Browsing by Author "Santhi Thilagam, P.S."

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A Clustering-based model for the Generation of Diversified Recommendations
(Institute of Electrical and Electronics Engineers Inc., 2022) Chaitanya, V.S.; Mohan, M.; Santhi Thilagam, P.S.
The primary goal of a recommender system is to generate accurate recommendations according to the user's interests. But the user's satisfaction increases when they get a chance to view the diverse categories of items. There exist several works on the generation of diverse recommendations but the performance of these methods often gets limited due to the issues such as cold start, filter bubble long tail, and grey sheep. Moreover, these methods do not consider the user's preference regarding exploration and exploitation while generating the recommendations. To this extent, this work proposes a model known as the iterative clustering-based diversity model, which can generate diverse recommendations and also solve the above-said issues. It groups the items based on the item description using the TF-IDF algorithm. The model generates two recommendations in such a way that one recommendation is similar and the other is different in comparison with the last interaction made by the user. The model has been evaluated on the benchmark dataset and has achieved promising results. Â© 2022 IEEE.
A linear hash based indexing scheme for location dependent data broadcast
(IEEE Computer Society, 2009) Sriprajna, K.J.; Santhi Thilagam, P.S.
Location based data dissemination in mobile wireless environment essentially requires some kind of indexing so that mobile clients can energy-efficiently access required data. Existing Location based indexing scheme (LBIS) for Location Dependent Data (LDD) follows tree based indexing mechanism and builds two levels of indices. But, LBIS spends considerable amount of time in fetching indices leading to increase in tuning time. On the other hand, LBIS records too much of index information making broadcast cycle short. In this paper we propose a hash based indexing scheme for LDD.Proposed scheme has a very compact structure because it binds the hashing parameters along with data and thereby eliminates the index overhead. This method also keeps mobile unit active for maximum three time units, thus decreasing the tuning time drastically. Even when the size of the broadcast data is increased, tuning time remains fairly stable. Through analysis and experiments; the effectiveness of the proposed method is shown. Â© 2009 IEEE.
A statistical tool for time synchronization problem in WSN
(Bentham Science Publishers P.O. Box 294 Bussum 1400 AG, 2019) Upadhyay, D.; Dubey, A.K.; Santhi Thilagam, P.S.
Background: In recent research, time synchronization has a great importance in the various application of wireless sensor network. Localization, tracking, message passing using contention-based schemes and communication are some of the fields where synchronization between sensor clocks is highly required. Therefore, several algorithms were designed to achieve a rational and reliable frame of time within the wireless sensor network. Patents related to time synchronization in WSN were also analyzed. Methods: This paper discusses the powerful statistical tool using maximum probability theory for synchronizing the time within the sensor's clock. In this paper, maximum probability theory is applied to estimate the best value of clock offset between two sensor clocks. The proposed algorithm is analyzed by exchanging timing messages between nodes using two-way message exchange schemes. Results: The proposed algorithm is also implemented along with a Time-Sync Protocol for Sensor Network. It reduces error deviation from 2.32 to 0.064 ms as compared with Time-Sync Protocol for Sensor Network without proposed works. Conclusion: It was observed that for a small network, proposed work gives better and efficient results with Time-Sync Protocol for Sensor Network. © 2019 Bentham Science Publishers.
Alleviating data sparsity and cold start in recommender systems using social behaviour
(Institute of Electrical and Electronics Engineers Inc., 2016) Reshma, R.; Ambikesh, G.; Santhi Thilagam, P.S.
Recommender systems are used to find preferences of people or to predict the ratings with the help of information available from other users. The most widely used collaborative filtering recommender system by the e-commerce sites suffers from both the sparsity and cold-start problem due to insufficient data. Most of the existing systems consider only the ratings of the similar users and they do not give any preferences to the social behavior of users which shall aid the recommendations made to the user to a great extent. In this paper, instead of finding similarity from rating information, we propose a new approach which predicts the ratings of items by considering directed and transitive trust with timestamps and profile similarity from the social network along with the user-rated information. In cases where the trust and the rating details of users from the system is absent, we still make use of the social data of the users like the products liked by the user, user's social profile-education status, location etc.To make recommendation. Experimental analysis proves that our approach can improve the user recommendations at the extreme levels of sparsity in user-rating data. We also show that our approach works considerably well for cold-start users under the circumstances where collaborative filtering approach fails. Â© 2016 IEEE.
An abstraction based communication efficient distributed association rule mining
(2008) Santhi Thilagam, P.S.; Ananthanarayana, V.S.
