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Aswani Kumar Cherukuri

Bio: Aswani Kumar Cherukuri is an academic researcher from VIT University. The author has contributed to research in topics: Formal concept analysis & Knowledge extraction. The author has an hindex of 9, co-authored 33 publications receiving 294 citations.

Papers
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Journal ArticleDOI
TL;DR: The results obtained from the proposed method are in good agreement with Levenshtein distance method and interval–valued fuzzy formal concepts method but with less computational complexity.
Abstract: In this paper we propose a method for reducing the number of formal concepts in formal concept analysis of data with fuzzy attributes. We compute the weight of fuzzy formal concepts based on Shannon entropy. Further, the number of fuzzy formal concepts is reduced at chosen granulation of their computed weight. We show that the results obtained from the proposed method are in good agreement with Levenshtein distance method and interval–valued fuzzy formal concepts method but with less computational complexity.

77 citations

Journal ArticleDOI
30 Jun 2016
TL;DR: A hybrid intrusiondetection model by integrating the principal component analysis (PCA) and support vector machine (SVM) and automatic parameter selection technique is proposed, which performs better with higher accuracy, faster convergence speed and better generalization.
Abstract: Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS) such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA) and support vector machine (SVM). The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor ( C ) and kernel parameter gamma ( γ ), thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed. ACM CCS (2012) Classification : Security and privacy → Intrusion/anomaly detection and malware mitigation → Intrusion detection systems *To cite this article: S. T. Ikram and A. K. Cherukuri, "Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM", CIT. Journal of Computing and Information Technology , vol. 24, no. 2, pp. 133–148, 2016.

61 citations

Journal ArticleDOI
TL;DR: The FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall and an extra operator namely negative operator is added to achieve this objective.
Abstract: Human brain represents the information and stores it as memory. They are stored in different parts of the brain and are linked together by associations. When a cue is provided, the memory is recalled through association. Encoding of the real world information is in the form of object-attribute relation. It is possible to perform both positive recall (object having the attribute and attribute shared by object) and negative recalls (object not having the attribute and attribute not shared by object) from memory. It is evident from literature that the formal concept analysis (FCA) based on bidirectional associative memory (BAM) performs only positive recall from memory. In this paper, FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall. In this extended model, both positive recall and negative recall are performed. In order to achieve this objective, an extra operator namely negative operator is added. The proposed model is validated with an experiment on real world scenario. We also presented the connection of the proposal with long term potentiation (LTP) and Hippocampus of the human brain.

57 citations

Journal ArticleDOI
TL;DR: A study and analysis of quality parameters of recommendation systems for LBSN with big data and a few quality parameters like parallel processing and multimodal interface have been selected.
Abstract: Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN). A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.

28 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present efficient algorithms using Apache Spark for both formal concept generation and concept lattice digraph construction in large formal contexts, which are more efficient for concept generation than existing algorithms.
Abstract: In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying the formal concepts and constructing the concept lattice (digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in large datasets, various distributed algorithms are available. However, the existing distributed algorithms are not well suited for concept generation, because the generation of concepts is an iterative process. Existing algorithms are implemented using distributed frameworks like MapReduce and Open MP. These frameworks are not appropriate for iterative applications. Hence, there is a need for efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts. In this paper, we present efficient algorithms using Apache Spark. The various performance metrics used in evaluation prove that the proposed algorithms are more efficient for concept generation and lattice graph construction than existing algorithms.

22 citations


Cited by
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Journal ArticleDOI
TL;DR: The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks.

321 citations

Journal ArticleDOI
TL;DR: This review is addressing an analytical survey of the current and potential application of Internet of Things in arable farming, where spatial data, highly varying environments, task diversity and mobile devices pose unique challenges to be overcome compared to other agricultural systems.

195 citations

Proceedings ArticleDOI
25 Jun 2018
TL;DR: This paper proposes a Gated Recurrent Unit Recurrent Neural Network enabled intrusion detection systems for SDNs and concludes that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.
Abstract: Software Defined Networking (SDN) has emerged as a key enabler for future agile Internet architecture. Nevertheless, the flexibility provided by SDN architecture manifests several new design issues in terms of network security. These issues must be addressed in a unified way to strengthen overall network security for future SDN deployments. Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs. The proposed approach is tested using the NSL-KDD dataset, and we achieve an accuracy of 89% with only six raw features. Our experiment results also show that the proposed GRU-RNN does not deteriorate the network performance. Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.

190 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an overview of unsupervised learning in the domain of networking, and provide a comprehensive review of the current state of the art in this area, by synthesizing insights from previous survey papers.
Abstract: While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

182 citations

Journal ArticleDOI
Yiyu Yao1
TL;DR: This paper presents a perception–cognition–action (PCA) tri-level conceptual model that is applicable to studying intelligent data analytics, intelligent systems, and human understanding.
Abstract: The underlying philosophy of three-way decision is thinking in threes, namely, understanding and processing a whole through three distinct and related parts. One can formulate many concrete models of three-way decision to account for different interpretations of the three parts. By interpreting the three parts as three levels, this paper investigates tri-level thinking to build concrete models of three-way decision. We examine some fundamental issues and basic ingredients of tri-level thinking. In accordance with the data–information–knowledge–wisdom (DIKW) hierarchy, we present a perception–cognition–action (PCA) tri-level conceptual model that is applicable to studying intelligent data analytics, intelligent systems, and human understanding.

150 citations