DGA-based botnet detection toward imbalanced multiclass learning

Chen, Yijing; Pang, Bo; Shao, Guolin; Wen, Guozhu; Chen, Xingshu

doi:10.26599/tst.2020.9010021

Cited by 13 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The domain names assigned for the sites will receive control from the malware and give their instructions. DGAs are common in Malware, which endeavors to install command and control communication with the botmaster and the infected machine (Chen et al, 2021). This is referred to as "command and control" or C2 (Yu et al, 2019).…”

Section: E Domain Generation Algorithm (Dga)mentioning

confidence: 99%

“…The domain names generated by DGA are generally detected by extracting the features of DNS traffic and statistical characteristics of the domain name language. Later, the ML algorithms analyze the extracted features to identify and classify the DGA domain names (Chen et al, 2021). The authors (Chen et al, 2021) Nowadays, ML approaches are susceptible to adversarial instances through Generative Adversarial Networks (GANs).…”

Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

“…Later, the ML algorithms analyze the extracted features to identify and classify the DGA domain names (Chen et al, 2021). The authors (Chen et al, 2021) Nowadays, ML approaches are susceptible to adversarial instances through Generative Adversarial Networks (GANs). It is an unsupervised ML technique that combines a generator and a discriminator (Gümüşbaş et al, 2020;Rao et al, 2020).…”

Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

See 2 more Smart Citations

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

View full text Add to dashboard Cite

Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

show abstract

Section: E Domain Generation Algorithm (Dga)mentioning

confidence: 99%

Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Finally, they found RNN is the best model for intrusion detection by using the KDD dataset. Yijing Chen et al [12] proposed a domain that generates an algorithm for botnet classification. It was a multiple classification problem.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Ismail

Mohmand

Hussain³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Distributed network attacks are often referred to as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, THE UNWS-np-15 dataset from GitHub and python used as a simulator. After applying the machine learning models generated a confusion matrix for model performance identification. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for Random Forest Algorithm. The average Accuracy (AC) of model is 89% which is extremely good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are 90% for XGBoost. The average Accuracy (AC) of model is 90%. By comparing work to existing research work, the accuracy of defect determination improved as compare to existing research work which is 85% and 79%.

show abstract

“…Chen proposed a Long Short-Term Memory-based Property and Quantity Dependent Optimization (LSTM.PQDO) method. The method realizes the dynamic optimization of the resampling proportion and overcome the difficulties of imbalanced datasets 14 . Hou proposed a time-varying optimization module to optimize the results of special periods and effectively eliminate imbalances 15 .…”

Section: Introductionmentioning

confidence: 99%

Research on expansion and classification of imbalanced data based on SMOTE algorithm

Wang

Dai

Shen

et al. 2021

Sci Rep

View full text Add to dashboard Cite

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments.

show abstract

DGA-based botnet detection toward imbalanced multiclass learning

Cited by 13 publications

References 18 publications

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Research on expansion and classification of imbalanced data based on SMOTE algorithm

Contact Info

Product

Resources

About