Association rule mining is one of the most researched areas because of its applicability in various fields. We propose a novel data structure called Sequence Pattern Count, SPC, tree which stores the database compactly and completely and requires only one scan of the database for its construction. The completeness property of the SPC tree with respect to the database makes it more suitable for mining association rules in the context of changing data and changing supports without rebuilding the tree. A performance study shows that SPC tree is efficient and scalable. We also propose a Doubly Logaxithmic-depth Tree, DLT, algorithm which uses SPC tree to efficiently mine the huge amounts of geographically distributed datasets in order to minimize the communication and computation costs. DLT requires only O(n) messages for support count exchange and it takes only O(log log n) time for exchange of messages, which increases its efficiency. Â© Springer-Verlag Berlin Heidelberg 2008.
An effective analysis on intrusion detection systems in wireless mesh networks
(Institute of Electrical and Electronics Engineers Inc., 2017) Karri, K.G.; Raju, V.P.; Santhi Thilagam, P.S.
Intrusion Detection Systems(IDSs) are widely used to detect both known attacks and unknown attacks performed by internal and external attackers in wireless networks. However, challenging issues for developing IDSs inWireless Mesh Networks (WMNs) are 1) supporting interoperability and 2) handling volatile parameters. In addition, security standards for WMN are still in draft stage, and to protect the WMN, IDSs of similar wireless networks such as wireless sensor, Ad-Hoc, MANET can be adopted, but the best performance is not guaranteed. In this paper, we have classified the existing IDSs for wireless networks into four categories namely single layer IDS, cross-layer IDS, reputation-based IDS, reputation based cross-layer IDS, and analyzed the performance of these IDSs with core-layer attacks and detection methodology. Based on our analysis, we address the loopholes in existing IDSs and specify research directions for strengthening the existing IDSs and for developing new efficient IDSs with respect to backbone mesh and client mesh networks. Â© 2017 IEEE.
An Efficient Multi-User Searchable Encryption Scheme without Query Transformation over Outsourced Encrypted Data
(Institute of Electrical and Electronics Engineers Inc., 2018) Rao, D.; Siva Kumar, D.V.N.S.; Santhi Thilagam, P.S.
Searchable Encryption (SE) schemes provide security and privacy to the cloud data. The existing SE approaches enable multiple users to perform search operation by using various schemes like Broadcast Encryption (BE), Attribute-Based Encryption (ABE), etc. However, these schemes do not allow multiple users to perform the search operation over the encrypted data of multiple owners. Some SE schemes involve a Proxy Server (PS) that allow multiple users to perform the search operation. However, these approaches incur huge computational burden on PS due to the repeated encryption of the user queries for transformation purpose so as to ensure that users' query is searchable over the encrypted data of multiple owners. Hence, to eliminate this computational burden on PS, this paper proposes a secure proxy server approach that performs the search operation without transforming the user queries. This approach also returns the top-k relevant documents to the user queries by using Euclidean distance similarity approach. Based on the experimental study, this approach is efficient with respect to search time and accuracy. Â© 2018 IEEE.
An efficient search to improve neighbour selection mechanism in P2P network
(2009) Totekar, C.R.; Santhi Thilagam, P.S.
One of the key challenging aspects of peer-to-peer systems has been efficient search for objects. For this, we need to minimize the number of nodes that have to be searched, by using minimum number of messages during the search process. This can be done by selectively sending requests to nodes having higher probability of a hit for queried object. In this paper, we present an enhanced selective walk searching algorithm along with low cost replication schemes. Our algorithm is based on the fact that most users in peer-to-peer network share various types of data in different proportions. This knowledge of amount of different kinds of data shared by each node is used to selectively forward the query to a node having higher hit-ratio for the data of requested type, based on history of recently succeeded queries. Replication scheme replicates frequently accessed data objects on the nodes which get high number of similar queries or closer to the peers from where most of the queries are being issued. Two simple replication schemes have been discussed and their performances are compared. Experimental results prove that our searching algorithm performs better than the selective walk searching algorithm. © 2009 Springer Berlin Heidelberg.
An energy-efficient static multi-hop (ESM) routing protocol for wireless sensor network in agriculture
(Institute of Electrical and Electronics Engineers Inc., 2018) Dubey, A.K.; Upadhyay, D.; Santhi Thilagam, P.S.
