Smart City Gnosys
Smart city article details
| Title | Analysis Of Intrusion Detection Using Machine Learning Techniques |
|---|---|
| ID_Doc | 9197 |
| Authors | Janani Pandeeswari G.; Jeyanthi S. |
| Year | 2022 |
| Published | 2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022 |
| DOI | http://dx.doi.org/10.1109/ICATIECE56365.2022.10047057 |
| Abstract | The Internet of Things relates to many sensors and devices connected via the Internet. With IoT, data is easily accessible and can be exchanged with each other. Technological developments raise security problems. As these IoT devices are inter linked to sensors that are coupled to large cloud servers, smart city network traffic through IoT systems is increasing rapidly, creating new cyber security challenges. The increasing network traffic reflects in the task of identifying IoT attacks and detect malicious data at an early stage. The proliferation of IoT systems comes with the hazard of network attacks such as denial of service (DoS) and spoofing. Lot of experimenter have worked on IDS development using machine learning approaches to rectify the problems outlined above. With the accuracy of computer drills, the non-uniform data can be spotted automatically. Even unknown threats can be detected by machine learning systems due to their generalizability. Although several studies have addressed the application of machine learning (ML) solutions to detect attacks in recent years, little attention has been paid to detecting attacks on IoT networks. This paper contributes in this area by analysing varying machine learning methods to discover network attacks quick and perfectly. In this study, ADFA dataset is used for intrusion detection, intelligent anomaly detection based on the Random Forest machine learning algorithm, to address IoT cyber security and compare it with the literature study, and the new features have proven useful. © 2022 IEEE. |
| Author Keywords | ADFA; deep learning; Intrusion detection system; machine learning |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
9646  | 0.922 | Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T. | Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine Learning | CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020) |
| 6153 | 0.92 | Alrashdi I.; Alqazzaz A.; Aloufi E.; Alharthi R.; Zohdy M.; Ming H. | Ad-Iot: Anomaly Detection Of Iot Cyberattacks In Smart City Using Machine Learning | 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 (2019) |
| 28563 | 0.919 | Ansari L. | Guarding The Future: Anomaly Detection In Iot-Enabled Smart Cities | Lecture Notes in Networks and Systems, 1110 LNNS (2024) |
| 47766 | 0.917 | Plazas Olaya M.K.; Vergara Tejada J.A.; Aedo Cobo J.E. | Securing Microservices-Based Iot Networks: Real-Time Anomaly Detection Using Machine Learning | Journal of Computer Networks and Communications, 2024 (2024) |
| 35873 | 0.914 | Kumar V.S.; Sunehra D. | Machine Learning Algorithms For Binary And Multiclass Classification Of Iot Network Traffic In Smart Cities | 2024 3rd International Conference on Artificial Intelligence for Internet of Things, AIIoT 2024 (2024) |
| 36913 | 0.909 | Girubagari N.; Ravi T.N. | Methods Of Anomaly Detection For The Prevention And Detection Of Cyber Attacks | International Journal of Intelligent Engineering Informatics, 11, 4 (2024) |
| 23835 | 0.907 | Arabiat A.; Altayeb M. | Enhancing Internet Of Things Security: Evaluating Machine Learning Classifiers For Attack Prediction | International Journal of Electrical and Computer Engineering, 14, 5 (2024) |
| 18314 | 0.904 | Abdulla H.; Al-Raweshidy H.S.; Awad W. | Denial Of Service Detection For Iot Networks Using Machine Learning | International Conference on Agents and Artificial Intelligence, 3 (2023) |
| 19430 | 0.903 | Alhanaya M.; Al-Shqeerat K. | Developing An Integrated Framework For Securing Internet Of Things Traffic In Smart Cities Using Machine Learning Techniques | Applied Sciences (Switzerland), 13, 16 (2023) |
| 41565 | 0.903 | Zhukabayeva T.; Ahmad Z.; Adamova A.; Karabayev N.; Mardenov Y.; Satybaldina D. | Penetration Testing And Machine Learning-Driven Cybersecurity Framework For Iot And Smart City Wireless Networks | IEEE Access, 13 (2025) |