| [9] | “Gives an overview of various attacks” | Replay attacks, DOS, Routing attacks, fake information attacks. | Privacy problem |
| [10] | “Overview of security issues in vehicular ad-hoc networks” | Eavesdropping, Tracking of Location, ID revealing, attribute-based DOS attack | Not addressed privacy problems |
| [11] | “Enhancing Security and Privacy for Identity-based Batch Verification Scheme in VANET”. | Fake attack | Gives a solution for Fake attacks only. |
| [15] | “Security Analysis of Vehicular Ad-Hoc Network (VANETs)” | Replay attack, DoS attack Fabrication attack, alteration attack, replay attack, message | No solution for other attacks |
| [16] | “Security challenges, Issues and their solutions on VANETs” |
| No solution for confidentiality. |
| [51] | “signature-based authentication” in VANETs” | DOS | Not so effective |
| [19] | “Detection of malicious vehicles (DMV) through monitoring in vehicular ad-hoc networks” | Node Impersonate | No solution for other attacks |
| [20] | “Outlier detection in ad hoc networks using dempster-shafer theory” | Node Impersonate | No solution for other attacks |
| [12] | “Detection and localization of Sybil nodes in VANETs” | Sybil Attack | No solution for other attacks |
| [13] | “P2DAP Sybil attacks detection in vehicular ad hoc networks” | Sybil Attack, Sending false info, ID Disclosure | No solution for other attacks |
| [14] | “Privacy-preserving detection of Sybil attacks in vehicular ad hoc networks” | Sybil Attack, Sending false info, ID Disclosure | No solution for other attacks |
| [18] | “Anovelsecure communication scheme in vehicular ad hoc networks” | Sending false info, ID Disclosure | No solution for other attacks |
| [21] | “A group signature based secure and privacy-preserving vehicular communication framework” | Sending false info, ID Disclosure | No solution for other attacks |
| [6] | “Defence against Sybil attack in vehicular ad-hoc network - based on roadside units support” | Sybil Attack | No solution for other attacks |
| [22] | “Privacy Technique” | Node Impersonate, Sending false info | No solution for other attacks |
| [23] | “Distributed misbehavior detection in VANETs” | Sending false info | No solution for other attacks |
| [25] | DoS Detection algorithm | It records the information of the vehicle for any unusual behavior | Time-Consuming |
| [26] | DoS detection algorithm Using Malicious and Irrelevant Packet Detection Algorithm | Calculate the time of packet generation and detect malicious nodes | Computing the vehicle position in RSU takes maximum time |
| [27] | Mitigating the effect of DoS Attack in VANETs using Multiple Malicious Nodes detection technique | To detect the multiple malicious node in the network by entropy and bandwidth | Less detection rate of malicious node. |
| [28] | Greedy Behavior Attack Detection Algorithm | It analyses network traffic by greedy nodes behaviour | The false positive rate is maximum in case of increased greedy nodes |
| [29] | Dempster Shafer Theory based Denial of Services Attack detection | Prepare trace file for self-organized map (based in machine learning) | Detection of misbehavior rate is low |
| [30] | Extenuation of DoS attack in VANETs | It detects fraud nodes by matching signature based authentication scheme | Method will not work in case if False information sent by attacker |
| [31] | Denial of Service (DoS) attacks detection in VANET | It uses Bloom filter to detect malevolent vehicle | This method is not suitable for high traffic environment. |
| [32] | Port hopping based Dynamic Defence Strategy for VANET | It manages vulnerable service port number for V2V and V2I communication | It fails to handle the issue of hopping frequency |
| [33] | Detection and Extenuation of DDoS Attack in VANET | This technique isolates fraud node from other victim nodes | High bandwidth consumption and difficult to manage the control packet |
| [34] | Multivariant approach to mitigate DDoS attacks | Using payload tracing and frequency assessment track and detect the malicious nodes | It reduces the packet latency but not guaranteed |
| [35] | Machine Learning Techniques to Detect DDoS Attacks on VANET System: A Survey | Using SVM it analyses the misbehavior node | The false positive rate is maximum |