Energy efficient routing is a crucial area of research for wireless sensor network(WSN). The communication between the wireless sensor nodes is handled by the routing protocols. The nature of the link, low power and limited recourse makes it a difficult task to design an energy and performance efficient routing protocol for WSN. This paper proposes an energy-efficient route selection protocol for a multi-hop network with a static link for deployment in the field of agriculture. An energy model is also given in this paper to evaluate the energy consumption of the network. The proposed energy-efficient static multi-hop(ESM) routing protocol is evaluated based on the performance parameters for energy efficiency, network lifetime and pack loss and compared with an existing scheme. The simulation results represent that the proposed protocol increases the network throughput and the lifetime. Â© 2018 IEEE.
An RDF approach for discovering the relevant semantic associations in a social network
(2008) A.k, T.; Santhi Thilagam, P.S.
A social network is a network of interactions between entities of social interest like people, organisations, hobbies and transactions. Finding relevant associations between entities in a social network is of great value in many areas like friendship networks, biology and countering terrorism. Semantic web technology enables us to capture and process relationships among social entities as metadata. Analysing semantic social networks requires newer methods. In a social network, entities are connected by short chains of relationships. Query to find associations between two entities returns a large number of results. One of the major issues is to rank the associations as per user preference. The work presents an approach to rank two categories of semantic associations viz. common associations and informative associations. Associations are modelled as property sequences in an RDF graph and they are ranked based on preferred search mode. Heuristics such as i) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a sequence are used to rank associations. The results obtained suggest that these heuristics indeed help in obtaining relevant associations. To scale the results to large RDF graphs, a relevant subgraph is extracted from the input graph on which ranking is applied. The approach is tested successfully on real RDF datasets and multigraphs. Â© 2008 IEEE.
Application of non-linear Gaussian regression-based adaptive clock synchronization technique for wireless sensor network in agriculture
(Institute of Electrical and Electronics Engineers Inc., 2018) Upadhyay, D.; Dubey, A.K.; Santhi Thilagam, P.S.
Efficient and low power utilizing clock synchronization is a challenging task for a wireless-sensor network (WSN). Therefore, it is crucial to design a light weight clock synchronization protocols for these networks. An adaptive clock offset prediction model for WSN is proposed in this paper that exchanges fewer synchronization messages to improve the accuracy and efficiency. Timing information required is collected by setting a small WSN set up to investigate the soil condition to control the irrigation in agriculture. The networks investigate soils moisture, temperature, humidity, and pressure content along with the sensors clock offset. First, the prediction model perceives the existing sensor clock offset to observe the clock characteristics and delay. Then, a Gaussian function is applied for adjusting the parameters weight of the observed value in the prediction model. The system results demonstrate that the proposed adaptive non-linear Gaussian regression synchronization model utilizes 20% less energy as consumed by time sync protocol for sensor-network and reference broadcast synchronization Protocol. It also reduces the synchronization error with respect to root-mean-square error (RMSE) by 24.85% as compared to linear prediction synchronization with RMSE 28.72% in terms of accuracy. © 2001-2012 IEEE.
Applications nature aware virtual machine provisioning in cloud
(Inderscience Publishers, 2018) Achar, R.; Santhi Thilagam, P.S.
Rapid growth of internet technologies and virtualisation has made cloud as a new IT delivery mechanism, which is gaining popularity from both industry and academia. Huge demand for a cloud resources, running similar nature applications in the same server results in application degradation whenever there is a sudden rise in workload. In order to minimise the application degradations, there is an urgent need to know the nature of applications running in cloud for efficient virtual machine (VM) provisioning. Existing cloud architecture does not provide any mechanism to handle this issue. This paper presents a modified cloud architecture which contains additional component called application analyser to identify the nature of applications running in each VM. Based on applications nature, this paper presents a novel VM provisioning mechanism using genetic algorithm. In order to utilise the resources efficiently, this paper also presents a mechanism for VM provisioning with migration. Experimental study is conducted using CloudSim simulator shows that proposed mechanism is efficiently allocating resources to the virtual machines. © 2018 Inderscience Enterprises Ltd.
Black-box detection of XQuery injection and parameter tampering vulnerabilities in web applications
(Springer Verlag service@springer.de, 2018) Deepa, G.; Santhi Thilagam, P.S.; Ahmed Khan, F.A.; Praseed, A.; Pais, A.R.; Palsetia, N.
