A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms
Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2013-02-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_84098_7028bb6f915a4f753d77cfabfde3460e.pdf |
| _version_ | 1797325655114776576 |
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| author | Soukaena Hassan Hashim Inas Ali Abdulmunem |
| author_facet | Soukaena Hassan Hashim Inas Ali Abdulmunem |
| author_sort | Soukaena Hassan Hashim |
| collection | DOAJ |
| description | Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse detection techniques using two DM classifiers (Interactive Dichotomizer 3 (ID3) classifier and Naïve Bayesian (NB) Classifier) to verify the validity of the proposed system in term of accuracy rate. A proposed HybD dataset used in training and testing the hybrid IDS. Feature selection is used to consider the intrinsic features in classification decision, this accomplished by using three different measures: Association rules (AR) method, ReliefF measure, and Gain Ratio (GR) measure. NB classifier with AR method given the most accurate classification results (99%) with false positive (FP) rate (0%) and false negative (FN) rate (1%). |
| first_indexed | 2024-03-08T06:12:30Z |
| format | Article |
| id | doaj.art-6d4d78eaa67c4510aa3f78d434c8949c |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:12:30Z |
| publishDate | 2013-02-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-6d4d78eaa67c4510aa3f78d434c8949c2024-02-04T17:34:21ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582013-02-01312 B14215510.30684/etj.31.2B.384098A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification AlgorithmsSoukaena Hassan HashimInas Ali AbdulmunemMalicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse detection techniques using two DM classifiers (Interactive Dichotomizer 3 (ID3) classifier and Naïve Bayesian (NB) Classifier) to verify the validity of the proposed system in term of accuracy rate. A proposed HybD dataset used in training and testing the hybrid IDS. Feature selection is used to consider the intrinsic features in classification decision, this accomplished by using three different measures: Association rules (AR) method, ReliefF measure, and Gain Ratio (GR) measure. NB classifier with AR method given the most accurate classification results (99%) with false positive (FP) rate (0%) and false negative (FN) rate (1%).https://etj.uotechnology.edu.iq/article_84098_7028bb6f915a4f753d77cfabfde3460e.pdfmalwareintrusion detection systemhybriddata mining |
| spellingShingle | Soukaena Hassan Hashim Inas Ali Abdulmunem A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms Engineering and Technology Journal malware intrusion detection system hybrid data mining |
| title | A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms |
| title_full | A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms |
| title_fullStr | A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms |
| title_full_unstemmed | A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms |
| title_short | A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms |
| title_sort | proposal to detect computer worms malicious codes using data mining classification algorithms |
| topic | malware intrusion detection system hybrid data mining |
| url | https://etj.uotechnology.edu.iq/article_84098_7028bb6f915a4f753d77cfabfde3460e.pdf |
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