Application of Artificial Neural Network in Detection of Probing Attacks
The prevention of any type of cyber attack is indispensable because a single attack may break the security of computer and network systems. The hindrance of such attacks is entirely dependent on their detection. The detection is a major part of any security tool such as Intrusion Detection System (I...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/2590/ http://scholars.utp.edu.my/id/eprint/2590/1/IEEE-ahmad-eprinted.pdf |
| _version_ | 1848659270190497792 |
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| author | Ahmad, iftikhar Azween, Abdullah Alghamdi, Abdullah |
| author_facet | Ahmad, iftikhar Azween, Abdullah Alghamdi, Abdullah |
| author_sort | Ahmad, iftikhar |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | The prevention of any type of cyber attack is indispensable because a single attack may break the security of computer and network systems. The hindrance of such attacks is entirely dependent on their detection. The detection is a major part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls. Consequently, in this paper, we are contemplating the feasibility of an approach to probing attacks that are the basis of others attacks in computer network systems. Our approach adopts a supervised neural network phenomenon that is majorly used for detecting security attacks. The proposed system takes into account Multiple Layered Perceptron (MLP) architecture and resilient backpropagation for its training and testing. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The developed system is applied to different probing attacks. Furthermore, its performance is compared to other neural networks’ approaches and the results indicate that our approach is more precise and accurate in case of false positive, false negative and detection rate. |
| first_indexed | 2025-11-13T07:27:45Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:2590 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:27:45Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:25902017-01-19T08:25:12Z http://scholars.utp.edu.my/id/eprint/2590/ Application of Artificial Neural Network in Detection of Probing Attacks Ahmad, iftikhar Azween, Abdullah Alghamdi, Abdullah QA75 Electronic computers. Computer science The prevention of any type of cyber attack is indispensable because a single attack may break the security of computer and network systems. The hindrance of such attacks is entirely dependent on their detection. The detection is a major part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls. Consequently, in this paper, we are contemplating the feasibility of an approach to probing attacks that are the basis of others attacks in computer network systems. Our approach adopts a supervised neural network phenomenon that is majorly used for detecting security attacks. The proposed system takes into account Multiple Layered Perceptron (MLP) architecture and resilient backpropagation for its training and testing. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The developed system is applied to different probing attacks. Furthermore, its performance is compared to other neural networks’ approaches and the results indicate that our approach is more precise and accurate in case of false positive, false negative and detection rate. 2009-10-06 Conference or Workshop Item PeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/2590/1/IEEE-ahmad-eprinted.pdf Ahmad, iftikhar and Azween, Abdullah and Alghamdi, Abdullah (2009) Application of Artificial Neural Network in Detection of Probing Attacks. In: 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), 4-6 October, 2009, Kuala Lumpur . http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5356382&isnumber=5356300 |
| spellingShingle | QA75 Electronic computers. Computer science Ahmad, iftikhar Azween, Abdullah Alghamdi, Abdullah Application of Artificial Neural Network in Detection of Probing Attacks |
| title | Application of Artificial Neural Network in Detection of Probing Attacks |
| title_full | Application of Artificial Neural Network in Detection of Probing Attacks |
| title_fullStr | Application of Artificial Neural Network in Detection of Probing Attacks |
| title_full_unstemmed | Application of Artificial Neural Network in Detection of Probing Attacks |
| title_short | Application of Artificial Neural Network in Detection of Probing Attacks |
| title_sort | application of artificial neural network in detection of probing attacks |
| topic | QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/2590/ http://scholars.utp.edu.my/id/eprint/2590/ http://scholars.utp.edu.my/id/eprint/2590/1/IEEE-ahmad-eprinted.pdf |