Siva S. Sivatha Sindhu

Anna University, Chennai, Chennai, Tamil Nādu, India

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Publications (23)0 Total impact

  • Siva S. Sivatha Sindhu, S. Geetha, A. Kannan
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    ABSTRACT: The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.
    International Journal of Systems Science. 12/2012; 43(12):2334-2350.
  • Siva S. Sivatha Sindhu, S. Geetha, A. Kannan
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    ABSTRACT: The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. Therefore in this work, the design of IDS is investigated from these three perspectives. The goals of this paper are (i) removing redundant instances that causes the learning algorithm to be unbiased (ii) identifying suitable subset of features by employing a wrapper based feature selection algorithm (iii) realizing proposed IDS with neurotree to achieve better detection accuracy. The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features. An extensive experimental evaluation of the proposed approach with a family of six decision tree classifiers namely Decision Stump, C4.5, Naive Baye’s Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern has been introduced.
    Expert Syst. Appl. 01/2012; 39:129-141.
  • S. Geetha, Siva S. Sivatha Sindhu, S. Barani Priya, S. Mubakiya, N. Kamaraj
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    ABSTRACT: Biometric watermarking refers to the process of incorporating the handwritten signatures or fingerprints in watermarking technology. In this paper, we present a novel statistical attack invariant watermarking scheme to embed an offline handwritten signature invisibly in the host as a notice of genuine ownership. The scheme employs Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) with Least Significant Bit (LSB) encoding for watermark insertion, which is to be known as DCT-SVD scheme. Experimental results confirm that DCT-SVD scheme is robust to statistical attacks even in the presence of deliberate distortions.
    01/2011;
  • S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications: 1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2) increasing the sensitivity and specificity of the system by the feature reduction phase; 3) realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods. KeywordsImage steganalysis-curvelet higher order statistics-neuro-C4.5 classifier-information forensics-information security
    International Journal of Automation and Computing 11/2010; 7(4):531-542.
  • Siva S. Sivatha Sindhu, A. Kannan
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    ABSTRACT: With the rapid growth of Internet communication and the availability of tools to intrude the network, an intrusion detection system (IDS) has become indispensable. Clustering algorithm utilize a distance metric in order to partition data points such that patterns within a single group have the same characteristics from those in a different group. The proposed system builds a clustering engine using genetic-X-means that can assign each new event to a cluster to determine its type. This is in contrast to approaches used by existing clustering-based IDSs, which require the number of attack types in advance. Genetic-X-means handle recently evolving attacks by clustering them into respective classes, and if the attack pattern deviates largely from the existing cluster it is grouped into a new class. Genetic paradigm employs a weighted sum fitness function to choose the predominant features, which reveals the occurrence of intrusions. The weighted sum fitness function used here is dependent on problem instance and not just on the problem class. As the data patterns include categorical attributes, an influence calculation formula which converts categorical attribute to numerical attribute is proposed. The experimental results obtained in this work show that the system attains improvement in terms of detection rate when compared to a conventional IDS. Experiments show that this system can be deployed in a real network or database environment for effective detection of both existing and new attacks.
    Information Security Journal: A Global Perspective. 01/2010; 19:204-212.
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    ABSTRACT: This paper proposes a reliable framework for the detection of the least significant bit (LSB) steganography using digital media files as cover objects. Steganographic methods attempt to insert data in multimedia signals in an undetectable fashion. However, these methods often disrupt the underlying signal characteristics, thereby allowing detection under careful steganalysis. Under repeated embedding, disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. This principle is used to derive a steganalysis tool that detects the presence of hidden messages in uncompressed twenty-four bits BMP image. This work presents close color pair analysis with stego-sensitive threshold (CCPASST) to detect stego-objects with even 10% payload. In earlier works 20% payload was detected through close color pair analysis. The new framework exploits the first-order statistics of structural similarity index measure of the samples to calculate the threshold. The literature contains only one other detector specialized with variable threshold, and the one presented here is substantially more sensitive. Simulation results with the stego-sensitive threshold applied on well-known LSB steganographic technique indicate that this approach is superior to the earlier methods and is able with promising accuracy to distinguish between clean and stego images.
    Computer Vision, Graphics & Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on; 01/2009
  • S. Geetha, S. Sindhu, N. Kamaraj
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    ABSTRACT: Steganography is used to hide the occurrence of communication. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous images (stego image) from pure images (cover image) is difficult and tedious. This paper investigates the use of a Genetic-X-means classifier, which distinguishes a pure image from the adulterated one. The basic idea is that, the various Image Quality Metrics (IQMs) calculated on cover images and on stego-images vis-a-vis their denoised versions, are statistically different. Our model employs these IQMs to steganalyse the image data. Genetic paradigm is exploited to select the IQMs that are sensitive to various embedding techniques. The classifier between cover and stego-files is built using X-means clustering on the selected feature set. The presented method can not only detect the presence of hidden message but also identify the hiding domains. The experimental results show that the combination strategy (Genetic-X-means) can improve the classification precision even with lesser payload compared to the traditional ANN (Back Propagation Network).
