A Usability Study on the creation of Intrusion Detection Rules on IoT Networks

被引:0
|
作者
Mauro Junior, Davino [1 ]
Gama, Kiev [1 ]
机构
[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil
关键词
IoT; Usability; Security; Network Intrusion Detection Systems; INTERNET;
D O I
10.1145/3643794.3648281
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Network Intrusion Detection Systems (IDS) can be used to employ defenses on IoT environments by making use of rules to detect anomalies on network traffic. Usability must be treated as a key feature of these systems, especially on the process of creating the aforementioned rules. In this work, we present IoT-Flows, a platform built on traditional IDS's concepts such as network monitoring and generation of alerts once an anomaly is detected, but focusing on enabling users to create rules in an intuitive way with a user-interface (UI). We compared the usability of our platform with Suricata, a popular open-source IDS. In our experimental design, participants were assigned the task of creating a rule to detect a popular distributed denial-of-service attack (DDoS) attack on both systems. Then, we applied a System Usability Scale questionnaire combined with open-ended questions. The feedback showed that Suricata lacks flexibility and a user-friendly UI, especially for nonexperienced users, despite its good documentation. In contrast, IoTFlows was praised for its UI and flexibility but was slower in rule creation compared to Suricata. We found that usability needs to be considered when developing security systems, especially when targeting IoT contexts, where non-IT users are common.
引用
收藏
页码:73 / 80
页数:8
相关论文
共 50 条
  • [31] An Intrusion Detection System Against DDoS Attacks in IoT Networks
    Roopak, Monika
    Tian, Gui Yun
    Chambers, Jonathon
    2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2020, : 562 - 567
  • [32] Evolving Intrusion Detection Rules on Mobile Ad Hoc Networks
    Sen, Sevil
    Clark, John A.
    PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE, 2008, 5351 : 1053 - 1058
  • [33] Dynamic hierarchical intrusion detection task offloading in IoT edge networks
    Sahi, Mansi
    Auluck, Nitin
    Azim, Akramul
    Maruf, Md Al
    SOFTWARE-PRACTICE & EXPERIENCE, 2024, 54 (11): : 2249 - 2271
  • [34] Lightweight Intrusion Detection for IoT Systems Using Artificial Neural Networks
    Saleh, Radhwan A. A.
    Al-Awami, Louai
    Ghaleb, Mustafa
    Abudaqa, Anas A.
    SECURITY AND PRIVACY IN COMMUNICATION NETWORKS, PT II, SECURECOMM 2023, 2025, 568 : 45 - 59
  • [35] Intrusion Detection of IoT Traffic Payload Based on Parallel Neural Networks
    Zhang, Yuhang
    PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024, 2024, : 16 - 20
  • [36] Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
    Cui, Bo
    Chai, Yachao
    Yang, Zhen
    Li, Keqin
    FUTURE INTERNET, 2024, 16 (07)
  • [37] Experimentation with Local Intrusion Detection in IoT Networks Using Supervised Learning
    Ioannou, Christiana
    Vassiliou, Vasos
    16TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2020), 2020, : 423 - 428
  • [38] NID-TGN: Spatiotemporal Intrusion Detection System for IoT Networks
    Sai, Jonna Likith
    Majumder, Souptik
    Verma, Rohit
    Bagade, Priyanka
    SECURITY, PRIVACY, AND APPLIED CRYPTOGRAPHY ENGINEERING, SPACE 2024, 2025, 15351 : 175 - 195
  • [39] A Novel Federated Learning Based Intrusion Detection System for IoT Networks
    Benameur, Rabaie
    Dahane, Amine
    Souihi, Sami
    Mellouk, Abdelhamid
    ICC 2024 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2024, : 2402 - 2407
  • [40] Secure and Efficient Federated Learning for Robust Intrusion Detection in IoT Networks
    Abou El Houda, Zakaria
    Moudoud, Hajar
    Khoukhi, Lyes
    IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM, 2023, : 2668 - 2673