Qualification: 
MS
shijus@am.amrita.edu
Phone: 
+91 476 280 4524/4530

Shiju Sathyadevan currently work as an Asst. Professor at Amrita Centre for Cyber Security, Amrita Vishwa Vidyapeetham, Amritapuri Campus mainly focusing on research pertaining to security enhancements in cloud computing, Internet of Things, Big Data Analytic Platforms and also focusing on innovative E-Governance initiatives. Shiju was the chief investigator for one of the first cloud computing research initiatives funded by Department of Electronics & Information Technology, Govt of India, “Development of Trust Models for Cloud Computing”. He is also the chief investigator for the DeitY funded research project "Secure, Scalable, Interoperable Internet of Things Middleware". Shiju is responsible for developing the All integrated Big Data processing framework "Amrita Bigdata Framework" (ABDF) which brings the power of Hadoop, Spark and Storm under one roof. It uses a graphic user based tool to build data analytics flows using a series of built in computational components. The tool intend to reduce the dependency of data scientists on developers in analysing large volume data. Also looking into visualizing large volume data sets in a high interactive manner.

Before joining Amrita, Shiju was a freelance BI consultant working with leading organisations from multitude of business domains ranging from Investment banking (Goldman Sachs, London, Capital International, Geneva, GE Capital & Finance, Melbourne), pharmaceuticals (Astrazeneca, UK), logistics (M&S, UK), UK Government tax department, wide range of insurance corporations (Swiss Re, Catlin Underwriting, Friends Provident, UK), telecom (DU Telecom, Dubai) and petroleum (Shell, Melbourne), Financial Management (Yodlee, USA), Quality Consulting (Omnex, USA) etc.

Shiju pursued his M.S. in Information Technology from University of Western Sydney, Sydney, Australia in 1996.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

V. Radhakrishnan, Narayanan, H., and Shiju Sathyadevan, “System call authorization in linux by a secure daemon”, ARPN Journal of Engineering and Applied Sciences, vol. 12, pp. 3903-3908, 2017.[Abstract]


Compromises on data integrity and confidentiality have exposed the vulnerability of security architectures of traditional Linux-based operating systems against malicious attacks. Minimized functionality and increased complexity restrict the effectiveness of traditional approaches such as sandboxing in handling attacks. We proposed architecture based on restricted user privileges and authorization to secure the Linux operating system. We developed a Secure Daemon to authorize the system calls. All the system calls invoked by user processes are redirected to secure daemon using a dynamic dispatch mechanism (wrapper functions) implemented on top of the existing libraries. Our approach ensures that critical system resources are protected in the event of an attack. Since the major elements of the proposed system operate at the user level, it is portable across all Linux distributions. ©2006-2017 Asian Research Publishing Network (ARPN). All rights reserved.

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2017

Journal Article

K. U. Abinesh Kamal and Shiju Sathyadevan, “Intrusion detection system using big data framework”, ARPN Journal of Engineering and Applied Sciences, vol. 12, pp. 3909-3913, 2017.[Abstract]


In the enormous stream of network traffic, there is no way to identify which packet is benign and which is an anomaly packet. Hence, we intend to develop a new network intrusion detection model using apache-spark to improve the performance and to detect the intrusions while handling the colossal stream of network traffic in IDS. The model can detect known intrusion effectively using real-time analytics and hence identify unknown data schema compared with traditional IDS. The objective of the model addresses the following capabilities: Deep Packet Inspection (DPI) by inspecting the network traffic and examining the properties that describe the intrusion characteristics. Collaborating the vulnerability assessment with human intervention, using C.45 decision tree algorithm, optimizes pattern matching to boost detection rate. The clustered hosts are grouped based on their number of visits in an unique IP. The intrusion classifiers are developed by investigating each IP groups which reflects different properties used for prediction. The prediction model is built over Amrita Big Data Apache-Spark framework as a sequence of workflows. The above workflow is implemented in Amrita Big Data Framework (ABDF) to improve the detection time and performance, the model output provides effective results in detecting DOS attacks and port scanning attacks. ©2006-2017 Asian Research Publishing Network (ARPN). All rights reserved.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2015