    Advanced Computing and Communications, 2008. ADCOM 2008. 16th International Conference on; 01/2009
  • S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: This paper reports the design principles and evaluation results of a new experimental universal, blind image steganalysing system. This system investigates the use of content independent statistical evidences left by the steganograms, as features for an image steganalyzer. The work is aimed at maximizing the sensitivity and specificity of the steganalyzer and to accomplish both security and system performance. A genetic-X-means classifier is constructed to realize the proposed model. For performance evaluation, a database composed of 5600 plain and stego images (generated by using seven different embedding schemes) was established. The results of our empirical experiment prove the vitality of the proposed scheme in detecting stego anomalies in images. In addition, the simulation results show that the effectiveness of steganalytic system can be enhanced by considering the content independent distortion measures and maximizing the sensitivity and specificity of the system.
    Computers & Security. 01/2009; 28:683-697.
  • S. Geetha, Siva S. Sivatha Sindhu, V. Kabilan, N. Kamaraj
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    ABSTRACT: Steganalysis has emerged as an important branch in information forensics. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviors, optimizing the performance of steganalysers becomes an important open problem. This paper is aimed at increasing the performance of the steganalysers in through feature selection thereby reducing the computational complexity and increase the classification accuracy of the selected feature subsets. In this study, we propose to employ Markov blanket-embedded genetic algorithm (MBEGA) for stego sensitive feature selection process. In particular, the embedded Markov blanket based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. Experimental results prove that the proposed method is superior in terms of number of selected features, classification accuracy, and running time than the existing algorithms.
    First International Conference on Networks and Communications, NetCoM 2009, Chennai, India, December 27-29, 2009; 01/2009
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    ABSTRACT: Techniques for information hiding and steganography are becoming increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages is also becoming considerably more difficult. In this paper, we describe a universal approach to steganalyse the least significant bit steganography method for detecting the presence of hidden messages embedded within digital images. The proposed work uses the 27 features that are calculated from the three different statistical moments i.e., PDF, CF and Absolute moment calculated from wavelet multi-resolution representation of the images. We have presented the efficacy of our approach on a large collection of images, and on four different LSB steganographic embedding algorithms. Four soft computing techniques viz., Support Vector Machine, Nai¿ve Bayes classifier, Decision Tree Classifier and K-nearest neighbor classifier are employed to efficiently distinguish the pure image from the anomalous or stego image file. The proposed steganalysis algorithm can steadily achieve a correct classification rate of over 90% thus indicating significant achievement in steganalysis.
    01/2009;
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    S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
    Informatica (Slovenia). 01/2009; 33:25-40.
  • S. Geetha, S.S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: Differentiating anomalous image documents (stego image) from pure image file (cover image) is difficult and tedious. Steganalytic techniques strive to detect whether an image contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to image steganalysis. The basic idea is that, the various image quality metrics calculated on cover image files and on stego-image files vis-a-vis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from image data using these image quality metrics and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the image documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to generally detect any new steganography technique. The implementation of the GA based image steganalyzer relies on the choice of these image quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched image samples. Experimental results show that the proposed technique provides promising detection rates.
    Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on; 01/2008
  • S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: Steganography, the means for covert communication, creates a potential problem when it is misused for planning criminal activities. Differentiating anomalous audio document from pure audio document is difficult. This paper presents a Genetic Algorithm based approach to audio steganalysis. The basic idea is that the various audio quality metrics calculated on cover audio signals and on stego audio signals vis‐a‐vis their denoised versions are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and the support‐confidence framework is utilised as a fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real‐time environment Experimental results show that the proposed technique provides promising detection rates.
    International Journal of Signal and Imaging Systems Engineering 01/2008; 1(2).
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    S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: We present a novel technique for effective steganalysis of high-color-depth digital images that have been subjected to embedding by LSB steganographic algorithms. The detection theory is based on the idea that under repeated embedding, the disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. That is the marginal distortions due to repeated embeddings decrease monotonically. This decreasing distortion property exploited with Close Color Pair signature is used to construct the classifier that can distinguish between stego and cover images. For evaluation, a database composed of 1200 plain and stego images (at 10% and 20% payload and each one artificially adulterated with 20% additional data) was established. Based on this database, extensive experiments were conducted to prove the feasibility of our proposed system. Our main results are (i) a 90%+ positive-detection rate; (ii) Close Color Pair ratio is not modified significantly when additional bit streams are embedded into a test image that is already tampered with a message.; (iii) an image quality metric Czenakowski Measure, that is substantially sensitive to LSB embedding is utilized to derive the effective image adaptive threshold; (iv) capable of detecting stego images with an embedding of even 10% payload while the earlier methods can achieve the same detection rate only with 20% payload.
