| Title |
Methods of anomaly detection for the prevention and detection of cyber attacks |
| ID_Doc |
41197 |
| Authors |
Girubagari, N; Ravi, TN |
| Title |
Methods of anomaly detection for the prevention and detection of cyber attacks |
| Year |
2023 |
| Published |
International Journal Of Intelligent Engineering Informatics, 11, 4 |
| DOI |
10.1504/IJIEI.2023.136097 |
| Abstract |
The idea of the 'smart city' has developed in response to the issues brought on by the rapid growth in urbanisation and population. Smart cities are interconnected. IoT and big data analytics enable smart city efforts. IoT devices connected to the always-on IoT network for several functions make unauthorised entry easier. This issue involves the safety system's ability to identify prior unidentified attacks. System behavioural modelling and unattended or semi-supervised machine learning could solve this problem. Machine learning model training datasets affect security system efficacy. Cyber-physical objects' security restrictions make system data inaccessible. These datasets have been constructed several times, but their reliability and completeness are questionable. Cyber attacks affect data privacy and security in connected IoT contexts, including smart infrastructure, communication, e-governance, etc. Cybersecurity requires a machine learning-based intelligent detection system. IoT anomaly detection finds odd behaviour. Many researchers have studied anomaly detection methods to detect and thwart data exchange cyber attacks. Existing technologies detected some cyber attacks, but others required newer, more powerful approaches. This article discusses the pros and cons of different methods and highlights the obstacles and research gaps that prevent anomaly detection approaches from reaching their full potential. |
| Author Keywords |
smart city; big data; internet of things; IoT; cyber attacks; attacks detection; anomaly detection; machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001143936700005 |
| WoS Category |
Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
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