanalakshmi. currently serves as Assistant Professor at the department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru. She successfully defended her Ph. D. in “Design and Analysis of Cryptographic Primitives Based on Computational Intelligence Techniques" under the guidance of Dr. K. Sangeeta. She has work experiences at Sri Krishna College of Engineering and Technology, Kuniamuthur, Coimbatore and Maharaja Engineering College Avinashi. Her research interests include Light weight crypto algorithms, Computational Intelligence Techniques.

Publications

Publication Type: Journal Article

Year of Publication Title

2020

Dr. S. Santhanalakshmi, Sangeeta, K., and Patra, G. K., “Design of group key agreement protocol using neural key synchronization”, Journal of Interdisciplinary Mathematics, vol. 23, pp. 435-451, 2020.[Abstract]


Abstract Security is considered as an important anxiety for applications which involve communication (more than two) over public networks. Group key management is one of the essential building blocks in securing group communication. Most of the researches in group key management are based on the general idea of the original Diffie–Hellman (DH) key agreement protocol. The major drawback of generalized DH for multi-party is the fact that there are expensive exponential operations to be carried out. The factors that influence the capability of a Group Key agreement (GKA) protocol are communication rounds and the computational cost. In this paper, the concept of neural cryptography, based on the mutual learning of Tree Parity Machine (TPM), is extended to operations to be carried out. The factors that influence the capability of a Group Key agreement (GKA) protocol are communication rounds and the computational cost. In this paper, the concept of neural cryptography, based on the mutual learning of Tree Parity Machine (TPM), is extended to obtain a group key. The group key has been generated for an interactive conference key agreement system, where the system authenticates the users and allows them to compute their own session key, and also it has been recognized that the neural network based GKA protocols achieve key freshness and key secrecy. The quantity demand and investment in research and development, while the other model focuses on a more realistic relationship between the quantity demand and the price.

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2017

Dr. S. Santhanalakshmi, Sangeeta, K., and Patra, G. K., “Design of secure Cryptographic hash function using soft computing techniques”, International Journal of Advances in Soft Computing and its Applications, vol. 9, pp. 188-203, 2017.[Abstract]


Data integrity is a crucial part of any secure system. Cryptographic hash functions serve as a basic building block of information security for ensuring data integrity and data origin authentication. They are used in numerous security applications such as digital signature schemes, construction of MAC and random number generation. A hash function takes an arbitrary amount of input and produces an output of fixed size. Many of the widely used cryptographic MD-5 and SHA-1 hash functions have been shown to be vulnerable to attacks. The non linear behavior of the neural network model which takes multiple inputs to produce single output makes it a perfect entrant for cryptographic hash design. The paper describes the construction of a cryptographic hash function using a multi layer Tree Parity Machine neural network. Although in our simulations we have considered 512 bit message blocks which generate 128 digit hash value, the proposed algorithm can be used flexibly to generate a hash function of arbitrary length. Simulations show that this hash function satisfies the security requirements of confusion, diffusion, and collision attack.

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2014

P. M.V.C, Yuganeshan, A. J., Deepthi, Y. V. S. S. N., and Dr. S. Santhanalakshmi, “Comparison of Naïve Bayes Using Different Feature Selection Techniques in Twitter Sentiment Analysis”, International Journal of Applied Engineering Research, vol. 9, 2014.

2014

S. D. Shenoy, Sharma, V., and Dr. S. Santhanalakshmi, “Automation of Timetable Generation using Genetic Algorithm”, Int.J.Computer Technology & Applications, vol. 5, pp. 1491-1494, 2014.[Abstract]


Finding a feasible lecture/tutorial timetable in a large university department is a challenging problem faced continually in educational establishments. This paper
presents an evolutionary algorithm (EA) based approach to solving a heavily constrained university timetabling problem. The approach uses a problem-specific chromosome representation. Heuristics and context-based reasoning have been used for obtaining feasible timetables in a reasonable computing time. An intelligent adaptive mutation scheme has been employed for speeding up the convergence. The comprehensive course timetabling system presented in this paper has been validated, tested and discussed using real world data from a large university.