    Transactions on Data Privacy. 01/2008; 1:140-161.
  • S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
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    ABSTRACT: Steganography is used to hide the occurrence of communication. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. This paper investigates the use of a Genetic-X-means classifier, which distinguishes a pure audio document from the adulterated one. The basic idea is that, the various audio quality metrics (AQMs) calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. Our model employs these AQMs to steganalyse the audio data. Genetic paradigm is exploited to select the AQMs that are sensitive to various embedding techniques. The classifier between cover and stego-files is built using X-means clustering on the selected feature set. The presented method can not only detect the presence of hidden message but also identify the hiding domains. The experimental results show that the combination strategy (Genetic-X-means) can improve the classification precision even with lesser payload compared to the traditional ANN (Back Propagation Network).
    01/2008;
  • S.S.S. Sindhu, S. Geetha, S.S. Sivanath, A. Kannan
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    ABSTRACT: Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originated inside the organizations is increasing steadily. Attacks made in this way, usually done by "authorized" users of the system, cannot be immediately traced. As the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. This paper presents a framework for a statistical anomaly prediction system using a neuro-genetic forecasting model, which predicts unauthorized invasions of user, based on previous observations and takes further action before intrusion occurs. We propose an evolutionary time-series model for adaptive network intrusion forecasting where the ANN (Artificial Neural Network) is trained using genetic algorithm. The learning of the ANN is formulated as a weight optimization problem. The experimental results show that the combination strategy (neuro-genetic) can quicken the learning speed of the network and improve the predicting precision compared to the traditional ANN (Back Propagation Network). A comparative evaluation of the proposed neuro-genetic model with the traditional back-propagation, on audit data set provided by MIT Lincoln labs, has been presented and a better prediction accuracy has been observed.
    Advanced Computing and Communications, 2006. ADCOM 2006. International Conference on; 01/2007
  • S. Geetha, S.S.S. Sindhu, A. Kannan
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    ABSTRACT: Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to audio steganalysis. The basic idea is that, the various audio quality metrics calculated on cover audio signals and on stego audio signals vis-avis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method provides flexibility to generally detect any new steganography technique. The implementation of the GA based audio steganalyzer relies on the choice of these audio quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates.
    Digital Information Management, 2006 1st International Conference on; 01/2007
  • S. Geetha, S.S. Sivatha Sindhu, A. Kannan
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    ABSTRACT: The goal of steganography is to avoid drawing suspicion to the transmission of a hidden message in multi-medium. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper investigates the use of genetic algorithm (GA) to aid autonomous intelligent software agents capable of detecting any hidden information in audio files, automatically. This agent would make up the detection agent in an architecture comprising of several different agents that collaborate together to detect the hidden information. The basic idea is that, the various audio quality metrics (AQMs) calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. GA employs these AQMs to steganalyse the audio data. The overall agent architecture will operate as an automatic target detection (ATD) system. The architecture of ATD system is presented in this paper and it is shown how the detection agent fits into the overall system. The design of ATD based audio steganalyzer relies on the choice of these audio quality measures and the construction of a GA based rule generator, which spawns a set of rules that discriminates between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates
    India Conference, 2006 Annual IEEE; 10/2006
  • S. Geetha, S.S.S. Sindhu, A. Kannan
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    ABSTRACT: This paper investigates the use of Support Vector Machines (SVM) to create and train agents capable of detecting any hidden information in Audio files. This agent would make up the Detection Agent in an architecture comprising of several different agents that collaborate together to detect the hidden information. The system exploits a soft computing approach to detect the presence of hidden messages in audio signals, by using the Audio Quality Metrics. The distribution of various statistical distance measures, calculated on cover audio signals and on stego-audio signals vis-à-vis their denoised versions, are different. The overall agent architecture will operate as an Automatic Target Detection (ATD) system. The architecture of ATD system is presented in this paper and it is shown how the detection agent fits into the overall system. The design of ATD based audio steganalyzer relies on the choice of these audio quality measures and the construction of a SVM classifier, which will discriminate between the adulterated and the untouched audio samples.
    Intelligent Sensing and Information Processing, 2005. ICISIP 2005. Third International Conference on; 01/2006
  • S. Geetha, S.S. Sivatha Sindhu, S. Gobi, A. Kannan
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    ABSTRACT: Differentiating anomalous audio document (Stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to audio steganalysis, and the software implementation of the approach. The basic idea is that, the various audio quality metrics calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and fitness function is used to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to generally detect any new steganography technique. The implementation of the GA based audio steganalyzer relies on the choice of these audio quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates.
    Intelligent Sensing and Information Processing, 2006. ICISIP 2006. Fourth International Conference on; 01/2006