Conference Paper

M. V. Sukanya, Shiju Sathyadevan, and Sreeveni, U. B. Unmesha, “Benchmarking support vector machines implementation using multiple techniques”, in Advances in Intelligent Systems and Computing, 2015, vol. 320, pp. 227-238.[Abstract]


Data management becomes a complex task when hundreds of petabytes of data are being gathered, stored and processed on a day to day basis. Efficient processing of the exponentially growing data is inevitable in this context. This paper discusses about the processing of a huge amount of data through Support Vector machine (SVM) algorithm using different techniques ranging from single node Linier implementation to parallel processing using the distributed processing frameworks like Hadoop. Map-Reduce component of Hadoop performs the parallelization process which is used to feed information to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression analysis. Paper also does a detailed anatomy of SVM algorithm and sets a roadmap for implementing the same in both linear and Map-Reduce fashion. The main objective is explain in detail the steps involved in developing an SVM algorithm from scratch using standard linear and Map-Reduce techniques and also conduct a performance analysis across linear implementation of SVM, SVM implementation in single node Hadoop, SVM implementation in Hadoop cluster and also against a proven tool like R, gauging them with respect to the accuracy achieved, their processing pace against varying data sizes, capability to handle huge data volume without breaking etc

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2015

Conference Paper

Shiju Sathyadevan, Hariram, Sa, Antony, Ab, and Mahith, V. Pb, “Fast approximate aggregation algorithms for effective browser visualization using similarity heuristics”, in International Journal of Applied Engineering Research, 2015, vol. 10, pp. 22133-22144.[Abstract]


Real world data is processed in multiple servers which constitutes a huge collection of data that causes the browser to crash at regular interval of time. The Aggregation have been done to those data that are generated from various sources like log files, video, text, image etc.This paper combines the fusion of hierarchical clustering with Map-reduce which indicates the grouping of same type of data and reducing it in to key-value pairs,this classification makes the processing and retrieval of real time data easy. Visualization of these data increases the efficiency for user to analyze it.The research results provide a better way to aggregate huge amount of data with high efficiency. © Research India Publications. More »»

2015

Conference Paper

Shiju Sathyadevan, Jaison, P. Jb, and Sankar, Gb, “Proposal of browser by exploiting the proficiency of V8 for large data visualization”, in International Journal of Applied Engineering Research, 2015, vol. 10, pp. 22939-22946.[Abstract]


Big data is a commonly discussed topic of today. The 4V’s (volume, velocity, variety and veracity) of big data is creating hurdles in many areas which also includes browser. Loading data in dynamic manner is hurdle faced by the browser. The performance falls down as the magnitude of data to be processed escalate. When the data size increases above a certain level it throws error in the java script followed by the crash of the browser. The performance of different browsers varies depending on the techniques they employ. Here we propose a solution based on Google V8 engine for handling big data by testing its efficiency in standalone mode. © Research India Publications. More »»

2015

Conference Paper

L. Mohan, Jinesh, M. K., Bipin, K., Harikrishnan, P., and Shiju Sathyadevan, “Implementation of Scatternet in an Intelligent IoT Gateway”, in Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2, Hyderabad, 2015, vol. 338, pp. 275–287.[Abstract]


Anything and everything will be connected in the world of IoT. This allows a ubiquitous communication around the world. The communication can be a sensed data from the physical world, or control signal for a device or else a usual internet data communication. There can be several ways with which the real world device can communicate to the IoT platform. Bluetooth is one such technology which allows communication between a device in the real world and IoT network. Bluetooth Scatternet concept, gives a bluetooth network the capability to support multiple concurrent bluetooth device communication over a wide area.. This paper discuss about a custom build intra and inter piconet scheduling model and its real-time validation on our own IoT platform which includes the Gateway, AGway (Amrita Gateway) and Middleware, AIoTm (Amrita Internet of Things Middleware). The paper emphasis on scatternet building and scatternet maintenance. The entire implementation is based on the linux bluetooth stack Blue-Z.

