|
De Araujo-Filho et al. (2021)
|
Hindering cyber-attacks |
CAN |
Unsupervised intrusion prevention system |
Improves security |
|
Mwinuka et al. (2022)
|
Network spoofing attack |
WSN |
Android-based client-side |
Fake access is prevented |
|
Huang et al. (2022)
|
forgery attacks |
WSN |
Wireless signals to detect looped videos |
Surveillance systems |
|
Tran Le et al. (2022)
|
Malware attacks |
WSN networks |
Epidemiological mathematical model |
Encryption and authentication in Wi-Fi |
|
Li et al. (2021)
|
impersonation attack |
Wi-Fi |
Meta-WF |
AWID dataset |
|
Sharma, Elmiligi & Gebali (2021)
|
Hello flood attack, increased version attack, decreased rank attack, DIS attack. |
Wireless sensor network. |
SVM, Bayes classifier, random forest. |
Reinforcement learning needs to be used. |
|
Sun, Yuan & Fan (2022)
|
Real world attack connections |
WSN |
High–interaction honeypot (HIH), Low–interaction honeypot (LIH) |
Time related connection properties dynamically need to be used |
|
Kaya & Selcuk (2020)
|
Post connection attacks |
Wireless sensor network |
Automated database scanning method |
Analysis of security is need to be improved |
|
Lounis & Zulkernine (2020)
|
WPA3 connection attacks |
WSN |
WPA3-SAE (Simultaneous Authentication of Equals) |
Improve security |
|
Salmi & Oughdir (2022)
|
DoS attacks |
WSN |
CNN–LSTM method |
Improves network security |
| Proposed |
DELETE attack, CONNECT attack, OPTIONS attack and TRACE attack |
WSN |
Cyclic analysis method (CAM) |
Reduces time complexity and space complexity |
