Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm
The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is impo...
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MDPI AG
2025-01-01
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| author | Bambang Susilo Abdul Muis Riri Fitri Sari |
| author_facet | Bambang Susilo Abdul Muis Riri Fitri Sari |
| author_sort | Bambang Susilo |
| collection | DOAJ |
| description | The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture’s left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats. |
| format | Article |
| id | doaj-art-153a268b9d5249fe898abdb06b02db9f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-153a268b9d5249fe898abdb06b02db9f2025-01-24T13:49:25ZengMDPI AGSensors1424-82202025-01-0125258010.3390/s25020580Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning AlgorithmBambang Susilo0Abdul Muis1Riri Fitri Sari2Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaThe Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture’s left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.https://www.mdpi.com/1424-8220/25/2/580deep learningintrusion detection systemInternet of Things (IoT)autoencoderlong short-term memory (LSTM)convolutional neural network (CNN) |
| spellingShingle | Bambang Susilo Abdul Muis Riri Fitri Sari Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm Sensors deep learning intrusion detection system Internet of Things (IoT) autoencoder long short-term memory (LSTM) convolutional neural network (CNN) |
| title | Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm |
| title_full | Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm |
| title_fullStr | Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm |
| title_full_unstemmed | Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm |
| title_short | Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm |
| title_sort | intelligent intrusion detection system against various attacks based on a hybrid deep learning algorithm |
| topic | deep learning intrusion detection system Internet of Things (IoT) autoencoder long short-term memory (LSTM) convolutional neural network (CNN) |
| url | https://www.mdpi.com/1424-8220/25/2/580 |
| work_keys_str_mv | AT bambangsusilo intelligentintrusiondetectionsystemagainstvariousattacksbasedonahybriddeeplearningalgorithm AT abdulmuis intelligentintrusiondetectionsystemagainstvariousattacksbasedonahybriddeeplearningalgorithm AT ririfitrisari intelligentintrusiondetectionsystemagainstvariousattacksbasedonahybriddeeplearningalgorithm |