How to cite (IEEE): A. Maslan, K. Mohamad, A. Hamid, H. Pangaribuan, & S. Sitohang "Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 815-822, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1533

How to cite (APA): Maslan, A., Mohamad, K., Hamid, A., Pangaribuan, H., & Sitohang, S. (2023). Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques. JOIV : International Journal on Informatics Visualization, 7(3), 815-822. https://doi.org/10.30630/joiv.7.3.1533

How to cite (Chicago): Maslan, Andi, Mohamad, Kamaruddin Malik, Hamid, Abdul, Pangaribuan, Hotma, AND Sitohang, Sunarsan. "Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1533

How to cite (Vancouver): Maslan A, Mohamad K, Hamid A, Pangaribuan H, & Sitohang S . Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):815-822. https://doi.org/10.30630/joiv.7.3.1533

How to cite (Harvard): Maslan, A., Mohamad, K., Hamid, A., Pangaribuan, H., & Sitohang, S. ,2023. Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 815-822. https://doi.org/10.30630/joiv.7.3.1533

How to cite (MLA8): Maslan, Andi, Kamaruddin Malik Bin Mohamad, Abdul Hamid, Hotma Pangaribuan, & Sunarsan Sitohang. "Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 815-822. Web. 2 Oct. 2023 , https://doi.org/10.30630/joiv.7.3.1533

BibTex Citation Data :

@article{JOIV1533,
    	author = {Andi Maslan and Kamaruddin Malik Mohamad and Abdul Hamid and Hotma Pangaribuan and Sunarsan Sitohang},
    	title = {Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {Chi-square distance; DDoS; Heuristic; N-Gram; Payload},
    	abstract = {Various forms of distributed denial of service (DDoS) assault systems and servers, including traffic overload, request overload, and website breakdowns. Heuristic-based DDoS attack detection is a combination of anomaly-based and pattern-based methods, and it is one of three DDoS attack detection techniques available. The pattern-based method compares a sequence of data packets sent across a computer network using a set of criteria. However, it cannot identify modern assault types, and anomaly-based methods take advantage of the habits that occur in a system. However, this method is difficult to apply because the accuracy is still low, and the false positives are relatively high. Therefore, this study proposes feature selection based on Hybrid N-Gram Heuristic Techniques. The research starts with the conversion process, package extract, and hex payload analysis, focusing on the HTTP protocol. The results show the Hybrid N-Gram Heuristic-based feature selection for the CIC-2017 dataset with the SVM algorithm on the CSDPayload+N-Gram feature with a 4-Gram accuracy rate of 99.86%, MIB- Dataset 2016 with the 2016 algorithm. SVM and CSPayload feature +N-Gram with 100% accuracy for 4-Gram, H2N-Payload Dataset with SVM Algorithm, and CSDPayload+N-Gram feature with 100% accuracy for 4-Gram. As a comparison, the KNN algorithm for 4-Gram has an accuracy rate of 99.44%, and the Neural Network Algorithm has an accuracy rate of 100% for 4-Gram. Thus, the best algorithm for DDoS detection is SVM with Hybrid N-Gram (4-Gram). },
        	issn = {2549-9904},    	pages = {815--822},    	doi = {10.30630/joiv.7.3.1533},
        	url = {https://joiv.org/index.php/joiv/article/view/1533}
    }
    

Refworks Citation Data :

@article{{JOIV}{1533}, author = {Maslan, A., Mohamad, K., Hamid, A., Pangaribuan, H., Sitohang, S.}, title = {Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1533}, url = {} }