IoT botnet attack detection using deep autoencoder and artificial neural networks

作     者：Stiawan, Deris Susanto, Abdi Bimantara, Abdi Idris, Mohd Yazid Budiarto, Rahmat 

作者机构：Univ Sriwijaya Fac Comp Sci Dept Comp Engn Palembang 30139 Indonesia Univ Bina Insan Fac Engn Sci Lubuklinggau 31626 Indonesia Univ Teknol Fac Engn Sch Comp Johor Baharu 81310 Malaysia Albaha Univ Coll Comp Sci & IT Alaqiq 657797738 Saudi Arabia 

出 版 物：《KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS》 (KSII Trans. Internet Inf. Syst.)

年 卷 期：2023年第17卷第5期

页      面：1310-1338页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Botnet Detection Internet of Things Autoencoder Artificial Neural Network 

摘      要：As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3 -layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.