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2015

Conference Paper

Shiju Sathyadevan, Muraleedharan, N., and Rajan, S. P., “Enhancement of data level security in mongoDB”, in Advances in Intelligent Systems and Computing, 2015, vol. 321, pp. 199-212.[Abstract]


Recent developments in information and web technologies have resulted in huge data outburst. This has posed challenging demands in efficiently storing and managing large volume of structured and unstructured data. Traditional relational models exposed its weakness so much so that need for new data storage and management techniques became highly desirable. This resulted in the birth of NoSQL databases. Several business houses that churn out large volume of data have been successfully using NoSQL databases to store bulk of their data. Since the prime objective of such DB’s were efficient data storage and retrieval, core security features like data security techniques, proper authentication mechanisms etc. were given least priority. MongoDB is one among the most popular NoSQL databases. It is a document oriented NoSQL database which helps in empowering business to be more agile and scalable. As MongoDB is gaining more popularity in the IT market, more and more sensitive information is being stored in it and so security issues are becoming a major concern. It does not guarantee privacy of information stored in it. This paper is about enabling security features in MongoDB for safe storage of sensitive information through “MongoKAuth” Driver, a new MongoDB client side component developed in order to automate a lot of manual configuration steps. © Springer International Publishing Switzerland 2015. More »»

2015

Conference Paper

Shiju Sathyadevan and Nair, R. R., “Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest”, in International Conference on Computational Intelligence in Data Mining(ICCIDM-Dec 20-21 2014), Computational Intelligence in Data Mining , New Delhi, 2015.

2015

Conference Paper

Shiju Sathyadevan and Chaitra, M. A., “Airfoil Self Noise Prediction Using Linear Regression Approach”, in Computational Intelligence in Data Mining Proceedings of the International Conference on CIDM, New Delhi, 2015, vol. 2.[Abstract]


This project attempts to predict the scaled sound pressure levels in decibels, based on the aerodynamic and acoustic related attributes. Each attribute can be regarded as a potential feature. The problem is how to predict the sound pressure level accurately based on those features. This paper describes the approaches of using linear regression models and other optimization algorithms used for the better predictions. The comparative results and analysis are also provided in experiment and results section. More »»

2015

Conference Paper

Shiju Sathyadevan, Kalarickal, B. S., and Jinesh, M. K., “Security, Trust and Implementation Limitations of Prominent IoT Platforms”, in Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014: Volume 2, Advances in Intelligent Systems and Computing, Cham, 2015, vol. 2, pp. 85–95.[Abstract]


Internet of Things (IoT) is indeed a novel technology wave that is bound to make its mark, where anything and everything (Physical objects) is able to communicate over an extended network using both wired and wireless protocols. The term “physical objects” means that any hardware device that can sense a real world parameter and can push the output based on that reading. Considering the number of such devices, volume of data they generate and the security concerns, not only from a communication perspective but also from its mere physical presence outside a secure/monitored vault demands innovative architectural approaches, applications and end user systems. A middleware platform/framework for IoT should be able to handle communication between these heterogeneous devices, their discoveries and services it offers in real time. A move from internet of computers to internet of anything and everything is increasing the span of security threats and risks. A comparative study of existing prominent IoT platforms will help in identifying the limitations and gaps thereby acting as the benchmark in building an efficient solution. More »»

2015

Conference Paper

Shiju Sathyadevan, Krishnasree Achuthan, and Poroor, J., “Architectural Recommendations in Building a Network Based Secure, Scalable and Interoperable Internet of Things Middleware”, in 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Cham, 2015, vol. 1, pp. 429–439.[Abstract]


