Anomaly Detection Using Deep Neural Network for IoT Architecture

Abstract

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

“…By taking few assumptions like identity can be shared publicly, communication channels are always secure, and some of the credentials can be sent without encryption, the timestamp cannot be modified, single hashing can resolve the integrity problem and cryptosystem is free of quantum brute force and password guessing attacks [12]- [15]. However, several researchers believed that if the above-mentioned assumptions are not carefully addressed, they can lead towards common MAC layer attacks [6], [16]- [18] For instance, an identity reveals attack can lead to theft of identity, which leads towards impersonation attack or man in the middle attack.…”

Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection

“…A comparison of various deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs) such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) network is instead proposed by Ahmad et al [12] and used to find zero-day anomalies within an IoT network with a False Acceptance Rate (FAR) ranging from 0.23% to 7.98%.…”

IoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques

“…Other aspects of IoT security can utilize deep learning, including those we already mentioned in this section. This includes intrusion detection [126][127][128][129] and other types of anomaly detection [130]. A full coverage of these areas would require a separate article.…”