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2014

Dr. S. Santhanalakshmi, Dr. K. Sangeeta Iyer, and Patra, G. K., “Secure Key Stream Generation using Computational Intelligence Techniques”, Int. Journal Computer Technology & Applications, Month and Year of Publication, , vol. 5, pp. 967-972, 2014.

Publication Type: Conference Paper

Year of Publication Title

2020

S. Sethuraman and Dr. S. Santhanalakshmi, “Implementing Vehicle Black Box System by IoT based approach”, in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020.[Abstract]


Vehicle black box is an eventual vehicle tracking system that sits on the dashboard for observing the performance of the vehicle and driver behavior ensuring safety & security of the vehicle as well as the driver. The main purpose of this project is to create an Internet of Things (IoT) model of the Vehicle Black Box System (VBBS) that can be mounted in any vehicle all over the world. The camera and the sensors will be mounted in the vehicle to monitor activity within the car, where the data and image will be sent to the mail and short message service, the web page being tracked in real-time. The crash and its likely location are sent for medical assistance. This paper mainly focuses on improving the care of victims of the crash, helping, easily detect fraud.

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2019

V. Vishwa Kiran and Dr. S. Santhanalakshmi, “Raspberry Pi based Remote Controlled Car using Smartphone Accelerometer”, in 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2019.[Abstract]


This thesis presents a development and implementation of a remote controlled car (RC) for surveillance purpose and the whole system is controlled from anyplace from the world by Smartphone accelerometer. The work was led by establishing Wi-Fi communication between RC car and Smartphone. After setting up the network connection between RC vehicle and Smartphone, the vehicle can be controlled by just tilting the Smartphone by using remote IMU application that is installed in android phone. This application uses accelerometer, gyrometer and magnetometer for linear and rotational movement of car. The values from this application are sent to the Raspberry Pi through UDP protocol. For the surveillance purpose and to navigate the area while controlling the car remotely we used pi camera that is joined to the vehicle. The test was carried on the RC vehicle with four directions. Directions are 1) Left 2) Right 3) Forward 4) Backward.

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2018

T. R. Poojitha and Dr. S. Santhanalakshmi, “Assured Privacy and Authentication of Health Data in Cloud Using Cryptographic Algorithm”, in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangalore, India, 2018.[Abstract]


In this modern era many health care centers and other individual care takers create a prolific path to share and store data. So there are many applications developed for sharing data which reduces healthcare cost, but use of public clouds for this purpose includes high risks since the data is shared among multiple users, security issues are more deep-rooted and data will be vulnerable to theft. Currently, cloud service providers have the full control over the key than the patients which makes patients to feel in harm's way. The technical terms like confidentiality, availability and integrity well defines the security of data, so it is very important for the health department to secure the data. The trusted concept used in this paper is asymmetric Elgamal algorithm from the number theory concept for the web based healthcare applications along with that keyword search engine to match a particular word with existing keywords over the encrypted data. For the protection of end to end security we use protocols to protect data among authorized users. Design of webpage helps the patient to upload the encrypted data to the cloud and the authorized user can only download the data if and only if the key is present with him, finally memory usage and encryption time is calculated. This system provides end to end security for sharing of health data between a patient and an authorized user.

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2018

P. Deshpande, Dr. S. Santhanalakshmi, Lakshmi, P., and Vishwa, A., “Experimental study of Diffie-Hellman key exchange algorithm on embedded devices”, in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, 2018, pp. 2042-2047.[Abstract]


Security in Embedded systems has become an important constraint in modern days. There are different cryptographic algorithms developed for providing data security. Among these Diffie-Hellman is the basic one. Diffie-Hellman Key Exchange method of cryptography has been the simplest method of implementation used in various cryptographic applications till now. In this work, we introduce the Diffie-Hellman based cryptography implementation on two embedded devices communicating with each other over Ethernet. Our aim is to provide a secure data communication by using key agreement scheme generated using the Diffie-Hellman key exchange algorithm Here, we are implementing the Diffie-Hellman based cryptography on Raspberry Pi and arduino hardware by establishing a socket communication between the two hardware devices and the key exchange happens using the established network communication. © 2017 IEEE.