With every device capable of emitting data, the need to amass and process them in real time or near real time, along with ever growing user requirement of, information-on-demand, have paved the way for the sudden surge in the development of the theme Internet of Things (IoT). Even though this whole new technology looks fascinating in theory, its practical implementation and ongoing sustenance is something that will need a lot of thought, effort and careful planning. Several cloud based and network based cloud platforms/middleware solutions are available but a lot of them are either extremely complex to set up, or needs standardized solutions to be applied across all participating devices or would leave behind vivid security loopholes that can’t be curbed with ease considering the overall capability of the devices involved. Based on a previous research effort conducted by our team, detailed scrutiny of prominent existing IoT platforms/middleware solutions were performed. This effort has resulted in defining the core problem matrix which if addressed adequately could result in the development of a well balanced IoT middleware solution. This paper uses the problem matrix so identified as the roadmap in defining a Secure, Scalable, Interoperable Internet of Things Middleware. More »»

2015

Conference Paper

Shiju Sathyadevan, Prabhakaranl, S., and Bipin, K., “A Survey of Security Protocols in WSN and Overhead Evaluation”, in Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014: Volume 2, Cham, 2015, pp. 729–738.[Abstract]


There has been a widespread growth in the area of wireless sensor networks mainly because of the tremendous possibility of using it in a wide spectrum of applications such as home automation, wildlife monitoring, defense applications, medical applications and so on. However, due to the inherent limitations of sensor networks, commonly used security mechanisms are hard to implement in these networks. For this very reason, security becomes a crucial issue and these networks face a wide variety of attacks right from the physical layer to application layer. This paper present a survey that investigates the overhead due to the implementation of some common security mechanisms viz. SPINS, TinySec and MiniSec and also the computational overhead in the implementation of three popular symmetric encryption algorithms namely RC5 AES and Skipjack. More »»

2015

Conference Paper

V. Vejesh, G. Nayar, R., and Shiju Sathyadevan, “Optimization of Hadoop Using Software-Internet Wide Area Remote Direct Memory Access Protocol and Unstructured Data Accelerator”, in Software Engineering in Intelligent Systems: Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015), Vol 3: Software Engineering in Intelligent Systems, Cham, 2015, pp. 261–270.[Abstract]


Over the last few years, data size grew tremendously in size and thus data analytics is always geared towards low latency processing. Processing of Big Data using traditional methodologies is not cost effective and fast enough to meet the requirements. Existing socket based communication (TCP/IP) used in Hadoop causes performance bottleneck on the significant amount of data transfers through a multi-gigabit network fabric. To fulfill the emerging demands , the underlying design should be modified to make use of data centre’s powerful hardware. The proposed project include integration of Hadoop with remote direct memory access (RDMA).For data-intensive applications, network performance becomes key component as the amount of data being stored and replicated to HDFS increases. RDMA is implemented in a commodity hardware through software ,namely, Soft-iWARP (Software-Internet Wide Area Protocol). Hadoop employs a Java-based network transport stack on top of the JVM . JVM introduces a significant amount of overhead to data processing capability of the native interfaces which constrains use of RDMA. The usage of plug-in library for data shuffling and merging part of Hadoop can take advantage of RDMA . An optimization for Hadoop in data shuffling part can be thus implemented. More »»

2015

Conference Paper

K. V. Swetha, Shiju Sathyadevan, and Bilna, P., “Network Data Analysis Using Spark”, in Software Engineering in Intelligent Systems: Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015), Vol 3: Software Engineering in Intelligent Systems, Cham, 2015, pp. 253–259.[Abstract]


With the huge increase in the volume of network traffic, there is a need for network monitoring systems that capture network packets and provide packet features in near real time to protect from attacks. As a first step towards developing such a system using distributed computation, new system has been developed in Spark, a cluster computing system, which extracts packet features with less memory consumption and at a faster rate. Traffic analysis and extraction of packet features are carried out using streaming capability inherent in Spark. Analysing the network data features provide a means for detecting attacks. This paper describes a system for the analysis of network data using Spark streaming technology which focuses on real time stream processing, built on top of Spark. More »»

