Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 2 Sayı: 1, 1 - 12, 02.08.2024

Öz

Kaynakça

  • Kasongo S.M., Sun Y., Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset, J Big Data, 7, 105, 2020.
  • Yadav M.S., Kalpana R., Data Preprocessing for Intrusion Detection System Using Encoding and Normalization Approaches, 2019 11th International Conference on Advanced Computing (ICoAC), ChennaiIndia, 265-269, 18-20 Aralık, 2019.
  • Liu H., Zhou M., Liu Q., An embedded feature selection method for imbalanced data classification, in IEEE/CAA Journal of Automatica Sinica, 6 (3), 703-715, 2019.
  • Alabadi, M., Habbal, A., Wei, X., Industrial internet of things: Requirements, architecture, challenges, and future research directions, IEEE Access, 2022.
  • Alaca, Y.,Çelik, Y., Cyber attack detection with QR code images using lightweight deep learning models. Computers \& Security, 126, 103065, 2023.
  • Kutluana, G., Turker, I., Classification of cardiac disorders using weighted visibility graph features from ECG signals, Biomedical Signal Processing and Control, 87, 105420, 2024.
  • Altunay, H. C., Kritik Altyapılara Yönelik Derin Öğrenme Tabanlı Saldırı Tespit Sistemi Tasarımı, (Doctoral dissertation), 2023.
  • Altunay, H., C., Albayrak, Z., Network Intrusion Detection Approach Based on Convolutional Neural Network, Avrupa Bilim ve Teknoloji Dergisi, (26), 22-29, 2021.
  • Bharadiya, J. P., Machine learning and AI in business intelligence: Trends and opportunities, International Journal of Computer (IJC), 48(1), 123-134, 2023.
  • Sharifani, K., Amini, M., Machine Learning and Deep Learning: A Review of Methods and Applications, World Information Technology and Engineering Journal, 10(07), 3897-3904, 2023.
  • Choi, S., Yoon, S., Energy signature-based clustering using open data for urban building energy analysis toward carbon neutrality: A case study on electricity change under COVID-19, Sustainable Cities and Society, 92, 104471, 2023.
  • Landauer, M., Wurzenberger, M., Skopik, F., Hotwagner, W., Höld, G., Aminer: A modular log data analysis pipeline for anomaly-based intrusion detection, Digital Threats: Research and Practice, 4(1), 1-16,2023.
  • Bhavsar, M., Roy, K., Kelly, J., Olusola, O., Anomaly-based intrusion detection system for IoT application, Discover Internet of Things, 3(1), 5, 2023.
  • Sharma, B., Sharma, L., Lal, C., Roy, S., Anomaly based network intrusion detection for IoT attacks using deep learning technique, Computers and Electrical Engineering, 107, 108626, 2023.
  • Hnamte, V., Hussain, J., DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system, Telematics and Informatics Reports, 10, 100053, 2023.
  • Yin, Y., Jang-Jaccard, J., Xu, W., Singh, A., Zhu, J., Sabrina, F., Kwak, J., IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset, Journal of Big Data, 10(1), 1-26, 2023.
  • Davis J.J., Clark A.J., Data preprocessing for anomaly based network intrusion detection: A review, Computers \& Security, 30 (6-7), 353-375, 2011.
  • Naseer S., Saleem Y., Enhanced Network Intrusion Detection Using Deep Convolutional Neural Networks, KSII Trans. Internet Inf. Syst, 12 (10), 5159-5178, 2018.
  • Hancock J.T., Khoshgoftaar T.M., Survey on categorical data for neural networks, Journal of Big Data, 7, 1-41, 2020.
  • Tang C., Luktarhan N., Zhao Y., An Efficient Intrusion Detection Method Based on LightGBM and Autoencoder. Symmetry, 12 (9), 1458, 2020.
  • Aslan, Ö., Samet, R., Tanriöver, Ö.Ö, Using a Subtractive Center Behavioral Model to Detect Malware, Secur. Commun. Networks, 7501894, 1-17, 2020.
  • Mazini M., Shirazi B., Mahdavi I., Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms, Journal of King Saud University - Computer and Information Sciences, 32 (10), 1206-1207, 2019.
  • Balakrishnan S.M., Venkatalakshmi K., Kannan A., Intrusion Detection System Using Feature Selection and Classification Technique, IJCSA, 3 (4), 145, 2014.
  • Torabi M., Udzir N.I., Abdullah M.T., Yaakob R.A., Review on Feature Selection and Ensemble Techniques for Intrusion Detection System, IJACSA, 12 (5), 538-553, 2021.
  • Özkan Okay M., Aslan Ö., Eryiğit R., Samet R., SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN, IEEE Access, 9, 157639-157653, 2021.
  • Ambusaidi M.A., He X., Tan Z., Nanda P., Lu L.F., Nagar U.T., A Novel Feature Selection Approach for Intrusion Detection Data Classification, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, BeijingChina, 82-89, 24-26 Eylül, 2014.
  • Chen C.W., Tsai Y.H., Chang F.R., Lin W.C., Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results, Expert Systems, 37 (5), e12553, 2020.
  • Song J., Feature selection for intrusion detection system, Ph.D. Thesis, Aberystwyth University, Department of Computer Science Institute of Mathematics, Physics and Computer Science, Penglais-UK, 2016.
  • Kanimozhi V., Jacob P., Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing, ICT Express, 5 (3), 211-214, 2019.
  • Latah M., Toker L., Towards an efficient anomaly-based intrusion detection for software-defined networks, IET Netw., 7, 453-459, 2018.
  • Uǧurlu M., Doğru İ. A., Arslan R.S., Detection and classification of darknet traffic using machine learning methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (3), 1737-1746, 2023.
  • Aleesa, A., Younis, M. O. H. A. M. M. E. D., Mohammed, A. A., Sahar, N., Deep-intrusion detection system with enhanced UNSW-NB15 dataset based on deep learning techniques, Journal of Engineering Science and Technology, 16(1), 711-727, 2021.
  • Choudhary, S., Kesswani, N., Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT, Procedia Computer Science, 167, 1561-1573, 2020.
  • Yousefi-Azar, M., Varadharajan, V., Hamey, L., Tupakula, U., Autoencoder-based feature learning for cyber security applications. In 2017 International joint conference on neural networks (IJCNN) (pp. 3854-3861), IEEE, 2017.
  • Basati, A., Faghih, M., M., APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder. Neural Computing and Applications, 35(7), 4813-4833, 2023.
  • Altunay, H., C., Albayrak, Z., A hybrid CNN+ LSTM-based intrusion detection system for industrial IoT networks, Engineering Science and Technology, an International Journal, 38, 101322, 2023.
  • Abedi, A., Khan, S. S., Fedsl: Federated split learning on distributed sequential data in recurrent neural networks, Multimedia Tools and Applications, 1-212, 2023.
  • Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Lindauer, M., Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), e1484, 2023.

