# Blockchain for Future Wireless Networks: A Decade Survey
## 3 The Proposed Taxonomy for Security Attacks and Countermeasures
### Passive Attacks
The authors of [102] proposed a SQL injection, brute force attack, and cross-site scripting to avoid MiTM attacks. Further, in [103] the authors analyzed the MiTM attack in Wi-Fi networks, where they proposed a secure mechanism to prevent the content addressable memory (CAM) table of the wireless switch from the attackers. Further, [PERSON] et al. [105] discussed how to accomplished MITM attack in HTTPS. They proposed Nebulas blockchain technology-based solution to prevent MITM attacks in internet communication. In [104], [PERSON] et al. proposed a blockchain-enabled lightweight digital signature approach to secure UAV communication. * Network layer attacks--This layer is most vulnerable to various types of network attacks, which are discussed as follows. * Wormhole attacks--In this attack, the attacker creates a forged link between sender and receiver to drop or modify the data packets. The wormhole hole attack impacts confidentiality, availability, and data integrity [139] in applications such as VANETs, IoT networks, and MANETs. The packet leashes approach is used to prevent and defend against the wormhole attack. Moreover, separate route algorithms, watchdog models, and Delphi techniques are feasible solutions to avoid wormhole attacks [140]. In [106], the authors considered wormhole attacks as traffic attacks and presented trust-enabled techniques that detect and prevent such attacks in MANETs. The authors of [107] analyzed and resolved wormhole attacks in IoT networks by simulating the attacks in an IoT environment using NS2 simulation. In addition, [PERSON] et al. [108] discussed security and privacy issues in payment channel network (PCN). They proposed Neo hashed time-lock commitment (n-HTLC) and a key encryption-based protocol that prevents wormhole attacks in the PCN. * Blackhole attack--In this attack, the router destroys and deletes all the messages that are supposed to forward to the legitimate destination (maybe the next router or intended receiver) [141]. Using this attack, the malicious user isolates the network and drops the incoming packets. To overcome the aforementioned issues, the authors of [142] used a time-based baited approach, and [143] proposed fake route request (RREQ) packets that find blackhole attacks in MANETs. Then, the authors of [109] analyzed various detection methods of blackhole attack [110] and proposed a hidden Markov model approach that prevents the attack in wireless sensor networks. Furthermore, [PERSON] et al. [111] presented a blockchain-based two-level detection system in the VANET routing protocol. This method eliminates blackhole attacks and calculates a trust score that increases VANET performance. * DoS attacks--The attacker often floods the data packets to the targeted access point, which shuts down the network and creates a resource starvation attack (DoS) [144]. In a DoS attack, the attacker sends spoofed, ping, and malformed packets to the target network, which slows down the network performance losing connectivity to the device. Using this attack, malicious users target sensitive data of the government, personnel, and the financial department [145]. Limiting broadcasting, protecting endpoints, and rejecting fake traffic using firewalls and routers prevent DoS attacks [146]. The bandwidth prediction technique [147], a simple and light-weight statistical detection and mitigation approach, can also be used to avoid DoS in WN applications [112; 148]. Moreover, ref. [112] prevented flooding-based DoS attack in MANETs. [PERSON] et al. [113] proposed the use of lightweight agents in various IoT applications such as smart homes, smart grids, smart industries, etc. that detect DoS attacks. They offer blockchain-based smart contracts that resist DoS attacks in IoT botnets and ensure data integrity. As well, [PERSON] et al. [114] presented a security service blockchain (SSB) solution that mitigates DoS attacks in network slice broker. 4.