As web applications become the most popular way to deliver essential services to customers, they also become attractive targets for attackers. The attackers craft injection attacks in database-driven applications through the user-input fields intended for interacting with the applications. Even though precautionary measures such as user-input sanitization is employed at the client side of the application, the attackers can disable the JavaScript at client side and still inject attacks through HTTP parameters. The injected parameters result in attacks due to improper server-side validation of user input. The injected parameters may either contain malicious SQL/XML commands leading to SQL/XPath/XQuery injection or be invalid input that intend to violate the expected behavior of the web application. The former is known as an injection attack, while the latter is called a parameter tampering attack. While SQL injection has been intensively examined by the research community, limited work has been done so far for identifying XML injection and parameter tampering vulnerabilities. Database-driven web applications today rely on XML databases, as XML has gained rapid acceptance due to the fact that it favors integration of data with other applications and handles diverse information. Hence, this work proposes a black-box fuzzing approach to detect XQuery injection and parameter tampering vulnerabilities in web applications driven by native XML databases. A prototype XiParam is developed and tested on vulnerable applications developed with a native XML database, BaseX, as the backend. The experimental evaluation clearly demonstrates that the prototype is effective against detection of both XQuery injection and parameter tampering vulnerabilities. © 2017, Springer-Verlag Berlin Heidelberg.
Clustering and bootstrapping based framework for news knowledge base completion
(Slovak Academy of Sciences, 2021) Srinivasa, K.; Santhi Thilagam, P.S.
Extracting the facts, namely entities and relations, from unstructured sources is an essential step in any knowledge base construction. At the same time, it is also necessary to ensure the completeness of the knowledge base by incrementally extracting the new facts from various sources. To date, the knowledge base completion is studied as a problem of knowledge refinement where the missing facts are inferred by reasoning about the information already present in the knowledge base. However, facts missed while extracting the information from multilingual sources are ignored. Hence, this work proposed a generic framework for knowledge base completion to enrich a knowledge base of crime-related facts extracted from online news articles in the English language, with the facts extracted from low resourced Indian language Hindi news articles. Using the framework, information from any low-resourced language news articles can be extracted without using language-specific tools like POS tags and using an appropriate machine translation tool. To achieve this, a clustering algorithm is proposed, which explores the redundancy among the bilingual collection of news articles by representing the clusters with knowledge base facts unlike the existing Bag of Words representation. From each cluster, the facts extracted from English language articles are bootstrapped to extract the facts from comparable Hindi language articles. This way of bootstrapping within the cluster helps to identify the sentences from a low-resourced language that are enriched with new information related to the facts extracted from a high-resourced language like English. The empirical result shows that the proposed clustering algorithm produced more accurate and high-quality clusters for monolingual and cross-lingual facts, respectively. Experiments also proved that the proposed framework achieves a high recall rate in extracting the new facts from Hindi news articles. © 2021 Slovak Academy of Sciences. All rights reserved.
ClustVariants: An Approach for Schema Variants Extraction from JSON Document Collections
(Institute of Electrical and Electronics Engineers Inc., 2022) Uma Priya, D.; Santhi Thilagam, P.S.
The use of NoSQL Document Stores has grown in recent years as it offers the potential for increased scalability, flexibility, and consistency to store a massive collection of varied structured data in JSON format. Although the document stores do not impose any structural constraint on the data, the lack of schema information challenges efficient data processing, data management, and data integration. Hence, extant research focussed on identifying the global schema for a collection. Nevertheless, it comes at the cost of losing essential benefits of schema such as a detailed structural description of data, query optimization, etc. To address the specific research gap, we propose ClustVariants, a novel approach for discovering the exact schema variants available in a collection. While the complex structure of large heterogeneous JSON data can not be analyzed directly, we resolve this limitation by systematically extract the structure of data, analyze the fields, and cluster the homogeneous documents. We apply a distributed Formal Concept Analysis algorithm, using Apache Spark, to identify the schema variants from a large cluster of JSON documents. The experimental study on real datasets prove that ClustVariants is efficient in inferring exact schema variants of JSON document collections. Â© 2022 IEEE.
DDoS attacks at the application layer: Challenges and research perspectives for safeguarding web applications
(Institute of Electrical and Electronics Engineers Inc., 2019) Praseed, A.; Santhi Thilagam, P.S.
Distributed denial of service (DDoS) attacks are some of the most devastating attacks against Web applications. A large number of these attacks aim to exhaust the network bandwidth of the server, and are called network layer DDoS attacks. They are volumetric attacks and rely on a large volume of network layer packets to throttle the bandwidth. However, as time passed, network infrastructure became more robust and defenses against network layer attacks also became more advanced. Recently, DDoS attacks have started targeting the application layer. Unlike network layer attacks, these attacks can be carried out with a relatively low attack volume. They also utilize legitimate application layer requests, which makes it difficult for existing defense mechanisms to detect them. These attacks target a wide variety of resources at the application layer and can bring a server down much faster, and with much more stealth, than network layer DDoS attacks. Over the past decade, research on application layer DDoS attacks has focused on a few classes of these attacks. This paper attempts to explore the entire spectrum of application layer DDoS attacks using critical features that aid in understanding how these attacks can be executed. defense mechanisms against the different classes of attacks are also discussed with special emphasis on the features that aid in the detection of different classes of attacks. Such a discussion is expected to help researchers understand why a particular group of features are useful in detecting a particular class of attacks. © 2018 IEEE.