AUTOENCODER-BASED INTRUSION DETECTION IN CRITICAL INFRASTRUCTURES

Yıl 2024, Cilt: 2 Sayı: 1, 1 - 12, 02.08.2024

Öz

Securing critical infrastructure systems such as electricity, energy, health, management, transportation, and production facilities against cyber attacks is the issue on which states spend the most time and money when creating security strategies. Every day, different methods have emerged to prevent attackers who endanger our personal and national security with varying types of attacks. The most important of these methods is intrusion detection systems. This study proposes an autoencoder-based intrusion detection system model to detect security anomalies in critical infrastructures. The accuracy of this proposed model in attack detection has been tested with the current and complex UNSW-NB15 dataset. In the proposed model, training and testing steps were carried out using the attack packages in the data set. These packages are then divided into two: normal or attack. According to the results obtained in the experiments, it has been confirmed that the proposed intrusion detection system can effectively detect attacks with high performance.

Kaynakça

  • Kasongo S.M., Sun Y., Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset, J Big Data, 7, 105, 2020.
  • Yadav M.S., Kalpana R., Data Preprocessing for Intrusion Detection System Using Encoding and Normalization Approaches, 2019 11th International Conference on Advanced Computing (ICoAC), ChennaiIndia, 265-269, 18-20 Aralık, 2019.
  • Liu H., Zhou M., Liu Q., An embedded feature selection method for imbalanced data classification, in IEEE/CAA Journal of Automatica Sinica, 6 (3), 703-715, 2019.
  • Alabadi, M., Habbal, A., Wei, X., Industrial internet of things: Requirements, architecture, challenges, and future research directions, IEEE Access, 2022.
  • Alaca, Y.,Çelik, Y., Cyber attack detection with QR code images using lightweight deep learning models. Computers \& Security, 126, 103065, 2023.
  • Kutluana, G., Turker, I., Classification of cardiac disorders using weighted visibility graph features from ECG signals, Biomedical Signal Processing and Control, 87, 105420, 2024.
  • Altunay, H. C., Kritik Altyapılara Yönelik Derin Öğrenme Tabanlı Saldırı Tespit Sistemi Tasarımı, (Doctoral dissertation), 2023.
  • Altunay, H., C., Albayrak, Z., Network Intrusion Detection Approach Based on Convolutional Neural Network, Avrupa Bilim ve Teknoloji Dergisi, (26), 22-29, 2021.
  • Bharadiya, J. P., Machine learning and AI in business intelligence: Trends and opportunities, International Journal of Computer (IJC), 48(1), 123-134, 2023.
  • Sharifani, K., Amini, M., Machine Learning and Deep Learning: A Review of Methods and Applications, World Information Technology and Engineering Journal, 10(07), 3897-3904, 2023.
  • Choi, S., Yoon, S., Energy signature-based clustering using open data for urban building energy analysis toward carbon neutrality: A case study on electricity change under COVID-19, Sustainable Cities and Society, 92, 104471, 2023.
  • Landauer, M., Wurzenberger, M., Skopik, F., Hotwagner, W., Höld, G., Aminer: A modular log data analysis pipeline for anomaly-based intrusion detection, Digital Threats: Research and Practice, 4(1), 1-16,2023.
  • Bhavsar, M., Roy, K., Kelly, J., Olusola, O., Anomaly-based intrusion detection system for IoT application, Discover Internet of Things, 3(1), 5, 2023.
  • Sharma, B., Sharma, L., Lal, C., Roy, S., Anomaly based network intrusion detection for IoT attacks using deep learning technique, Computers and Electrical Engineering, 107, 108626, 2023.
  • Hnamte, V., Hussain, J., DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system, Telematics and Informatics Reports, 10, 100053, 2023.
  • Yin, Y., Jang-Jaccard, J., Xu, W., Singh, A., Zhu, J., Sabrina, F., Kwak, J., IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset, Journal of Big Data, 10(1), 1-26, 2023.
