Qualification: 
MS, BSc
padmamala@am.amrita.edu

Padmamala Sriram currently serves as the Assistant Professor (Selection Grade) at the Department of Computer Science Engineering at Amrita School of Engineering, Amritapuri. She has completed M. S. in Computer Science from California State University. She has 14 years of academic experience and 4 years of industrial experience.

Publications

Publication Type: Conference Paper

Year of Publication Title

2016

B. Pillai, Mounika, M., Rao, P. J., and Padmamala Sriram, “Image steganography method using K-means clustering and encryption techniques”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.[Abstract]


Steganography involves hiding of text, image or any sensitive information inside another image, video or audio in such a way that an attacker will not be able to detect its presence. Steganography is, many times, confused with cryptography as both the techniques are used to secure information. The difference lies in the fact that steganography hides the data so that nothing appears out of ordinary while cryptography encrypts the text, making it difficult for an outsider to infer anything from it even if they do attain the encrypted text. Both of them are combined to increase the security against various malicious attacks. Image Steganography uses an image as the cover media to hide the secret message. In this paper, we propose an image steganography method which clusters the image into various segments and hides data in each of the segment. Various clustering algorithms can be used for image segmentation. Segmentation involves huge set of data in the form of pixels, where each pixel further has three components namely red, green and blue. K-means clustering technique is used to get accurate results. Therefore, we use K-means clustering technique to get accurate results in a small time period.

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Publication Type: Conference Proceedings

Year of Publication Title

2016

Pa Seshagiri, Vazhayil, Ab, and Padmamala Sriram, “AMA: Static Code Analysis of Web Page for the Detection of Malicious Scripts”, Proceedings of the 6th International Conference on Advances in Computing & Communications 2016, Procedia Computer Science, vol. 93. Elsevier, pp. 768-773, 2016.[Abstract]


JavaScript language, through its dynamic feature, provides user interactivity with websites. It also pose serious security threats to both user and website. On top of this, obfuscation is widely used to hide its malicious purpose and to evade the detection of antivirus software. Malware embedded in web pages is regularly used as part of targeted attacks. To hinder detection by antivirus scanners, the malicious code is usually obfuscated, often with encodings like hexadecimal, unicode, base64, escaped characters and rarely with substitution ciphers like Vigenere, Caesar and Atbash. The malicious iframes are injected to the websites using JavaScript and are also made hidden from the users perspective in-order to prevent detection. To defend against obfuscated malicious JavaScript code, we propose a mostly static approach called, AMA, Amrita Malware Analyzer, a framework capable of detecting the presence of malicious code through static code analysis of web page. To this end, the framework performs probable plaintext attack using strings likely contained in malicious web pages. But this approach targets only few among many possible obfuscation strategies. The evaluation based on the links provided in the Malware domain list demonstrates high level accuracy. © 2016 The Authors. Published by Elsevier B.V.

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2014

R. Sasidharan and Padmamala Sriram, “Hyper-Quadtree-Based K-Means Algorithm for Software Fault Prediction”, Advances in Intelligent Systems and Computing, International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3-2013, vol. 246. Springer India, Coimbatore; India, pp. 107-118, 2014.[Abstract]


Software faults are recoverable errors in a program that occur due to the programming errors. Software fault prediction is subject to problems like non-availability of fault data which makes the application of supervised technique difficult. In such cases, unsupervised techniques are helpful. In this paper, a hyper-quadtree-based K-means algorithm has been applied for predicting the faults in the program module. This paper contains two parts. First, the hyper-quadtree is applied on the software fault prediction dataset for the initialization of the K-means clustering algorithm. An input parameter Δ governs the initial number of clusters and cluster centers. Second, the cluster centers and the number of cluster centers obtained from the initialization algorithm are used as the input for the K-means clustering algorithm for predicting the faults in the software modules. The overall error rate of this prediction approach is compared with the other existing algorithms.

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Faculty Research Interest: