Malicious URLs detection based on a novel optimization algorithm

被引：0
|
作者：
Bo W. [1 ,2 ]
Fang Z.B. [1 ]
Wei L.X. [1 ]
Cheng Z.F. [2 ]
Hua Z.X. [3 ]
机构：
[1] School of Electrical Engineering and Electronic Information, Xihua University, Chengdu
[2] School of Electronic Information and Automation, Aba Teachers University, Sichuan
[3] School of Information, Hiroshima Institute of Technology, Hiroshima-shi
来源：
IEICE Transactions on Information and Systems | 2021年 / E104.D卷 / 04期
关键词：
Neural network; Optimization; URLs detection;
D O I：
10.1587/TRANSINF.2020EDL8147
中图分类号：
学科分类号：
摘要：
SUMMARY In this paper, the issue of malicious URL detection is investigated. Firstly a P system is proposed. Then the new P system is introduced to design the optimization algorithm of BP neural network to achieve the malicious URL detection with better performance. In the end some examples are included and corresponding experimental results display the advantage and effectiveness of the optimization algorithm proposed. © 2021 Institute of Electronics, Information and Communication, Engineers, IEICE. All rights reserved.
引用
收藏
页码：513 / 516
页数：3
相关论文
共 50 条
  • [31] Limited Dictionary Builder: An Approach to Select Representative Tokens for Malicious URLs Detection
    Sha, Hongzhou
    Zhou, Zhou
    Liu, Qingyun
    Liu, Tingwen
    Zheng, Chao
    2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2015, : 7077 - 7082
  • [32] Sec2vec: Anomaly Detection in HTTP Traffic and Malicious URLs
    Gniewkowski, Mateusz
    Maciejewski, Henryk
    Surmacz, Tomasz
    Walentynowicz, Wiktor
    38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, 2023, : 1154 - 1162
  • [33] Is this URL Safe: Detection of Malicious URLs Using Global Vector for Word Representation
    Bharadwaj, Rohit
    Bhatia, Ashutosh
    Chhibbar, Laxmi Divya
    Tiwari, Kamlesh
    Agrawal, Ankit
    36TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2022), 2022, : 486 - 491
  • [34] Detecting Malicious URLs Based on Machine Learning Algorithms and Word Embeddings
    Crisan, Andrei
    Florea, Gabriel
    Halasz, Lorand
    Lemnaru, Camelia
    Oprisa, Ciprian
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2020), 2020, : 187 - 193
  • [35] The Algorithm of Malicious Code Detection Based on Data Mining
    Yang, Yubo
    Zhao, Yang
    Liu, Xiabi
    GREEN ENERGY AND SUSTAINABLE DEVELOPMENT I, 2017, 1864
  • [36] Malicious webpages analysis and detection algorithm based on BiLSTM
    Wang H.-H.
    Yu L.
    Tian S.-W.
    Luo S.-Q.
    Pei X.-J.
    International Journal of Electronic Business, 2020, 15 (04) : 351 - 367
  • [37] Detecting Malicious URLs Using Lexical Analysis
    Mamun, Mohammad Saiful Islam
    Rathore, Mohammad Ahmad
    Lashkari, Arash Habibi
    Stakhanova, Natalia
    Ghorbani, Ali A.
    NETWORK AND SYSTEM SECURITY, (NSS 2016), 2016, 9955 : 467 - 482
  • [38] A Malicious Webpage Detection Algorithm Based on Image Semantics
    Li, Xiangjun
    Li, Sifan
    Liu, Shengnan
    Liu, Lingfeng
    He, Daojing
    TRAITEMENT DU SIGNAL, 2020, 37 (01) : 113 - 118
  • [39] Unknown malicious detection based on improved Bayes algorithm
    Lai, Ying-Xu
    Yang, Zhen
    Beijing Gongye Daxue Xuebao/Journal of Beijing University of Technology, 2011, 37 (05): : 766 - 772
  • [40] Finding the malicious URLs using Search Engines
    Nagaonkar, Amruta Rajeev
    Kulkarni, Umesh L.
    PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT, 2016, : 3692 - 3694
12345