Deriving temporal trends in user preferences through short message strings
(Institute of Electrical and Electronics Engineers Inc., 2016) Deb, S.; Mohan, S.; Venkatraman, P.; Bindu, P.V.; Santhi Thilagam, P.S.
Short message strings are widely prevalent in the age of social networking. Taking Facebook as an example, a user may have many other users in his contact list. However, at any given time frame, the user interacts with only a small subset of these users. In this paper, we propose a recommender system that determines which users have common interests based on the content of the short message strings of different users. The system calculates the similarity between two users based on the contents of short message strings by the users over a certain time period. A similarity measure based on short message strings must be temporal study as the contents of the short messages vary rapidly over time. Experimental study is conducted in the Facebook domain using status updates of users. Â© 2016 IEEE.
DetLogic: A black-box approach for detecting logic vulnerabilities in web applications
(Academic Press, 2018) Deepa, G.; Santhi Thilagam, P.S.; Praseed, A.; Pais, A.R.
Web applications are subject to attacks by malicious users owing to the fact that the applications are implemented by software developers with insufficient knowledge about secure programming. The implementation flaws arising due to insecure coding practices allow attackers to exploit the application in order to perform adverse actions leading to undesirable consequences. These flaws can be categorized into injection and logic flaws. As large number of tools and solutions are available for addressing injection flaws, the focus of the attackers is shifting towards exploitation of logic flaws. The logic flaws allow attackers to compromise the application-specific functionality against the expectations of the stakeholders, and hence it is important to identify these flaws in order to avoid exploitation. Therefore, a prototype called DetLogic is developed for detecting different types of logic vulnerabilities such as parameter manipulation, access-control, and workflow bypass vulnerabilities in web applications. DetLogic employs black-box approach, and models the intended behavior of the application as an annotated finite state machine, which is subsequently used for deriving constraints related to input parameters, access-control, and workflows. The derived constraints are violated for simulating attack vectors to identify the vulnerabilities. DetLogic is evaluated against benchmark applications and is found to work effectively. © 2018 Elsevier Ltd
Diffusion models and approaches for influence maximization in social networks
(Institute of Electrical and Electronics Engineers Inc., 2016) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.
Social Network Analysis (SNA) deals with studying the structure, relationship and other attributes of social networks, and provides solutions to real world problems. Influence maximization is one of the significant areas in SNA as it helps in finding influential entities in online social networks which can be used in marketing, election campaigns, outbreak detection, and so on. It deals with the problem of finding a subset of nodes called seeds such that it will eventually spread maximum influence in the network. This paper focuses on providing a complete survey on the influence maximization problem and covers three major aspects: i) different types of input required ii) influence propagation models that map the spread of influence in the network, and iii) the approximation algorithms suggested for seed set selection. We also provide the state of the art and describe the open problems in this domain. Â© 2016 IEEE.
Discovering spammer communities in twitter
(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Bindu, P.V.; Mishra, R.; Santhi Thilagam, P.S.
Online social networks have become immensely popular in recent years and have become the major sources for tracking the reverberation of events and news throughout the world. However, the diversity and popularity of online social networks attract malicious users to inject new forms of spam. Spamming is a malicious activity where a fake user spreads unsolicited messages in the form of bulk message, fraudulent review, malware/virus, hate speech, profanity, or advertising for marketing scam. In addition, it is found that spammers usually form a connected community of spam accounts and use them to spread spam to a large set of legitimate users. Consequently, it is highly desirable to detect such spammer communities existing in social networks. Even though a significant amount of work has been done in the field of detecting spam messages and accounts, not much research has been done in detecting spammer communities and hidden spam accounts. In this work, an unsupervised approach called SpamCom is proposed for detecting spammer communities in Twitter. We model the Twitter network as a multilayer social network and exploit the existence of overlapping community-based features of users represented in the form of Hypergraphs to identify spammers based on their structural behavior and URL characteristics. The use of community-based features, graph and URL characteristics of user accounts, and content similarity among users make our technique very robust and efficient. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.