Abstract
Day after day, new types of malware are appearing, renewing, and continuously developing, which makes it difficult to identify and stop them. Some attackers exploit artificial intelligence (AI) to create renewable malware with different signatures that are difficult to detect. Therefore, the performance of the traditional malware detection systems (MDS) and protection mechanisms were weakened so the malware can easily penetrate them. This poses a great risk to security in the internet of things (IoT) environment, which is interconnected and has big and continuous data. Penetrating any of the things in the IoT environment leads to a penetration of the entire IoT network and control different devices on it. Also, the penetration of the IoT environment leads to a violation of users’ privacy, and this may result in many risks, such as obtaining and stealing the user’s credit card information or theft of identity. Therefore, it is necessary to propose a robust framework for a MDS based on DL that has a high ability to detect renewable malware and propose malware Anomaly detection systems (MADS) work as a human mind to solve the problem of security in IoT environments. RoMADS model achieves high results: 99.038% for Accuracy, 99.997% for Detection rate. The experiment results overcome eighteen models of the previous research works related to this field, which proved the effectiveness of RoMADS framework for detecting malware in IoT.
Highlights
Accepted: 1 November 2021Many countries and governments around the world are currently seeking to switch from the traditional environments to internet of things (IoT) environments in various fields, whether in industry, healthcare, oil and gas, or smart cities, in order to take advantage of the services and facilities provided by the IoT
We present the simulation assumptions and scenario, and explains the procedures to evaluate the effectiveness of RoMADS model, and evaluates the ability of the RoMADS model to predict the malware based on the accuracy, recall & detection rate (%), F1-score, precision, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE)
RoMADS framework is considered a solution to improve the security in IoT environments
Summary
Accepted: 1 November 2021Many countries and governments around the world are currently seeking to switch from the traditional environments to IoT environments in various fields, whether in industry, healthcare, oil and gas, or smart cities, in order to take advantage of the services and facilities provided by the IoT. We proposed, in this paper, a robust framework to detect the malware that targets the IoT system. We named our proposed framework RoMADS, which refers to Robust Malware Anomaly Detection Systems (MADS). The main principal function of the autoencoder algorithm depends on encoding and decoding It takes a large amount of data, compresses it, and extracts the main features that have a major impact on the data. After that, it uses the compressed data in training neural networks. It compares the original data with the reconstructed data to get the prediction error This algorithm improves the accuracy by a gradual self-training.
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