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2018

S. M. Brundha, Lakshmi, P., Dr. S. Santhanalakshmi, S.S., M., V.R., H., and S.K., N., “Home automation in client-server approach with user notification along with efficient security alerting system”, in Proceedings of the 2017 International Conference On Smart Technology for Smart Nation, SmartTechCon 2017, 2018, pp. 596-601.[Abstract]


Internet of things(IoT) is evolving to a vast extent. It involves collaboration of different devices and ultimately achieving efficient home automation as one application. Some of the key challenges in adopting IoT for mainstream life style varies from device diversity, security, connected services of IoT devices to add newer use case value proposition. This paper proposes a Client-Server service and device friendly approach for Home automation. A typical home automation workflow consists of 4 stages. Understanding the user environment by sensing, reporting the events to a centralized entity, centralized entity analyses and triggers the workflow, workflow will execute and update user by any interactive channels or even exercise over a home device (actuating). The physical condition of the device can also be altered based on the user request. The Home automation can be made efficient by including security factor by alerting user about an unknown person in the house. This IoT project implements a Client-Server based home automation with intruder alert to the user mobile phone. The user can also retrieve the image of the person entered in to the home. © 2017 IEEE.

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2017

P. Deshpande, Dr. S. Santhanalakshmi, and Lakshmi P., “Experimental study of DiffieHellmam Key Exchange Algorithm on Embedded Devices”, in International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS 2017) , Chennai, 2017.

2017

B. Sholaganga, Lakshmi P., and Dr. S. Santhanalakshmi, “Home Automation in Client-Server approach with user notification along with efficient security Alert System”, in International Conference On Smart Technologies For Smart Nation (SmartTechCon2017), Reva University, Bengaluru, 2017.

2015

Dr. S. Santhanalakshmi, K., S., and Patra, G. K., “Analysis of Neural Synchronization Using Genetic Approach for Secure Key Generation”, in Security in Computing and Communications, Cham, 2015.[Abstract]


Cryptography depends on two components, an algorithm and a key. Keys are used for encryption of information as well as in other cryptographic schemes such as digital signature and message authentication codes. Neural cryptography is a way to create shared secret key. Key generation in Tree Parity Machine neural network is done by mutual learning. Neural networks receive common inputs to synchronize using a suitable learning rule. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Faster synchronization of the neural network has been achieved by generating the optimal weights for the sender and receiver from a genetic process. In this paper the performance of the genetic algorithm has been analysed by varying the neural network and genetic parameters.

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2015

Dr. S. Santhanalakshmi, Dr. K. Sangeeta Iyer, and Patra, G. K., “Design and Analysis of Cryptographic Protocols using Computational Intelligence Techniques”, in International Conference on Inter-Research-Institute Student Seminar (IRISS 2015) , ACM India Annual Event at BITS, Goa, 2015.

2012

Dr. S. Santhanalakshmi, Sudarshan, T. S. B., and Patra, G. K., “Neural Synchronization by Mutual Learning Using Genetic Approach for Secure Key Generation”, in International Conference on Security in Computer Networks Distributed Systems (SNDS-2012), IITM-K Techno park at Trivandrum, 2012, vol. 335, pp. 422-431.[Abstract]


Neural cryptography is a new way to create shared secret key. It is based on synchronization of Tree Parity Machines (TPM) by mutual learning. Two neural networks trained on their mutual output bits synchronize to a state with identical time dependent weights. This has been used for creation of a secure cryptographic secret key using a public channel. In this paper a genetic approach has been used in the field of neural cryptography for synchronizing tree parity machines by mutual learning process. Here a best fit weight vector is found using a genetic algorithm and then the training process is done for the feed forward network. The proposed approach improves the process of synchronization.

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Faculty Research Interest: