Preventing phishing attacks using text and image watermarking

Hajiali, Mahdi; Amirmazlaghani, Maryam; Kordestani, Hossain

doi:10.1002/cpe.5083

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Selection of the suitable replica and accessing a data file become a significant issue in any distributed environment. This process can decrease the probability of the attack and enhance security [26]. Knowledge about the capabilities and performance features of a storage system is a fundamental issue for replica selection.…”

Section: Proposed Methodsmentioning

confidence: 99%

Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Wakil

Nazif²,

Panahi

et al. 2019

IET Communications

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Section: Proposed Methodsmentioning

confidence: 99%

Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Wakil

Nazif²,

Panahi

et al. 2019

IET Communications

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“…A systematic measure of the infiltration process involved in phishing detection along with the application for chrome extension, namely, Sniff-Phish, was developed in [18], where the computational time involving the resource-intensive model in cloud was designed to detect several types of phishing attacks, thus minimizing the false positive significantly. Text and image watermarking tools [19] were used as means for detecting between phishing attack and normal traffic. Here, watermark was used in the client side and was found to be fool proof.…”

Section: Structurementioning

confidence: 99%

Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

Jenni¹,

Shankar²

2022

Computer Systems Science and Engineering

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The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable. Phishing websites also create traffic in the entire network. Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed. Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state. The upcoming big data system requires MapReduce, a popular parallel programming, to process massive datasets. To address these issues, a probabilistic latent semantic and greedy levy gradient boosting (PLS-GLGB) algorithm for website phishing detection using MapReduce is proposed. A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection. Here, the missing data in each URL are identified and discarded for further processing to ensure data quality. Subsequently, with the preprocessed features (URLs), feature vectors are updated by the greedy levy divergence gradient (model) that selects the optimal features in the URL and accurately detects the websites. Thus, greedy levy efficiently differentiates between phishing websites and legitimate websites. Experiments are conducted using one of the largest public corpora of a website phish tank dataset. Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods. Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.

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“…Color image watermarking is quite like the grayscale watermarking, but only the difference is, there are three color components like red, green, and blue. Recently, the interest in security of copyrights have been enhanced in several fields like military, bioinformatics, medical, 16 biometric, 13 and webserver applications, 17 and so on.…”

Section: Introductionmentioning

confidence: 99%

A new video watermarking using redundant discrete wavelet in singular value decomposition domain with multi‐objective optimization

Kumar¹,

Srinivas²,

Prabhakar

2021

Concurrency and Computation

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Day by day, the transmission or sharing of digital information online is increasing due to the fact that Internet usage has become somewhat of an addiction for the majority of people. This results in larger copyright violation problems. Nowadays, most of the data or information is being transmitted in the form of digital images or videos, which are rather simple for the violators of copyright to forge or fake and then share for profit. Thus, digital watermarking came into existence as a possible solution to address these violations of copyright. This article proposes a new blind video watermarking (BVW) using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain, which utilizes the advantages of both RDWT and SVD to embed and extract the watermark information into the cover video without degrading the quality of the watermark. In addition, an efficient meta‐heuristic optimization with multi‐objective optimization (MOO) is employed to further optimize the proposed RDWT‐SVD approach. Further, various attacks were applied to the watermarked frame to disclose the robustness of proposed BVW using RDWT‐SVD with MOO approach. Extensive simulations on several test videos with comparison to the conventional BVW methodologies exhibit the transcendency of the proposed BVW using RDWT‐SVD with MOO approach in terms of watermarking quality evaluation metrics such as peak signal‐to‐noise ratio (PSNR), structural similarity index, normalized correlation, and even that of root mean square error as well.

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Preventing phishing attacks using text and image watermarking

Cited by 7 publications

References 27 publications

Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

A new video watermarking using redundant discrete wavelet in singular value decomposition domain with multi‐objective optimization

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