#Webmon password key generator#

However, it is having some repetitive features which can lessen the efficiency of the malicious URL detection process. At first, the optimal features are extracted from the datasets using DBA-based detector module and then seventy-five optimal rules are generated using Association Rule mining based on these features. In this paper, two datasets (Phishtank and UCI) were considered for malicious URL detection analysis. Phishing is one of the real computer security dangers looked by the digital world and could prompt monetary losses for both industries and people. Phishing is an online extensive fraud, which can trick Internet clients into uncovering their mystery data and qualifications, e.g., login id, secret key, charge card number, and so on. Also, the proposed work has concluded that, the associate algorithm has achieved better accuracy, when compared to other existing methods. The extracted feature is then analyzed with our proposed algorithm to provide better prediction accuracy. The proposed algorithm is used in 7k phishing and real websites content data for performing feature extraction. The updated public online repository called Mendeley dataset is used in the proposed research work.

#Webmon password key generator#

This research article integrates the content-based classification with a rule-based generator algorithm to improve the overall performance of the system. Therefore, the proposed algorithm is associated with the content-based classification method along with the rule-based generator algorithm. This classification struggles in classifying the leaked data content-based challenge. In this scenario, machine learning method is observed as a very effective to classify the phishing and non-phishing web (Uniform Resource Locator) URLs. The confidential data in a fake surrounding has intended the category of leaked data due to the action of attackers.

Many tools are used for anti-phishing purposes they are blacklist and antivirus software. Recently, the classification techniques are employed to detect the phishing websites. Currently, many websites are targeting to acquire the confidential data, which is stored in websites.

In the digital world, phishing assaults are emerging as the most common type of social engineering attack. Moreover, the digital platform is increasing in many developing countries and at the same time, the chance of fraudulence is also increasing day by day. Recently, the internet is becoming as the most effective tool to interact with many foreign societies especially during COVID-19 pandemic. Experiment results indicate the effectiveness of text and image combination in performance improvement, the outperformance of the adaptive fusion method, as well as the potential of this approach when applied to customs classification. Finally, we conduct a case study and comparison experiments based on a group of customs tariff codes and a data set from an e-commerce website. The submodels are fused by a novel method, which can adjust the value of parameters according to the model training result. The proposed model includes two independent submodels: one for text and the other for image. Thus, in this paper, we propose a text-image adaptive convolutional neural network to effectively utilize website information and facilitate the customs classification process. The current abundant e-commence data and advanced machine learning techniques provide an opportunity for cross-border e-commerce sellers to classify goods efficiently. Proper classification of such goods with high efficiency in light of the rapidly increasing amount of international trade is still challenging. Customs classification is an essential international procedure to import cross-border goods traded by various companies and individuals.