Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network

Wang, Huanhuan; Yu, Long; Tian, Shengwei; Peng, Yongfang; Pei, Xinjun

doi:10.1007/s10489-019-01433-4

Cited by 36 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN was used as a single classifier in numerous research to distinguish between phishing and legitimate websites [7,8,20,[24][25][26][27][28]. It can also be used in combination with other DL techniques to form an ensemble model and to improve phishing detection accuracy [10,11,[29][30][31][32][33][34][35][36]. The difference between the architectures of CNN and DNN is the use of convolutional layers and kernels.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…While this problem was avoided in [10], details of optimizing these parameters were not provided in the paper. Similarly, the authors of [24,28,29,32] described the optimization process, but only on certain parameters, for example, the number of convolutional layers, number of kernels, and kernel size. Additionally, in terms of performance metrics, it was observed that accuracy, precision, recall, and F1-score were the most common measures [7,24,28,[30][31][32]34,35,37,38].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

See 1 more Smart Citation

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.

show abstract

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Some recent works focus on deep learning methods. For example, Peng et al proposed a joint CNN and LSTM based attention mechanism [25]; Mahudeswaran and Liu combined RNN and LSTM [20] and Wang et al put forward a bidirectional LSTM algorithm based on CNN and independent RNN [22]. These research proved their method best in their data sets.…”

Section: B Classical Machine Learning Techniquesmentioning

confidence: 99%

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Liu

Lee

2020

IEEE Access

View full text Add to dashboard Cite

Although a variety of techniques to detect malicious websites have been proposed, it becomes more and more difficult for those methods to provide a satisfying result nowadays. Many malicious websites can still escape detection with various Web spam techniques. In this paper, we first summarize three types of Web spam techniques used by malicious websites, such as redirection spam, hidden IFrame spam, and content hiding spam. We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. In order to verify the effectiveness of the method, two different experiments have been conducted. First, the proposed method was tested based on a constructed complex dataset. We present comparison results between the proposed method and representative machine learning-based detection algorithms. Second, the proposed method was tested to detect malicious websites in a real-world Web environment for three months. These experimental results illustrate that the proposed method has a better performance and is applicable to a practical Web environment. INDEX TERMS Convolutional neural network, machine learning, malicious website detection.

show abstract

“…A few more past lexicon-based works are presented in Table 3 below. [47] Sentence level Weight scheme Tweets 70% [48] Phrase level OpenDover (web service) Tweets 75% [49] Sentence level Semantic-based lexicon analysis Tweets - A few more past lexicon-based works are presented in Table 3 below. Lexicons are built for applications, such as online product reviews, blogs, Twitter, medical forums, etc., and various works related to them are presented.…”

Section: Lexicon-based Sentiment Analysis: a Reviewmentioning

confidence: 99%

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

et al. 2019

View full text Add to dashboard Cite

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

show abstract

Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network

Cited by 36 publications

References 9 publications

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

Contact Info

Product

Resources

About