Artificial Intelligence based Intrusion Detection System
Volume 18, Issue 2, 2024
Download| Author(s): |
Muhammad Moosa Bin Naseem College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , moosabinnaseem@gmail.com Sana Alam* College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , sana.alam@iobm.edu.pk Muhammad Abbas College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , dr.abbas@iobm.edu.pk Asghar Khan College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , muhammad.asghar@iobm.edu.pk Asim Ifthikhar College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , asim.iftikhar@iobm.edu.pk Wazir Ali College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , wazir.ali@iobm.edu.pk Qurrat-ul-Ain Naiyar College of Computer Science and Information Systems, IoBM, Karachi, Pakistan , qurratulain.naiyar@iobm.edu.pk |
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| Abstract | One of the major aspects in today’s digital world is to ensure network security. Traditional Intrusion Detection Systems (IDS) mainly focus on the identification and categorization of active attacks, often leaving networks vulnerable to passive and emerging threats. This article presents an advanced IDS that uses deep learning, specifically stacked Long Short-Term Memory (LSTM) and the CatBoost algorithm, to detect anomalies in network traffic. The proposed system is designed to flag suspicious IP addresses in a proactive manner to detect potential passive attacks before escalation. We train the IDS on LUFLow's 2021 dataset using the LSTM neural network with the agility of CatBoost. The proposed model yields an Area Under the Curve (AUC) score of 87%, which shows its performance in detecting normal and anomalous traffic. The proposed IDS has the capability to monitor and detect the cyber threats in real-time. |
| Keywords | Intrusion Detection Systems, Artificial Intelligence, Catboost, LSTM-AutoEncoder, Classification, Anomaly Detection |
| Year | 2024 |
| Volume | 18 |
| Issue | 2 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/18.2/2.pdf | Page |