# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 2 Literature Review
### Machine Learning Enabled Intrusion Detection for IoT Appliances
#### 2.1.3 IoT-Enabled Networks  
For secure communications to transmit data over IoT-enabled networks, various studies have been carried out that define the vulnerabilities in IoT systems. This section explores the contributions of many researchers suggesting different methods for defending the IoT against malicious attacks. [PERSON] et al. [21] suggested an IDS based on the specification for learning automata to prevent DoS attacks against the IoT. In this proposed system, an IoT center product layer is used instead of a specific tool and is helpful in the decision-making process. This system sets an edge for the number of solvents a middleware layer can support. When the number of convulsions exceeds the set limit, an attack is recognized. [PERSON] et al. [12] proposed a signature-based intrusion detection system that consists of monitoring and recognizing the modules. In that study, the module was coordinated with the European Union (EU) FP7 enterprise project's network system, called 'ebbits', to ensure that networks were subject to DoS attacks. A DoS assurance administrator and IDS were included in the proposed network. An IDS was used to catch and inspect the sniffing packets from IDS testing centers and spread them across the networks. The estimation results indicated that the proposed system exhibits true positive and false-positive rates. [PERSON] [13] put forward an Open Source IDS that provided the functionality of matching patterns and detecting attacks in the network. A test node was used to sniff all packet transmissions in the network and the transmission of the data to IDS for additional analysis. The performance of the proposed IDS was tested by penetration testing tools called scope. No simulation-based studies were conducted in favor of IDS performance and its convenience. Additionally, the authors did not refer to any information about the signature database management. [PERSON] et al. [14] came up with a novel IDS to spot DoS attacks. The main idea behind the proposed IDS was to reduce the energy of the nodes by detecting malicious nodes. In this research, it was suggested that various models use cross-section direction-based networks. The security framework proposed expects the nodes to demonstrate the use of their energy at an inspection rate of 0.5 s. The suggested approach continuously monitored the energy usage by the nodes and any node that was found to deviate at any point was set as a malicious node. Finally, such nodes were removed, and the routing table of the connected nodes was also updated. An IDS proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2015) operates as a software-based supervisor between the network and the gateway. This helps to protect the IP addresses in the grey list or the ones that control access to the network. At every 300 s interval, the blacklist is refreshed, and the grey list is refreshed every 40 s. The simulated framework of this IDS is reproduced on the Contiki operating system by [PERSON] et al. [PERSON] et al. (2016). Amount of packet delivery ratio, packets served, true positives, and false positives are not performed at an acceptable rate in this IDS. Another significant limitation of the proposed IDS is that it has a larger retrieval time as compared to the learning time. Similarly, machine learning techniques are recommended in modern-day IDSs to achieve accurate prediction, automation, speed, and scalability. In the same direction, machine learning for intrusion detection in the industrial IoT (IIoT) was applied through federated learning (FL) in ([PERSON] et al., 2017). The federated learning assistant application is primarily designed to train an algorithm that enables multiple edge devices to connect without sharing private data. Distributed denial of service (DDoS) attacks pose a significant threat to IIoT applications. The adversarial nodes overwhelm the network servers, SCADA systems, and other IoT-enabled devices, causing the crash of industrial systems and resulting in operational disruption.