A Multi-Modal Neuro-Physiological Study of Phishing Detection and Malware Warnings

被引:28
|
作者
Neupane, Ajaya [1 ]
Rahman, Md. Lutfor [2 ,4 ]
Saxena, Nitesh [1 ]
Hirshfield, Leanne [3 ]
机构
[1] Univ Alabama Birmingham, Dept Comp & Informat Sci, Birmingham, AL 35294 USA
[2] Aegis Foundry LLC, Birmingham, AL USA
[3] Syracuse Univ, Newhouse Sch Publ Commun, Syracuse, NY 13244 USA
[4] UAB, Birmingham, AL USA
关键词
Phishing Detection; Malware Warnings; EEG; Eye Tracking; Neuroscience; EEG;
D O I
10.1145/2810103.2813660
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Detecting phishing attacks (identifying fake vs. real websites) and heeding security warnings represent classical user-centered security tasks subjected to a series of prior investigations. However, our understanding of user behavior underlying these tasks is still not fully mature, motivating further work concentrating at the neurophysiological level governing the human processing of such tasks. We pursue a comprehensive three-dimensional study of phishing detection and malware warnings, focusing not only on what users' task performance is but also on how users process these tasks based on: (1) neural activity captured using Electroencephalogram (EEG) cognitive metrics, and (2) eye gaze patterns captured using an eye tracker. Our primary novelty lies in employing multi-modal neurophysiological measures in a single study and providing a near realistic set-up (in contrast to a recent neuro-study conducted inside an fMRI scanner). Our work serves to advance, extend and support prior knowledge in several significant ways. Specifically, in the context of phishing detection, we show that users do not spend enough time analyzing key phishing indicators and often fail at detecting these attacks, although they may be mentally engaged in the task and subconsciously processing real sites differently from fake sites. In the malware warning tasks, in contrast, we show that users are frequently reading, possibly comprehending, and eventually heeding the message embedded in the warning. Our study provides an initial foundation for building future mechanisms based on the studied real-time neural and eye gaze features, that can automatically infer a user's "alertness" state, and determine whether or not the user's response should be relied upon.
引用
收藏
页码:479 / 491
页数:13
相关论文
共 50 条
  • [1] A Multi-Modal Neuro-Physiological Study of Malicious Insider Threats
    Hashem, Yassir
    Takabi, Hassan
    Dantu, Ram
    Nielsen, Rodney
    [J]. PROCEEDINGS OF THE 2017 INTERNATIONAL WORKSHOP ON MANAGING INSIDER SECURITY THREATS (MIST'17), 2017, : 33 - 44
  • [2] NEURO-PHYSIOLOGICAL AND BIOCHEMICAL STUDY
    JOUVET, M
    PUJOL, JF
    [J]. REVUE NEUROLOGIQUE, 1972, 127 (01) : 115 - 138
  • [3] MULTIPHISH: MULTI-MODAL FEATURES FUSION NETWORKS FOR PHISHING DETECTION
    Zhang, Lei
    Zhang, Peng
    Liu, Luchen
    Tan, Jianlong
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 3520 - 3524
  • [4] Neural Markers of Cybersecurity: An fMRI Study of Phishing and Malware Warnings
    Neupane, Ajaya
    Saxena, Nitesh
    Maximo, Jose Omar
    Kana, Rajesh
    [J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2016, 11 (09) : 1970 - 1983
  • [5] Multi-Modal Comparative Analysis on Execution of Phishing Detection Using Artificial Intelligence
    Jennifer Dsouza, Divya
    Rodrigues, Anisha P.
    Fernandes, Roshan
    [J]. IEEE Access, 2024, 12 : 163016 - 163041
  • [6] Integration of Multi-modal Features for Android Malware Detection Using Linear SVM
    Ban, Tao
    Takahashi, Takeshi
    Guo, Shanqing
    Inoue, Daisuke
    Nakao, Koji
    [J]. 2016 11TH ASIA JOINT CONFERENCE ON INFORMATION SECURITY (ASIAJCIS), 2016, : 141 - 146
  • [7] Analysis of Malware Communities Using Multi-Modal Features
    Cruickshank, Iain J.
    Carley, Kathleen M.
    [J]. IEEE ACCESS, 2020, 8 : 77435 - 77448
  • [8] Neuro-physiological study in persons with cervico-brachial disorders
    Hirata, M
    Taoda, K
    Kitahara, T
    Tsujimura, H
    Nishiyama, K
    [J]. INDUSTRIAL HEALTH, 2005, 43 (04) : 630 - 635
  • [9] Multi-modal Machine Learning Model for Interpretable Malware Classification
    Lisa, Fahmida Tasnim
    Islam, Sheikh Rabiul
    Kumar, Neha Mohan
    [J]. EXPLAINABLE ARTIFICIAL INTELLIGENCE, PT III, XAI 2024, 2024, 2155 : 334 - 349
  • [10] Android malware defense through a hybrid multi-modal approach
    Asmitha, K.A.
    Vinod, P.
    K.A., Rafidha Rehiman
    Raveendran, Neeraj
    Conti, Mauro
    [J]. Journal of Network and Computer Applications, 2025, 233