Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset

Senthilkumar Perumal; Kumaresan Devarajan

Makale detayları

Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset

Yazarlar:

Senthilkumar Perumal | Kumaresan Devarajan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Computer System Software

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.535-543

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339464

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 Dynamic cyber threats are screaming for better anomaly detection techniques in Intrusion Detection Systems. Organizations today are hugely dependent on digital infrastructures for which effective security is priceless. The following research article does a critical and comparative analysis among three popular algorithms, namely Amazon Sage Maker Random Cut Forest, Robust Random Cut Forest, and traditional Random Cut Forest. Using the CIS IDS 2017 dataset with multifaceted network traffic features together with the labeled type of attack, this work rigorously tests the performance in anomaly detection that may show potential intrusion, robustness, scalability, and adaptability of each algorithm. The comparative analysis does the performance metrics of each algorithm based on accuracy, precision, recall, and F1-score in a real-world setting. The findings are expected to provide useful insights toward optimizing IDS frameworks for hi-tech cybersecurity resilience. Finally, an organization can make decisions on its strategy regarding cyber security by being enlightened on the strengths and weaknesses of algorithms. In essence, this paper contributes to the larger body of research on enhancing intrusion detection methodologies in an environment that is confronted by sophisticated cyber-attacks.

 Dynamic cyber threats are screaming for better anomaly detection techniques in Intrusion Detection Systems. Organizations today are hugely dependent on digital infrastructures for which effective security is priceless. The following research article does a critical and comparative analysis among three popular algorithms, namely Amazon Sage Maker Random Cut Forest, Robust Random Cut Forest, and traditional Random Cut Forest. Using the CIS IDS 2017 dataset with multifaceted network traffic features together with the labeled type of attack, this work rigorously tests the performance in anomaly detection that may show potential intrusion, robustness, scalability, and adaptability of each algorithm. The comparative analysis does the performance metrics of each algorithm based on accuracy, precision, recall, and F1-score in a real-world setting. The findings are expected to provide useful insights toward optimizing IDS frameworks for hi-tech cybersecurity resilience. Finally, an organization can make decisions on its strategy regarding cyber security by being enlightened on the strengths and weaknesses of algorithms. In essence, this paper contributes to the larger body of research on enhancing intrusion detection methodologies in an environment that is confronted by sophisticated cyber-attacks.

 Dynamic cyber threats are screaming for better anomaly detection techniques in Intrusion Detection Systems. Organizations today are hugely dependent on digital infrastructures for which effective security is priceless. The following research article does a critical and comparative analysis among three popular algorithms, namely Amazon Sage Maker Random Cut Forest, Robust Random Cut Forest, and traditional Random Cut Forest. Using the CIS IDS 2017 dataset with multifaceted network traffic features together with the labeled type of attack, this work rigorously tests the performance in anomaly detection that may show potential intrusion, robustness, scalability, and adaptability of each algorithm. The comparative analysis does the performance metrics of each algorithm based on accuracy, precision, recall, and F1-score in a real-world setting. The findings are expected to provide useful insights toward optimizing IDS frameworks for hi-tech cybersecurity resilience. Finally, an organization can make decisions on its strategy regarding cyber security by being enlightened on the strengths and weaknesses of algorithms. In essence, this paper contributes to the larger body of research on enhancing intrusion detection methodologies in an environment that is confronted by sophisticated cyber-attacks.

 Dynamic cyber threats are screaming for better anomaly detection techniques in Intrusion Detection Systems. Organizations today are hugely dependent on digital infrastructures for which effective security is priceless. The following research article does a critical and comparative analysis among three popular algorithms, namely Amazon Sage Maker Random Cut Forest, Robust Random Cut Forest, and traditional Random Cut Forest. Using the CIS IDS 2017 dataset with multifaceted network traffic features together with the labeled type of attack, this work rigorously tests the performance in anomaly detection that may show potential intrusion, robustness, scalability, and adaptability of each algorithm. The comparative analysis does the performance metrics of each algorithm based on accuracy, precision, recall, and F1-score in a real-world setting. The findings are expected to provide useful insights toward optimizing IDS frameworks for hi-tech cybersecurity resilience. Finally, an organization can make decisions on its strategy regarding cyber security by being enlightened on the strengths and weaknesses of algorithms. In essence, this paper contributes to the larger body of research on enhancing intrusion detection methodologies in an environment that is confronted by sophisticated cyber-attacks.

Makale detayları

Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset

Yazarlar:

Senthilkumar Perumal | Kumaresan Devarajan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Computer System Software

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.535-543

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339464

pISSN:

2587-1366

Cyber Threats

Anomaly Detection

Intrusion Detection Systems

Amazon Sage Maker Random Cut Forest

Robust Random Cut Forest

Sayı

Makale detayları

Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset

Yazarlar: Senthilkumar Perumal | Kumaresan Devarajan

Yayıncı: Murat YAKAR

Ana Başlıklar: Computer System Software

Dergi: Turkish Journal of Engineering Vol.9 Issue 3, pp.535-543

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001339464

pISSN: 2587-1366

Cyber Threats

Anomaly Detection

Intrusion Detection Systems

Amazon Sage Maker Random Cut Forest

Robust Random Cut Forest

Sayı

Yazarlar:

Senthilkumar Perumal | Kumaresan Devarajan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Computer System Software

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.535-543

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339464

pISSN:

2587-1366