  • Davis J.J., Clark A.J., Data preprocessing for anomaly based network intrusion detection: A review, Computers \& Security, 30 (6-7), 353-375, 2011.
  • Naseer S., Saleem Y., Enhanced Network Intrusion Detection Using Deep Convolutional Neural Networks, KSII Trans. Internet Inf. Syst, 12 (10), 5159-5178, 2018.
  • Hancock J.T., Khoshgoftaar T.M., Survey on categorical data for neural networks, Journal of Big Data, 7, 1-41, 2020.
  • Tang C., Luktarhan N., Zhao Y., An Efficient Intrusion Detection Method Based on LightGBM and Autoencoder. Symmetry, 12 (9), 1458, 2020.
  • Aslan, Ö., Samet, R., Tanriöver, Ö.Ö, Using a Subtractive Center Behavioral Model to Detect Malware, Secur. Commun. Networks, 7501894, 1-17, 2020.
  • Mazini M., Shirazi B., Mahdavi I., Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms, Journal of King Saud University - Computer and Information Sciences, 32 (10), 1206-1207, 2019.
  • Balakrishnan S.M., Venkatalakshmi K., Kannan A., Intrusion Detection System Using Feature Selection and Classification Technique, IJCSA, 3 (4), 145, 2014.
  • Torabi M., Udzir N.I., Abdullah M.T., Yaakob R.A., Review on Feature Selection and Ensemble Techniques for Intrusion Detection System, IJACSA, 12 (5), 538-553, 2021.
  • Özkan Okay M., Aslan Ö., Eryiğit R., Samet R., SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN, IEEE Access, 9, 157639-157653, 2021.
  • Ambusaidi M.A., He X., Tan Z., Nanda P., Lu L.F., Nagar U.T., A Novel Feature Selection Approach for Intrusion Detection Data Classification, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, BeijingChina, 82-89, 24-26 Eylül, 2014.
  • Chen C.W., Tsai Y.H., Chang F.R., Lin W.C., Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results, Expert Systems, 37 (5), e12553, 2020.
  • Song J., Feature selection for intrusion detection system, Ph.D. Thesis, Aberystwyth University, Department of Computer Science Institute of Mathematics, Physics and Computer Science, Penglais-UK, 2016.
  • Kanimozhi V., Jacob P., Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing, ICT Express, 5 (3), 211-214, 2019.
  • Latah M., Toker L., Towards an efficient anomaly-based intrusion detection for software-defined networks, IET Netw., 7, 453-459, 2018.
  • Uǧurlu M., Doğru İ. A., Arslan R.S., Detection and classification of darknet traffic using machine learning methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (3), 1737-1746, 2023.
  • Aleesa, A., Younis, M. O. H. A. M. M. E. D., Mohammed, A. A., Sahar, N., Deep-intrusion detection system with enhanced UNSW-NB15 dataset based on deep learning techniques, Journal of Engineering Science and Technology, 16(1), 711-727, 2021.
  • Choudhary, S., Kesswani, N., Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT, Procedia Computer Science, 167, 1561-1573, 2020.
  • Yousefi-Azar, M., Varadharajan, V., Hamey, L., Tupakula, U., Autoencoder-based feature learning for cyber security applications. In 2017 International joint conference on neural networks (IJCNN) (pp. 3854-3861), IEEE, 2017.
  • Basati, A., Faghih, M., M., APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder. Neural Computing and Applications, 35(7), 4813-4833, 2023.
  • Altunay, H., C., Albayrak, Z., A hybrid CNN+ LSTM-based intrusion detection system for industrial IoT networks, Engineering Science and Technology, an International Journal, 38, 101322, 2023.
  • Abedi, A., Khan, S. S., Fedsl: Federated split learning on distributed sequential data in recurrent neural networks, Multimedia Tools and Applications, 1-212, 2023.
  • Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Lindauer, M., Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), e1484, 2023.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler
Bölüm Research Article
Yazarlar

Hakan Can Altunay

Zafer Albayrak 0000-0001-8358-3835

Muhammet Çakmak

Erken Görünüm Tarihi 17 Temmuz 2024
Yayımlanma Tarihi 2 Ağustos 2024
Gönderilme Tarihi 27 Aralık 2023
Kabul Tarihi 1 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 2 Sayı: 1

Kaynak Göster