CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

被引:0
|
作者
Ortiz-Garcés I. [1 ]
Govea J. [1 ]
Sánchez-Viteri S. [2 ]
Villegas-Ch. W. [1 ]
机构
[1] Escuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Pichincha, Quito
[2] Departamento de Sistemas, Universidad Internacional del Ecuador, Universidad Internacional del Ecuador, Pichincha, Quito
关键词
Artificial intelligence in cybersecurity; Cybersecurity awareness and education; Network anomaly detection;
D O I
10.7717/PEERJ-CS.2041
中图分类号
学科分类号
摘要
Cybersecurity has become a central concern in the contemporary digital era due to the exponential increase in cyber threats. These threats, ranging from simple malware to advanced persistent attacks, put individuals and organizations at risk. This study explores the potential of artificial intelligence to detect anomalies in network traffic in a university environment. The effectiveness of automatic detection of unconventional activities was evaluated through extensive simulations and advanced artificial intelligence models. In addition, the importance of cybersecurity awareness and education is highlighted, introducing CyberEduPlatform, a tool designed to improve users’ cyber awareness. The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats. This research highlights the need for an integrated approach to cybersecurity, combining advanced technological solutions with robust educational strategies. © 2024 Ortiz-Garcés et al.
引用
收藏
相关论文
共 50 条
  • [1] CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Arti fi cial Intelligence
    Ortiz-Garces, Ivan
    Govea, Jaime
    Sanchez-Viteri, Santiago
    Villegas-Ch, William
    PEERJ COMPUTER SCIENCE, 2024, 10
  • [2] Bridging Artificial Intelligence and Railway Cybersecurity: A Comprehensive Anomaly Detection Review
    Qi, Jingwen
    Wang, Jian
    TRANSPORTATION RESEARCH RECORD, 2024,
  • [3] Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity
    Zeadally, Sherali
    Adi, Erwin
    Baig, Zubair
    Khan, Imran A.
    IEEE ACCESS, 2020, 8 : 23817 - 23837
  • [4] CyberAIBot: Artificial Intelligence in an intrusion detection system for CyberSecurity in the IoT
    Serrano, Will
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 166
  • [5] Artificial intelligence advances in anomaly detection for telecom networks
    Edozie, Enerst
    Shuaibu, Aliyu Nuhu
    Sadiq, Bashir Olaniyi
    John, Ukagwu Kelechi
    ARTIFICIAL INTELLIGENCE REVIEW, 2025, 58 (04)
  • [6] Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection
    Chengula, Tumlumbe Juliana
    Mwakalonge, Judith
    Comert, Gurcan
    Sulle, Methusela
    Siuhi, Saidi
    Osei, Eric
    MACHINE LEARNING WITH APPLICATIONS, 2024, 17
  • [7] Anomaly detection in cloud environment using artificial intelligence techniques
    Girish, L.
    Rao, Sridhar K. N.
    COMPUTING, 2023, 105 (03) : 675 - 688
  • [8] Anomaly Detection and Root Cause Analysis Enabled by Artificial Intelligence
    Yuan, Yannan
    Yang, Jiaolong
    Duan, Ran
    I, Chih-Lin
    Huang, Jinri
    2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2020,
  • [9] Anomaly detection in cloud environment using artificial intelligence techniques
    L. Girish
    Sridhar K. N. Rao
    Computing, 2023, 105 : 675 - 688
  • [10] Anomaly detection and trust authority in artificial intelligence and cloud computing
    Qureshi, Kashif Naseer
    Jeon, Gwanggil
    Piccialli, Francesco
    COMPUTER NETWORKS, 2021, 184