Faculty Publications
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Item Non-local means image denoising using shapiro-wilk similarity measure(Institute of Electrical and Electronics Engineers Inc., 2018) Yamanappa, W.; Sudeep, P.V.; Sabu, M.K.; Rajan, J.Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method. © 2013 IEEE.Item Keyword-based private searching on cloud data along with keyword association and dissociation using cuckoo filter(Springer Verlag service@springer.de, 2019) Vora, A.V.; Hegde, S.Outsourcing of data is a very common scenario in the present-day world and quite often we need to outsource confidential data whose privacy is of utmost concern. Performing encryption before outsourcing the data is a simple solution to preserve privacy. Preferably a public-key encryption technique is used to encrypt the data. A demerit of encrypting data is that while requesting the data from the cloud we need to have some technique which supports search functionality on encrypted data. Without the searchable encryption technique, the cloud is forced to send the whole database, which is highly inefficient and impractical. To address this problem, we consider the email scenario, in which the sender of the email will encrypt email contents using receiver’s public key; hence, only the receiver can decrypt email contents. We propose a scheme that will have encrypted emails stored on the cloud and have capabilities that support searching through the encrypted database. This enables the cloud to reply to a request with a more precise response without compromising any privacy in terms of email contents and also in terms of access patterns. We provide a solution for the email scenario in which we can tag or associate emails with some keywords, and during retrieval, the email owner can request all the emails associated with a particular keyword. Although attempts are seen in the literature to solve this issue they do not have the flexibility of dissociating keywords from an email. Keyword dissociation is essential to modify the association between keywords and emails to enable better filtering of emails. Our technique also supports the functionality of keyword dissociation. The solution allows single-database private information retrieval writing in an oblivious way with sublinear communication cost. We have theoretically proved the correctness and security of our technique. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Item Applicability of machine learning in spam and phishing email filtering: review and approaches(Springer Science+Business Media B.V. editorial@springerplus.com, 2020) Gangavarapu, T.; Jaidhar, C.D.; Chanduka, B.With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper. © 2020, Springer Nature B.V.Item Classification of Phishing Email Using Word Embedding and Machine Learning Techniques(River Publishers, 2022) Somesha, M.; Pais, A.R.Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.Item A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites(Institute of Electrical and Electronics Engineers Inc., 2023) Lakshmana Rao, L.R.; Rao, R.S.; Pais, A.R.; Gabralla, L.A.Phishing is a type of online scam where the attacker tries to trick you into giving away your personal information, such as passwords or credit card details, by posing as a trustworthy entity like a bank, email provider, or social media site. These attacks have been around for a long time and unfortunately, they continue to be a common threat. In this paper, we propose a boosting based multi layer stacked ensemble learning model that uses hybrid feature selection technique to select the relevant features for the classification. The dataset with selected features are sent to various classifiers at different layers where the predictions of lower layers are fed as input to the upper layers for the phishing detection. From the experimental analysis, it is observed that the proposed model achieved an accuracy ranging from 96.16 to 98.95% without feature selection across different datasets and also achieved an accuracy ranging from 96.18 to 98.80% with feature selection. The proposed model is compared with baseline models and it has outperformed the existing models with a significant difference. © 2013 IEEE.Item DeepEPhishNet: a deep learning framework for email phishing detection using word embedding algorithms(Springer, 2024) Somesha, M.; Pais, A.R.Email phishing is a social engineering scheme that uses spoofed emails intended to trick the user into disclosing legitimate business and personal credentials. Many phishing email detection techniques exist based on machine learning, deep learning, and word embedding. In this paper, we propose a new technique for the detection of phishing emails using word embedding (Word2Vec, FastText, and TF-IDF) and deep learning techniques (DNN and BiLSTM network). Our proposed technique makes use of only four header based (From, Returnpath, Subject, Message-ID) features of the emails for the email classification. We applied several word embeddings for the evaluation of our models. From the experimental evaluation, we observed that the DNN model with FastText-SkipGram achieved an accuracy of 99.52% and BiLSTM model with FastText-SkipGram achieved an accuracy of 99.42%. Among these two techniques, DNN outperformed BiLSTM using the same word embedding (FastText-SkipGram) techniques with an accuracy of 99.52%. © Indian Academy of Sciences 2024.