2015

Conference Paper

U. B. Unmesha Sreeveni and Shiju Sathyadevan, “ABDF Integratable Machine Learning Algorithms-MapReduce Implementation”, in Procedia Computer Science, 2015, vol. 58, pp. 297 - 306.[Abstract]


As the amount of data generated on a day to day basis is on the uphill the urgency for efficient frameworks to handle, store and process the same is also increasing. Frameworks like Hadoop have proven its strength to churn huge volume of data to bring out the hidden patterns supporting decision making. Project that is being assigned to us is to develop Mapreduce based Machine Learning Algorithms to run on Hadoop clusters. Algorithms will be assigned on a case to case basis. Algorithms so developed will be integrated with Amrita BigData Framework (ABDF). Among those algorithms an application area or an end to end comparison will be done against different processing modes like linear implementation. Key matrix such as execution speed, usage of resources, accuracy, etc will be measured as applicable to the algorithm. Amrita Bigdata Framework (ABDF) is essentially an all integrated framework for effortless BigData analytics. ABDF is feature rich analytics framework, providing user community with an easy to use GUI for analyzing large data heaps. ABDF is capable of switching its processing modes between, Hadoop, Spark streaming/in-memory, Storm in-memory and Linear execution. Implementing Machine Learning algorithm in a distributed environment is trickier than its sequential implementation. While writing a mapreduce job we need to identify what part of the algorithm can be parallelized and how to parallelize. More »»

2015

Conference Paper

R. Meenakshi, Jayalekshmi, G., Hariram, S., Shiju Sathyadevan, and Thushara, M. G., “Visualization with Charting Library Based on SVG for Amrita Dynamic Dashboard”, in Procedia Computer Science, 2015, vol. 58, pp. 371 - 379.[Abstract]


Data Visualization is the representation of data in a graphical or pictorial format. For the effective communication of data for a user proper visualization is necessary. Visualization is essential in order for the user to understand the data in an easy way. Visualization of data is done through various charts that represent the attributes of the data. For web applications, there are many open source JavaScript libraries that work on HTML5 (using SVG or CANVAS). But the drawback of these libraries is that they don’t provide for much flexibility with respect to configuration. They also don’t provide generalization of charts. Also many data mining algorithms are not supported by these libraries for data visualization. This paper has illustrated in building JavaScript charting libraries that would ensure proper visualization of data which is flexible for user customization. The charting library supports different types of charts varying from scatter chart to line chart to bar chart that are used for various algorithms. The libraries are built based on Object-Oriented JavaScript concept to support web applications that run either on the internet or intranet, so that extending the same in the future is also possible. More »»

2015

Conference Paper

N. Rani N, Shiju Sathyadevan, Renault, E., and Hai, V. Ha, “Comparison of checkpointed aided parallel execution against mapreduce”, 2015.[Abstract]


Researchers have been actively working for the past few decades in parallelizing programs so as to cut through massive data chunks for faster response. Current day processors are faster and have more number of cores. So as to utilize the computational capabilities of the processors to its full extend, processes need to be run in parallel. A task can be performed in lesser time by using parallel programming. But writing a parallel programming manually is a difficult and time consuming task. So we have to use tools to convert a sequential program to a parallel one automatically. Open-MP (Open Multi-Processing) is a set of directives which can be used to generate parallel programs written in c, c++, FORTRAN to efficient parallel programs. A new paradigm called CAPE (Check-pointing Aided Parallel Execution) is introduced that uses check-pointing technique to generate parallel programs from sequential programs provided with Open-MP directives. Map-reduce is a programming model for performing parallel processing. In this paper we have compared the performance and coding complexity of map-reduce against CAPE under different levels of difficulties More »»

2014

Conference Paper

Shiju Sathyadevan, S, A., and Raghunath, A., “Improved Document Classification Through Enhanced Naive Bayes Algorithm”, in ICDSC 2014, 2014, pp. 100-104.[Abstract]


Immense growth in communication has paved way for existence of information across the world in wide separated zones. There exists a need for a mechanism to render apt information to the needy from this enormous source of information. This mechanism is of high demand for educational purposes. Knowledge based cloud (Kloud) proposes a solution to combine together the information in different area, which is managed by several organizations. It then organizes them into different sections and hence providing a platform to furnish relevant information to people in search of it. The paper discusses about a method based on Naive Bayes algorithm to classify documents pushed into "Kloud". A variation to this algorithm has been implemented by calculating term weight using "converged weight" method resulting in better accuracy and speed. A comparative study of proposed variance in classification algorithm against the actual algorithm was performed. Further we also implemented two subclassification algorithms namely hierarchical subclassification and subcategorization using document similarity method.

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2014

Conference Paper

Shiju Sathyadevan, Akhila, C. A., and Jinesh, M. K., “Customizing EPCglobal to Fit Local ONS Requirements”, in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 308 of the series Advances in Intelligent Systems and Computing , 2014, vol. 320, pp. 21–30.[Abstract]


Internet of Things (IoT) is the new network of physical object that has the ability to automatically transfer data over a network. This paper proposes an architecture to extend the current object identification scheme in order to custom build the same to uniquely identify each object associated with an RFID tag. The electronic product code (EPC) is a very popularly used identification scheme to identify objects and is stored in the RFID tags. The work described in this paper is based on the EPCglobal framework. Our research study focused primarily on extending the EPCglobal framework, thereby defining a customized identification scheme for each object in an IoT platform. A lookup called object naming service (ONS) is used to locate information about these objects in the EPC network. Object name service makes use of Internet’s existing domain name system (DNS) for looking up information about an EPC.

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2014

Conference Paper

Shiju Sathyadevan, S, D. M., and S., S. G., “Crime analysis and prediction using data mining”, in Networks Soft Computing (ICNSC), 2014 First International Conference on, 2014.[Abstract]


Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day. More »»

2014

Conference Paper

Shiju Sathyadevan, Balakrishnan, A. K., S, A., and S, A. R., “Identifying moving bodies from CCTV videos using machine learning techniques”, in First International Conference on Networks Soft Computing (ICNSC), 2014, 2014.[Abstract]


The idea of auto face detection from surveillance cameras and CCTVs is very relevant today. More and more CCTVs and surveillance cameras are being installed everyday. If there is a database of facial data present then the task of recognition boils down to comparison of each and every face detected from the video with every face saved in the database. Now this process involves capturing the faces before hand. This is actually a very tedious job. So the database of images is created (/updated) as and when new faces come into the camera view. The labeling of the faces can be done at leisure (by a human) or not be done at all. The current system once deployed does not need a database of images to start with. It creates its own collection of images, and then tracks the future occurrences of those images. Eigenface, fisherface, LBP histograms and SURF are different algorithms used for face recognition. We have tried all these algorithms.but among these surf shows better result. So the paper uses SURF for comparing image descriptors. More »»

2014

Conference Paper

Shiju Sathyadevan, ,, ,, and Jinesh, M. K., “Implementing Scatternet to Enhance Bluetooth Communication Capability”, in CSI Hyderabad, 2014.

2014

Conference Paper

Shiju Sathyadevan, Nair, R., and Krishnan, A. B., “Comparing ID3, C4.5 and Random Forest ”, in ICCIDM 2014, 2014.

2014

Conference Paper

Shiju Sathyadevan, Prabhakar, S., and K, B., “Analysis Of Ovehead Due to Security Implementations in WSN”, in FICTA 2014, 2014.

2014

Conference Paper

Shiju Sathyadevan, Muraleedharan, N., and Rajan, S. P., “Accomplishing Data Level Security in MongoDB”, in ISI 2014, 2014.

2014

Conference Paper

Shiju Sathyadevan and Anoop, S., “Comparison of Traditional Linear SVM Implementation Against MapReduce SVM Implementation”, in ISI 2014, 2014.

2014

Conference Paper

Shiju Sathyadevan, Gangadharan, S., and M.S, D., “Crime Analytics using News feeds and Social Media through profiling”, in ICNSC 2014, 2014.

