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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Andi Maslan
Putera Batam University
Computer Science & IT

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Enhancement detection distributed denial of service attacks using hybrid n-gram techniques Andi Maslan; Kamaruddin Malik Mohamad; Cik Feresa Mohd Foozy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.18103

Abstract

Distributed denial of service (DDoS) attacks have always been a concern of cyber experts. To detect DDoS attacks, several methods can be used. One of the methods used in this research is the n-gram technique. The n-gram approach analyzes the payload of data packets that enter the network to obtain attack patterns. Data is captured and analyzed, after which it is compared with clean data packets. The chi-square distance value close to 1 indicates that the two packages are very similar so that the data packet is not an attack. A deal less than one means the data packet is categorized as an attack. In this research, the threshold for determining the attack level can be lowered to obtain a very high detection accuracy. As a result, the 2-gram technique has a detection accuracy rate with the lowest false positive value of around 13%, with the highest actual positive ratio reaching 99.98%.
Enhancement detection distributed denial of service attacks using hybrid n-gram techniques Andi Maslan; Kamaruddin Malik Mohamad; Cik Feresa Mohd Foozy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.18103

Abstract

Distributed denial of service (DDoS) attacks have always been a concern of cyber experts. To detect DDoS attacks, several methods can be used. One of the methods used in this research is the n-gram technique. The n-gram approach analyzes the payload of data packets that enter the network to obtain attack patterns. Data is captured and analyzed, after which it is compared with clean data packets. The chi-square distance value close to 1 indicates that the two packages are very similar so that the data packet is not an attack. A deal less than one means the data packet is categorized as an attack. In this research, the threshold for determining the attack level can be lowered to obtain a very high detection accuracy. As a result, the 2-gram technique has a detection accuracy rate with the lowest false positive value of around 13%, with the highest actual positive ratio reaching 99.98%.
Feature selection to improve distributed denial of service detection accuracy using hybrid N-Gram heuristic techniques Andi Maslan; Abdul Hamid; Dedy Fitriawan; Anggia Dasa Putri; Tukino Tukino
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.24913

Abstract

Distributed denial of service (DDoS) attacks servers and computers in various ways, such as flooding traffic. There are three DDoS detection methods, namely anomaly-based, pattern-based and heuristic-based. However, pattern-based methods cannot detect recent attacks, while anomaly-based methods have low accuracy and relatively high false positives. This research proposes increasing accuracy using a heuristic-based DDoS detection method and a new feature. The combination of CSDPayload+N-Gram and CSPayload+N-Gram features is called hybrid N-Gram, which is analysed on four datasets: CIC2017, CIC2019, MIB-2016, and H2NPayload. Next, calculate Chi-square distance (CSD) and cosine similarity (CS) using the N-Gram frequency value results. Subsequently, compute Pearson Chi-square using the N-Gram frequency value results. Compare the CSDPayload+N-Gram and CSPayload+N-Gram, along with the Pearson Chi-square value, to classify it as either DDoS or not. Finally, feature selection based on weight correlation and payload classification employs machine learning algorithms: support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). The average accuracy rate for detecting DDoS attacks across four datasets, utilising the CSDPayload+4-Gram and CSPayload+4-Gram features with the SVM algorithm, is 99.71%, which surpasses the accuracy achieved by using KNN (96.22%) and NNs (99.50%) imitation. Thus, the best algorithm for detecting DDoS is SVM with hybrid 4-Gram.
Co-Authors Abdul Hamid Anggia Dasa Putri Cik Feresa Mohd Foozy Dedy Fitriawan Kamaruddin Malik Mohamad Tukino Tukino