## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Security and Privacy
#### 4.5.5 Anomaly or Intrusion Diagnosis and Suppression  
Systems for detecting anomalies, attacks, and intrusions scan traffic on the network for indications of harmful behavior and take the necessary steps to avoid it. Firewalls use the technique of restricting data flow according to a preset set of regulations, which is distinct in that these systems seek indications of unusual activity or malicious attempts. Additionally, these systems either use network anomalies, behavior, or previously known patterns to uncover assaults. Behavior-driven threat detection searches for trends in operations that depart from typical conduct, whereas signature-driven detection compares data packet flows to a repository of known threat profiles. On the other hand, anomaly detection employs statistical approaches to find strange or unanticipated traffic patterns [342]. Intrusion detection--In a distributed KDN where spectral partitioning is used for dividing the network, Support Vector Machine (SVM)-based machine learning is utilized for intrusion detection, where the Attacker List (AL) is distributed among partitioned networks, ensuring integrity using blockchains [343]. Similarly, another research work suggests using blockchain for secure data sharing for intrusion detection in networks using Deep Learning (DL) since attackers cannot modify a block in a blockchain without affecting all other blocks [344]. Likewise, the PRO-DLBIDCPS attack detection platform makes use of gated recurrent neural networks for intrusion detection. Its performance is improved by optimization, and the suggested platform's security is improved through the usage of blockchain in the digital-physical network context [345]. Furthermore, in order to provide privacy for the intrusion detection system known as the Deep Blockchain Framework (DBF), privacy-based blockchain and smart contracts are utilized, while deep neural networks based on LSTM are utilized for distributed intrusion detection [346]. Some have suggested using blockchain to store both data and machine learning models, protecting the integrity to be used in Collaborative Intrusion Detection (CID) in Unmanned Aerial Vehicular Networks where decisions are driven by knowledge [347]. Moreover, blockchain is used to establish trust and integrity, while an intrusion detection system employs the K Nearest Neighbor (KNN) machine learning algorithm to assess the likelihood of harmful activities in the network's infrastructure [348]. Additionally, a framework called DeepCoin combines a deep machine learning-inspired method to detect breaches that uses sequential neural networks to identify assaults in the blockchain-enabled power system with a blockchain-driven approach that uses brief signatures and hashing algorithms to defend against intelligent grid hacking attempts [349]. Alternatively, a Fused Realtime Sequential Deep Extreme Learning (FFSDEL) system is employed in home automation networks built on blockchain to identify breaches in knowledge-driven smart home architecture [350]. The Federated Deep Learning-based Intrusion Detection System (FED-IDS) was recently used in intelligent transportation networks for dispersed surveillance in automobile nodes at the edge, where the blockchain enables reliable training and avoids the storage of untrustworthy changes in the blockchain [351]. Similarly, a Collaborative Intrusion Detection System (CIDS) for intelligent vehicular networks has been proposed by performing federated learning using vehicles and road side units while utilizing blockchain to securely share and distribute the trained models in order for cooperative trespassing detection to provide safe interaction between each intrusion identification node [352]. In CIDS, application guidelines have been specified for employing blockchain to build trust-based conversations among identifying nodes, and the controller sends the most recent modifications to CIDS, which uses blockchain for safely distributing the signatures to the snort nodes.