Publisher

IAESONLINEIAESONLINE

Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Abstract

Cyber threats continue to grow in this era since the bad actors are attempting to exploit individuals, organisations, and systems. The latest development in artificial intelligence has unleashed strong agents at the fingertips of humanity. As open as it is, it has made more room for possible bad actors. Systems that can successfully counter these threat actors need to be created to rescue humanity. In this research work, RNN and Random Forest classifiers hybridised models are combined for the development of a Network Intrusion Detection System (NIDS) based on the benchmark dataset (CICIDS 2017). The requirement for an efficient and accurate method to detect network intrusions, both known and zero-day anomalies, is the primary problem considered. This research aims to enhance the accuracy and reliability of intrusion detection systems through a hybrid modelling approach. For evaluating the performance of the proposed model, various measures like accuracy, precision, recall, F1 measure, true positive rate, and true negative rate were employed. The hybrid model showed very good results with testing accuracy of 96.08%, precision of 96.0%, and recall of 96.0%, along with an F1 measure of 96.0%. The result of the experiment indicates that the model is effective and, when implemented, can detect and classify cyberattacks in modern environments.

Conclusion

The achievements of this research are significant.The hybrid model demonstrated an impressive accuracy of 96.08% during testing, showcasing its ability to classify most network traffic instances correctly.0%, the model effectively minimised false positives and false negatives, achieving a balance crucial for reducing unnecessary alerts and ensuring that actual threats are not overlooked.The F1 score also agrees with this opinion at 96.0%, indicating that the model effectively handled the tradeoff between precision and recall.In addition, the model had maintained low FPR and FNR—1.29%, respectively, meaning the system was reliable and trustworthy.The model also achieved high sensitivity and specificity with a TPR of 96.

Future Research

Berdasarkan latar belakang, metode, hasil, keterbatasan, dan juga bagian saran penelitian lanjutan, berikut adalah beberapa saran penelitian lanjutan yang dapat dikembangkan:. . Pertama, penelitian selanjutnya dapat mengeksplorasi penggunaan teknik pembelajaran transfer (transfer learning) untuk meningkatkan kinerja model deteksi intrusi pada dataset yang lebih kecil atau dengan karakteristik yang berbeda. Hal ini dapat dilakukan dengan melatih model awal pada dataset yang besar dan kemudian menyesuaikannya dengan dataset yang lebih kecil, sehingga mengurangi kebutuhan data pelatihan dan meningkatkan kemampuan generalisasi model.. . Kedua, penelitian dapat difokuskan pada pengembangan model deteksi intrusi yang mampu beradaptasi secara dinamis terhadap perubahan pola serangan. Hal ini dapat dicapai dengan mengintegrasikan mekanisme pembelajaran berkelanjutan (continual learning) ke dalam model, sehingga model dapat terus belajar dan menyesuaikan diri dengan serangan baru tanpa melupakan pengetahuan sebelumnya.. . Ketiga, penelitian dapat menginvestigasi penggunaan teknik interpretasi model (model interpretability) untuk memahami bagaimana model membuat keputusan deteksi. Hal ini penting untuk meningkatkan kepercayaan pengguna terhadap model dan untuk mengidentifikasi potensi bias atau kelemahan dalam model. Dengan memahami alasan di balik keputusan model, kita dapat meningkatkan akurasi dan keandalan model secara keseluruhan.

References

1. Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning | IIETA. development... iieta.org/journals/ijsse/paper/10.18280/ijsse.130401Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning IIETA development iieta journals ijsse paper 10 18280 ijsse 130401
2. Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional... iieta.org/journals/mmep/paper/10.18280/mmep.100113Development of a Web and Mobile Applications Based Cassava Disease Classification Interface Using Convolutional iieta journals mmep paper 10 18280 mmep 100113
3. Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms | Mary... doi.org/10.52549/ijeei.v13i3.5890Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms Mary doi 10 52549 ijeei v13i3 5890

Subjects

1. #cloud computing#cloud computing
2. #intrusion detection#intrusion detection

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