Malware distribution networks are a huge network that involves in malware distribution. Our experimental results show that the proposed methods can improve the accuracy and efficiency in the classification of malicious webpage detections. To increase the detection accuracy for malicious webpages, two methods of filling missing values are presented to process the null attribute values of webpages.We compare the performance of our algorithms when the different methods are applied in terms of the information gain ratio, classification accuracy, and detection efficiency. To overcome this challenge, a Markov detection tree scheme is proposed in this paper to automatically identify and classify malicious webpages, where the link relations of unified resource locators (URL), the information gain ratio, and Markov decision process as well as decision tree are used to analyze malicious webpages simultaneously. However, the detection results of current methods are poor and their efficiency is low, and thus, it is important and challenging to design an efficient detection scheme that can improve the accuracy of classification of malicious webpages. The effective detection of malicious webpages plays a paramount role in ensuring the web security on the Internet. The outcomes show that the proposed Fuzzy Deep Neural Network classifier accomplishes the most extreme accuracy in the prediction analysis of phishing, non-phishing, and suspicious URL. To beat this issue, the Frequent Rule Reduction algorithm along with the classification approach for predicting Phishing Websites should be implemented.
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. 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.
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