Detecting malicious clients in ISP networks using HTTP connectivity graph and flow information

被引：0
|
作者：
Liu, Lei [1 ]
Saha, Sabyasachi [3 ]
Torres, Ruben [3 ]
Xut, Jianpeng [2 ]
Tant, Pang-Ning [2 ]
Nucci, Antonio [3 ]
Mellia, Marco [4 ]
机构：
[1] HP Labs., 1501 Page Mill Rd, Palo Alto,CA,95050, United States
[2] Dept. of Computer Science, Michigan State University, East Lansing,MI,48824, United States
[3] Narus Inc., 570 Maude Court, Sunnyvale,CA,94085, United States
[4] Politecnico di Torino, Italy
来源：
HP Laboratories Technical Report | 2015年 / 29期
关键词：
HTTP - Web services - Flow graphs;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [21] Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism
    Aggarwal, Priya
    Gupta, Anubha
    MEDICAL IMAGE ANALYSIS, 2019, 56 : 11 - 25
  • [22] Detecting Malicious Botnets in IoT Networks Using Machine Learning Techniques
    Asghar, Muhammad Nabeel
    Raza, Muhammad Asif
    Murad, Zara
    Alyahya, Ahmed
    IPSI BGD TRANSACTIONS ON INTERNET RESEARCH, 2024, 20 (01): : 24 - 35
  • [23] Detecting Malicious Java']JavaScript Using Structure-Based Analysis of Graph Representation
    Rozi, Muhammad Fakhrur
    Ban, Tao
    Ozawa, Seiichi
    Yamada, Akira
    Takahashi, Takeshi
    Kim, Sangwook
    Inoue, Daisuke
    IEEE ACCESS, 2023, 11 : 102727 - 102745
  • [24] Detecting Labor using Graph Theory on Connectivity Matrices of Uterine EMG
    Al-Omar, S.
    Diab, A.
    Nader, N.
    Khalil, M.
    Karlsson, B.
    Marque, C.
    2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2015, : 2195 - 2198
  • [25] Detecting malicious IoT network communication through Graph Neural Networks in real-world conditions
    Carletti, Vincenzo
    Foggia, Pasquale
    Rosa, Francesco
    Vento, Mario
    PATTERN RECOGNITION LETTERS, 2025, 189 : 92 - 98
  • [26] GRAPH INFORMATION THEORETIC MEASURES ON FUNCTIONAL CONNECTIVITY NETWORKS BASED ON GRAPH-TO-SIGNAL TRANSFORM
    Villafane-Delgado, Marisel
    Aviyente, Selin
    2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2016, : 1137 - 1141
  • [27] Providing a Hybrid Approach for Detecting Malicious Traffic on the Computer Networks Using Convolutional Neural Networks
    Pakanzad, Seyed Navid
    Monkaresi, Hamed
    2020 28TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2020, : 1189 - 1194
  • [28] RAGN: Detecting unknown malicious network traffic using a robust adaptive graph neural network
    Akpaku, Ernest
    Chen, Jinfu
    Ahmed, Mukhtar
    Agbenyegah, Francis Kwadzo
    Leslie, William
    COMPUTER NETWORKS, 2025, 262
  • [29] Graph analysis of uterine networks using EHG source connectivity
    Nader, Noujoud
    Zahran, Saeed
    Marque, Catherine
    Hassan, Mahmoud
    Yochum, Maxime
    2017 FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN BIOMEDICAL ENGINEERING (ICABME), 2017, : 37 - 40
  • [30] A Twist for Graph Classification: Optimizing Causal Information Flow in Graph Neural Networks
    Zhao, Zhe
    Wang, Pengkun
    Wen, Haibin
    Zhang, Yudong
    Zhou, Zhengyang
    Wang, Yang
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 15, 2024, : 17042 - 17050
12345