Faculty Publications

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Publications by NITK Faculty

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    A novel Multi-Threaded K-Means clustering approach for intrusion detection
    (2012) Pathak, V.; Ananthanarayana, V.S.
    Due to the proliferation of high-speed internet access, more and more organizations are becoming vulnerable to potential cyber-attacks. An intrusion is defined as any set of actions that compromise the integrity, confidentiality or availability of a resource. Intrusion Detection System (IDS), as the main security defending technique, is widely used against malicious attacks. IDS system should be good enough to detect existing attacks as well as novel attacks at high speed. Thus to fulfil these requirements a new novel Multi-Threaded K-Means clustering approach has been used which has resulted in high detection rate and low false alarm rate. A subset of KDD99 Data set has been used as an input dataset for experiments. © 2012 IEEE.
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    Leveraging Structural and Semantic Measures for JSON Document Clustering
    (IICM, 2023) Uma Priya, D.; Santhi Thilagam, P.S.
    In recent years, the increased use of smart devices and digital business opportunities has generated massive heterogeneous JSON data daily, making efficient data storage and management more difficult. Existing research uses different similarity metrics and clusters the documents to support the above tasks effectively. However, extant approaches have focused on either structural or semantic similarity of schemas. As JSON documents are application-specific, differently annotated JSON schemas are not only structurally heterogeneous but also differ by the context of the JSON attributes. Therefore, there is a need to consider the structural, semantic, and contextual properties of JSON schemas to perform meaningful clustering of JSON documents. This work proposes an approach to cluster heterogeneous JSON documents using the similarity fusion method. The similarity fusion matrix is constructed using structural, semantic, and contextual measures of JSON schemas. The experimental results demonstrate that the proposed approach outperforms the existing approaches significantly. © 2023, IICM. All rights reserved.