The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. Cyber security has become an increasingly difficult issue to manage as various IoT devices and services grow. Malicious traffic identification using deep learning techniques has emerged as a key component of network intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A Recurrent Neural Network (RNN) is useful in a wide range of applications. First, this paper proposes a novel deep learning model for anomaly detection in IoT networks using a recurrent neural network. Long Short Term Memory (LSTM), BiLSTM, and Gated Recurrent Unit (GRU) techniques are used to implement the proposed model for anomaly detection in IoT networks. A Convolutional Neural Network (CNN) can analyze input features without losing important information, making them particularly well suited for feature learning. Next, a hybrid deep learning model was proposed using convolutional and recurrent neural networks. Finally, a lightweight deep learning model for binary classification was proposed using LSTM, BiLSTM, and GRU based approaches. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, IoT-23, MQTT, MQTTset, and IoT-DS2 datasets. Compared to current deep learning implementations, the proposed multiclass and binary classification model achieved high accuracy, precision, recall, and F1 score.

While anomaly detection and the related concept of intrusion detection are widely studied, detecting anomalies in new operating behavior in environments such as the Internet of Things (IoT) is an active field of research. Anomaly detection models trained on datasets that are likely imbalanced have poor results, but the ability of Generative Adversarial Networks (GANs) to emulate complex high-dimensional distributions seen in real-world data suggests that they can be effective for anomaly detection. This paper proposes a novel framework for detecting anomalies in IoT networks utilizing conditional GANs (cGANs) to build realistic distributions for a given feature set to overcome the issue of data imbalance. To this end, a one class cGAN (ocGAN) model was utilized to learn the minority data class to balance the dataset. Then, the binary class cGAN (bcGAN) model generates augmented data for the binary balance dataset. The performance of the ocGAN and bcGAN models in binary and multiclass classification environments were evaluated using a Feed Forward Neural Network (FFN) and tested on two network-based anomaly detection datasets and five IoT network-based anomaly detection datasets. The proposed models outperformed other anomaly detection models in the standard metrics of accuracy, precision, recall, and F1 score.

The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry.

The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score.