A convolutional neural network combined with attention mechanism and a parallel joint algorithm model (CATTB) of bidirectional independent recurrent neural network are proposed. The algorithm extracts the relocation feature and the “texture fingerprint” feature for expressing the similarity of the URL (Uniform Resource Locator) binary file content of the malicious web page, and uses the word vector tool word2vec to train the URL word vector feature and extract the URL static vocabulary feature. CNN (Convolutional Neural Network) is used to extract deep local features. Secondly, Attention mechanism adjusts weight and BiIndRNN (Bidirectional Independently Recurrent Neural Network) to extract global features. Finally, softmax is used for classification. This paper extracts more comprehensive features from different angles and using different methods. The experimental results show that the test results are higher than other researchers, and compared with other algorithms, the proposed CATTB algorithm improves the accuracy of malicious web page detection.
S.Ren, K.He and R.Girshick, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems, 2015, 91–99.
2.
F.A.Gers, J.A.Schmidhuber and F.A.Cummins, Learning to forget: Continual prediction with LSTM[J], Neural Computation12(10) (2000), 2451–2471.
3.
S.BARON-COHEN, The eye detection detector (EDD) and the shared attention mechanism (SAM): Two cases for evolutionary psychology[J], Joint Attention: Its Origin and Role in Development, 1995, 41–59.
4.
S.Li, W.Li and C.Cook, et al. Independently recurrent neural network (indrnn): Building a longer and deeper rnn[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 5457–5466.
5.
ShaHong-Zhou, ZhouZhou, LiuQing-Yun and QinPeng. Light-weight self-learning for URL classification[J], Journal of Communications35(9) (2014), 32–39.
6.
ShaHong-Zhou, ZhouZhou, LiuQing-Yun and QinPeng, Survey on malicious webpage detection research[J], Chinese Journal of Computers39(3) (2016), 529–542.
7.
LinHai-Lun, LiWei, WangWei-Ping, YueYin-Liang and LinZheng, Efficient segment pattern based method for malicious URL detection[J]. Journal of Communications36(s1) (2015), 141–148.
8.
LiuYanbing, ShaoWei and WangYong, et al. A multiple string matching algorithm for large-scale URL filtering[J], Chinese Journal of Computers, 2014(5), 1159–1169.
9.
ZhouHao, The research and implementation of malicious web pages detection from search engine based on decision tree[D]. Hunan University, 2013.
10.
A.N.Langville and C.D.Meyer, Google's PageRank and Beyond[J]. Mathematical Intelligencer, 30(1) (2011), 68–69.
11.
S.Poomagal and T.Hamsapriya, K-means for search results clustering using URL and tag contents[C]International Conference on Process Automation, Control and Computing. IEEE, 2011, 1–7.
12.
R.K.Gibson, K.Gillan and F.Greffet, et al. Party organizational change and ICTs: The growth of a virtual grassroots?[J], New Media & Society, 15(1)(2013), 31–51.
13.
L.X.Zheng, L.I.Qing-Shan and L.I.Su-Ke, et al. Phishing URL Detection Based on Domain Name Information[J], Computer Engineering38(10) (2012), 108–110.
14.
WangQiu-Shi, Design and implementation of HTTP Trojan horse network monitoring system based on client honeypot technology[D], Beijing University, 2008.
15.
Y.Shiraishi, M.Kamizono and M.Hirotomo, et al. Detection of Malicious PDF Files by Windows API Hook-based Network Monitoring[J], D -- Abstracts of IEICE TRANSACTIONS on Information and Systems (Japanese Edition), 2014.
16.
K.Chen, K.E.Wen-De and A.G.Wang, et al. Research on behavior analysis system based on sandbox technology[J], Computer Technology & Development, 2015.
17.
X.P.Feng, L.I.Zhi-Tang and T.U.Hao, et al. A method of malicious webpages behavior detection based on BHO technology[J]. Journal of Guangxi University, 2011.
18.
M.N.Feroz and S.Mengel, Examination of data, rule generation and detection of phishing URLs using online logistic regression[C]IEEE International Conference on Big Data. IEEE, 2015, 241–250.
19.
S.C.Jeeva and E.B.Rajsingh, Intelligent phishing url detection using association rule mining[J]. Human-centric Computing and Information Sciences6(1) (2016), 1–19.
20.
