| Title |
An Edge Based Attack Detection Model (EBAD) for Increasing the Trustworthiness in IoT Enabled Smart City Environment |
| ID_Doc |
38885 |
| Authors |
Minu, RI; Nagarajan, G; Munshi, A; Venkatachalam, K; Almukadi, W; Abouhawwash, M |
| Title |
An Edge Based Attack Detection Model (EBAD) for Increasing the Trustworthiness in IoT Enabled Smart City Environment |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/ACCESS.2022.3200703 |
| Abstract |
Several massive real-time services could be offered to the residents of smart cities by the incorporation of collaborative applications. All such applications require latency-aware network services for accomplishing various needs of the smart city environment. It requires technological enhancements to the existing mechanisms to serve better in smart environments. Such enhancements to the prevailing approaches also opened a wide range of chances to the intruders. Among such infringes, the identity-based attack is the most powerful attack, which may directly affect the credibility of legitimate network components. Such attackers aim to steal the identity of other legitimate entities. Thus, the prevailing trust-based approaches cannot withstand such attacks. The proposed Edge-based approach, EBAD has been designed for smart city environments, as a robust prevention mechanism for identity theft and misuse. EBAD is efficient enough to identify the Sybil attacker nodes and the early identification of such attacker nodes will nullify the probability of performing the Sybil attack over a Cooperative blackmailing attack (SA-CBA). EBAD uses an Edge-based accusation analysis approach to assess the malicious behavior of the network entities. The major part of the required computations has been placed at the edge node for reducing the computational overload of the end devices. Finally, the efficiency of EBAD has been examined under a malicious environment. |
| Author Keywords |
Behavioral sciences; Smart cities; Cloud computing; Security; Internet of Things; Real-time systems; Image edge detection; Edge computing; Edge computing; IoT; MEC; smart cities; Sybil attack |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000848177700001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09864193.pdf
|