# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 2 Related Work
### Machine Learning for Securing IoT--An Overview
The IDS is deployed using Python, Keras, and TensorFlow, and evaluated using accuracy precision, recall, and F1-score. Similarly, Ref. [35] have leveraged supervised DL and unsupervised clustering techniques to develop an SSL approach to address the attacks present in the NSL-KDD dataset. The proposed method is tested on the IoT-fog testbed and 99.78% accuracy is achieved. The authors of [36] have also leveraged a DL-based SSL approach to address security attacks in IoT. Transfer learning methodology is used to develop the solution by training it on nine IoT datasets. The technique is evaluated using comparative effectiveness of information transfer, analysis of processing time, and performance comparison of the models on labeled and unlabeled datasets using AUC scores. The authors of [37] have implemented semi-supervised and federated learning approaches in an industrial IoT use case. Federated learning is used to locally train the model, and an active learning-based SSL technique is used to globally adjust the model. A 7.1% accuracy increment is observed through the proposed method in ten active learning queries. Numerous reviews, investigative studies, and surveys have been conducted by the researchers in the domain of IoT networks, security challenges, and routing standards used by LLNs such as IoT and WSNs. For instance, in [38], the authors have studied ML and DL methods for securing IoT ecosystems. They have explored the potential role of ML and DL in improving security aspects of these systems and developed a thematic taxonomy in terms of IoT security threats, learning methods to counter them, layer-wise security approaches using ML and DL, and other dimensions of IoT security. Both ML and DL approaches for IoT security are categorized into (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) reinforcement learning. An elaboration of respective techniques and possibilities of their application in IoT systems is also presented. They are discussed in terms of working principles, advantages, disadvantages, and potential for successful application in an IoT environment. Similarly, Ref. [39] have surveyed ML and DL algorithms categorically for IoT application security. Moreover, security problems and threat models are systematically surveyed, and complexity analysis of ML algorithms is performed along with the discussion on the limitations of their application in IoT networks. The authors of [40] have performed a detailed survey on DL and big data technologies for security in IoT. DL-based architectures and frameworks are explained along with performance evaluation metrics. Additionally, layer-wise security attacks and datasets used for experimental analysis are discussed in detail. In [41], a comprehensive review is conducted on ML-based security solutions for power systems. Various aspects of these systems are covered, including Power Quality Disturbances (PQD), Voltage Stability Assessment (VSA), Transient Stability Assessment (TSA), and Supervisory Control and Data Acquisition (SCADA). ML classifiers, IDSs, and other ML-based security approaches are explored, compared, and evaluated in terms of accuracy for the four power system facets mentioned earlier. The authors of [42] have presented a brief review of ML techniques for improvement in the security of smart grids, detection of power quality events, estimation of transformer life loss, making energy dispatch decisions, and operations of the electricity market. ML has been used in IoT and WSNs for security enhancements in recent years. This solution development domain has gained the acclaim of researchers due to its prospective potential for developing robust security models. For instance, in [43], the authors have used ML as part of their proposal of a robust architecture for adversarial attack detection to improve the identification and classification of High Spatial Resolution Remote Sensing (HSRRS) images. They have used adversarial detection models based on SVM with single or fused two-level features to improve detection accuracy.