期刊：
International Journal of Network Security,2017年19(6):1016-1023 ISSN：1816-353X
通讯作者：
Zhang, Jianjun(jianjun998@163.com)
作者机构：
[Jianjun Zhang 0005] College of Computer Science and Electronic Engineering, Hunan University, 36 Lushan Rd, Yuelu Qu, Changsha Shi, Hunan, 410080, China;[Deng Gao] College of Software, Hunan Vocational College of Science and Technology, 36 Lushan Rd, Yuelu Qu, Changsha Shi, Hunan, 410080, China;[HaiJun Lin; Lucai Wang; Jianjun Zhang 0005] College of Engineering and Design, Hunan Normal University, 36 Lushan Rd, Yuelu Qu, Changsha Shi, Hunan, 410080, China;[Huajun Huang] College of Computer and Information Engineering, Central South University of Forestry and Technology, 36 Lushan Rd, Yuelu Qu, Changsha Shi, Hunan, 410080, China
通讯机构：
College of Computer Science and Electronic Engineering, Hunan University, 36 Lushan Rd, Yuelu Qu, Changsha Shi, Hunan, China
关键词：
Steganography;Frequent words hash;Information hiding;Secret information;State of the art;Statistical characteristics;Steganalysis;Text database;Text information;Big data
摘要：
The attackers may discover the existence of the secret information or even get it by analyzing the cover's statistical characteristics, changes of which often occur due to the embedding. In this paper, a novel coverless text information hiding method was proposed. By using the words rank map and the frequent words hash, normal texts containing the secret information could be retrieved from the text database, and will be sent to the receiver without any modification. Because the embedding is not needed, the proposed method could be able to escape from almost all state-of-the-art steganalysis methods.
摘要：
Although anti-phishing solutions were highly publicized, phishing attack has been still an important serious problem. In this paper, a novel phishing webpage detecting algorithm using the webpage noise and n-gram was proposed. Firstly, the phishing webpage detecting algorithm extracts the webpage noise from suspicious websites, and then expresses it as a feature vector by using n-gram. Lastly, the similarity of feature vector between the protected website and suspicious is calculated. Experimental results on detecting phishing sites samples data show that: this algorithm is more effective, accurate and quick than existing algorithms to detect whether a site is a phishing website.
摘要：
<jats:p>This document explains and demonstrates how to prepare your camera-ready manuscript for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. The text area for your manuscript must be 17 cm wide and 25 cm high (6.7 and 9.8 inches, resp.). Do not place any text outside this area. Use good quality, white paper of approximately 21 x 29 cm or 8 x 11 inches (please do not change the document setting from A4 to letter). Your manuscript will be reduced by approximately 20% by the publisher. Please keep this in mind when designing your figures and tables etc.Intrusion detection is a very important research domain in network security. Current intrusion detection systems (IDS) especially NIDS (Network Intrusion Detection System) examine all data features to detect intrusions. Also, many machine learning and data mining methods are utilized to fulfill intrusion detection tasks. This paper proposes an effective intrusion detection model that is computationally efficient and effective based on Random Forest based feature selection approach and Neural Networks (NN) model. We firstly utilize random forest method to select the most important features to eliminate the insignificant and/or useless inputs leads to a simplification of the problem, in order to faster and more accurate detection; Secondly, classic NN model is used to learn and detect intrusions using the selected important features. Experimental results on the well-known KDD 1999 dataset demonstrate the proposed hybrid model is actually effective.</jats:p>
作者机构：
[黄华军; 谭骏珊] School of Computer and Information Engineering, Central and South University of Forestry and Technology, Changsha 410004, China;[孙星明] School of Computer and Communication, Hunan University, Changsha 410082, China
通讯机构：
[Huang, H.-J.] S;School of Computer and Information Engineering, , Changsha 410004, China