M.Akiyama, T.Yagi and T.Yada, et al. Analyzing the ecosystem of malicious URL redirection through longitudinal observation from honeypots[J], Computers & Security, 2017.
21.
E.Bhagyashree and K.Tanuja, Phishing URL Detection: A Machine Learning and Web Mining-based Approach[J], International Journal of Computer Applications123(13) (2015), 46–50.
22.
M.Konte, R.Perdisci and N.Feamster, ASwatch:An AS Reputation System to Expose Bulletproof Hosting ASes[J], ACM SIGCOMM Computer Communication Review45(5) (2015), 625–638.
23.
Y.Xue, Y.Li and Y.Yao, et al. Phishing sites detection based on Url Correlation[C]International Conference on Cloud Computing and Intelligence Systems. IEEE, 2016, 244–248.
24.
M.N.Feroz and S.Mengel, Phishing URL Detection Using URL Ranking[C]IEEE International Congress on Big Data. IEEE Computer Society, 2015, 635–638.
25.
J.Ma, L.K.Saul and S.Savage, et al. Learning to detect malicious URLs[J]. Acm Transactions on Intelligent Systems & Technology2(3) (2011), 1–24.
26.
M.Aldwairi and R.Alsalman, MALURLS: A lightweight malicious website classification based on URL features[J], Journal of Emerging Technologies in Web Intelligence, 4,2(2012-05-01) 4(2) (2012), 128–133.
27.
R.Shukla and M.Singh, PythonHoneyMonkey: Detecting malicious web URLs on client side honeypot systems[C]International Conference on Reliability, INFOCOM Technologies and Optimization. IEEE, 2014, 1–5.
28.
M.Akiyama, T.Yagi and T.Yada, et al. Analyzing the ecosystem of malicious URL redirection through longitudinal observation from honeypots[J]. Computers & Security, 2017.
29.
M.Ishida, H.Takakura and Y.Okabe, High-performance intrusion detection using OptiGrid clustering and grid-based labelling[C]Ieee/ipsj, International Symposium on Applications and the Internet. IEEE, 2011, 11–19.
30.
K.Rajitha and D.Vijayalakshmi, Suspicious URLs filtering using optimal RT-PFL: A novel feature selection based web URL detection[M]Smart Computing and Informatics, 2018.
31.
G.E.Hinton, Learning distributed representations of concepts[C]Proceedings of the Eighth Annual Conference of the Cognitive Science Society1 (1986), 12.
32.
TianShengwei, ZhouXingfa and PeiLong, et al. Causal relationship extraction based on bidirectional LSTM in Uighur language[J], Journal of Electronics & Information Technology40(1) (2018), 200–208.
33.
TianShengwei, QinYue, QiLong, TurgenIbrahim and FengChampion, Bi-LSTM-based Uighur Personal Pronouns Referential Decomposition[J], Acta Electronica Sinica46(07) (2018), 1691–1699.
34.
D.Rahmawati and M.L.Khodra, Word2vec semantic representation in multilabel classification for Indonesian news article[C]International Conference on Advanced Informatics: Concepts, Theory and Application. IEEE, 2017, 1–6.
35.
D.Zhang, H.Xu and Z.Su, et al. Chinese comments sentiment classification based on word2vec and SVM perf[J], Expert Systems with Applications42(4) (2015), 1857–1863.
36.
B.Xue, C.Fu and S.Zhan, A study on sentiment computing and classification of Sina Weibo with Word2vec[C]IEEE International Congress on Big Data. IEEE, 2014, 358–363.
37.
G.Liu, B.Qiu and W.Liu, Automatic Detection of Phishing Target from Phishing Webpage[J]57(11) (2017), 4153–4156.
38.
J.C.S.Fatt, C.K.Leng and S.S.Nah, Phishdentity: Leverage Website Favicon to Offset Polymorphic Phishing Website[C]International Conference on Availability. IEEE, 2015, 114–119.
39.
P.Dewan and P.Kumaraguru, Detecting Malicious Content on Facebook[J]. Computer Science, 2015.
40.
A.K.Jain and B.B.Gupta, A novel approach to protect against phishing attacks at client side using auto-updated white-list[J], Eurasip Journal on Information Security2016(1) (2016), 9.
41.
A.K.Jain and B.B.Gupta, PHISH-SAFE: URL Features-Based Phishing Detection System Using Machine Learning[J]. 2018.
