Cyber Security Intrusion Detection Using a Deep Learning Method
The World is moving towards information technology dependence, the cornerstone of which is information security. As the number of active connections becomes large so is the need of security increasing day by day. Presently, billions of devices are connected and every hour 0.46 Million new devices ar...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2025-01-01
|
| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/3170 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561169253367808 |
|---|---|
| author | Basheer Ullah Shafiq-ur-Rehman Massan M. Abdul Rehman Rabia Ali Khan |
| author_facet | Basheer Ullah Shafiq-ur-Rehman Massan M. Abdul Rehman Rabia Ali Khan |
| author_sort | Basheer Ullah |
| collection | DOAJ |
| description | The World is moving towards information technology dependence, the cornerstone of which is information security. As the number of active connections becomes large so is the need of security increasing day by day. Presently, billions of devices are connected and every hour 0.46 Million new devices are connected to the web. Hence, due to this huge increase, the number of interconnections and the use of diverse protocols increases. Information and cyber security is a challenge worldwide and a big issue in business. One of the major aspects of information security is intrusion detection. It is important for cyber protection due an increasing number of cyber-attacks. Present methods to detect, predict and prevent malware still fall short of the desired level. The new techniques of deep learning are poised to succeed for detecting intrusion by employing different algorithms of detection and prevention. This paper proposes a deep neural network (DNN) for intrusion detection by the use of Kaggle NLS-KDD dataset with the highest attained accuracy of 92%. This detection method may prove to be very useful for ensuring cyber security of computers hence preventing data and economic loss. |
| format | Article |
| id | doaj-art-2ccc80a6f8944b5d918a7323073d61bf |
| institution | Kabale University |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-2ccc80a6f8944b5d918a7323073d61bf2025-01-03T05:23:58ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-01-01441697410.22581/muet1982.31703170Cyber Security Intrusion Detection Using a Deep Learning MethodBasheer Ullah0Shafiq-ur-Rehman Massan1M. Abdul Rehman2Rabia Ali Khan3Department of Computer Science and Information Systems, Khadim Ali Shah Bukhari Institute of Technology, Karachi, PakistanDepartment of Computer Science and Information Systems, Khadim Ali Shah Bukhari Institute of Technology, Karachi, PakistanDepartment of Computer Science, IBA Sukkur University, Sukkur, PakistanDepartment of Computer Science, Newports Institute of Communications and Economics, Karachi, PakistanThe World is moving towards information technology dependence, the cornerstone of which is information security. As the number of active connections becomes large so is the need of security increasing day by day. Presently, billions of devices are connected and every hour 0.46 Million new devices are connected to the web. Hence, due to this huge increase, the number of interconnections and the use of diverse protocols increases. Information and cyber security is a challenge worldwide and a big issue in business. One of the major aspects of information security is intrusion detection. It is important for cyber protection due an increasing number of cyber-attacks. Present methods to detect, predict and prevent malware still fall short of the desired level. The new techniques of deep learning are poised to succeed for detecting intrusion by employing different algorithms of detection and prevention. This paper proposes a deep neural network (DNN) for intrusion detection by the use of Kaggle NLS-KDD dataset with the highest attained accuracy of 92%. This detection method may prove to be very useful for ensuring cyber security of computers hence preventing data and economic loss.https://publications.muet.edu.pk/index.php/muetrj/article/view/3170 |
| spellingShingle | Basheer Ullah Shafiq-ur-Rehman Massan M. Abdul Rehman Rabia Ali Khan Cyber Security Intrusion Detection Using a Deep Learning Method Mehran University Research Journal of Engineering and Technology |
| title | Cyber Security Intrusion Detection Using a Deep Learning Method |
| title_full | Cyber Security Intrusion Detection Using a Deep Learning Method |
| title_fullStr | Cyber Security Intrusion Detection Using a Deep Learning Method |
| title_full_unstemmed | Cyber Security Intrusion Detection Using a Deep Learning Method |
| title_short | Cyber Security Intrusion Detection Using a Deep Learning Method |
| title_sort | cyber security intrusion detection using a deep learning method |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/3170 |
| work_keys_str_mv | AT basheerullah cybersecurityintrusiondetectionusingadeeplearningmethod AT shafiqurrehmanmassan cybersecurityintrusiondetectionusingadeeplearningmethod AT mabdulrehman cybersecurityintrusiondetectionusingadeeplearningmethod AT rabiaalikhan cybersecurityintrusiondetectionusingadeeplearningmethod |