2012

Conference Paper

Ra Padmashani, Shiju Sathyadevan, and Dath, Da, “BSnort IPS: Better snort intrusion detection/prevention system”, in International Conference on Intelligent Systems Design and Applications, ISDA, Kochi, 2012, pp. 46-51.[Abstract]


With the advent of a range of intrusion detection and prevention systems out in the market and Snort IPS standing out from others, always there have been efforts to improve upon the current scenario. Here, a novel technique that can curb many of the current Denial-of-Service attacks which usually disrupts the network connectivity by consuming a large amount of bandwidth is discussed. The Better Snort Intrusion Detection/Prevention System (BSnort) uses Aho-Corasick automaton for the deep packet inspection and makes use of the modified Snort signatures which utilizes minimum amount of CPU and memory. The BSnort stands out from other Network Intrusion Detection Systems (NIDSs) in its integrated use of anomaly detection approach to find out novel attacks using the packet header along with the use of known attack signatures for the payload to pin-point intrusions. © 2012 IEEE.

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2012

Conference Paper

Shiju Sathyadevan, “Kloud - A virtual elastic knowledge cloud: A centralized directory based approach for education content aggregation”, in Proceedings - 2012 IEEE International Conference on Technology Enhanced Education, ICTEE 2012, Kerala, 2012.[Abstract]


Rapid global growth has caused information to exist as discrete isolated islands dispersed across the globe. As in the case of educational content, a single hub to tap this immense wealth of information asset is still non-existent. This paper introduces "Kloud" (Knowledge based cloud) - an innovative solution to addressing and integrating information. Kloud is designed to amalgamate disjointed information chains pertaining to diverse domains, owned and managed by multitude of organizations, under a single platform to build a powerful information base that could be accessed by a user anywhere anytime. Such an information base will act as single source information feeder to seekers. The system architected to allow elastic inclusivity includes several key modules such as a) Central Content Directory Management Cloud, b) Central Content Scavenger & Aggregator Platform and c) Content Aggregator & Broadcast Agent that overpowers any geographic limitations in residence of knowledge. In contrast to commonly used methodologies this architecture uses a directory-based approach to maintain a master content directory list that retrieves content from its registered providers. This directory is generated by a content aggregator and broadcast agent (CAB Agent) located at the providers end and broadcasted to the central directory server which then mirrors the same across key access points. © 2012 IEEE.

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2011

Conference Paper

Shiju Sathyadevan, Kallingalthodi, H., and Hari, N. N., “IRIGNET - Intelligent communication network for power-scarce rural India”, in ACWR 2011 - Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief, Amritapuri, 2011, pp. 465-471.[Abstract]


Considering the contribution agriculture has made towards India's rapid raise in economic status, its relevance to the nation's subjects, its depth and far reaching wing span across the length and breadth of the country and foremost to account for the turmoil through which the farming community in India is going through under the current circumstances, it does deserve to be treated with higher reverence and priority. Although agriculture has made enormous contribution towards India's rapid raise in economic status, accounting for nearly 20% of the GDP and the livelihood for 58% of the population, the farmers who are the mainstay of this industry face a lot of problems in raising crops and sustaining production. This paper proposes a system that will automate the irrigation system, especially in rural India, to compensate for the flimsy, inconsistent and unreliable power supply system. Suggested design uses a Central regional scheduler server hosting an intelligent software working alongside with remote microcontroller based sensors to get up to date environmental parameters so as to assist it to work out the best array of water pumps to be turned on at a given instance. Sensor devices are kept at the vicinity of where the water pumps are so that influential decision making parameters can be sensed and fed to the intelligent software stationed at the central server. It uses mobile technology to handshake between the central server and the sensor controllers. Various derivatives of the same are discussed which need to be weighed in terms of their practicality, ease of deployment and of course the cost factor. It also helps the nation to preserve both water and electricity by enforcing controlled but adequate usage of the same. More »»

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