Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing
(چکیده مقاله) :
Abstract :
One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important
component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in
detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are
emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of
botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial
intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity
(CIC) on AWS (Amazon Web Services).
The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area
under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of
Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed
system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time
network traffic data analysis.
component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in
detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are
emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of
botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial
intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity
(CIC) on AWS (Amazon Web Services).
The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area
under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of
Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed
system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time
network traffic data analysis.
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار:2019
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 6 صفحه
سال انتشار:2019
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 6 صفحه
Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2019 - مقاله ISI / نویسندگان: V. Kanimozhi , Dr. T. Prem Jacob
Sent date(تاریخ ارسال) :
1398/02/27 | 5/17/2019
Number of visits(تعداد بازدید):
997
Key words (کلمات کلیدی):
Artificial Intelligence, AWS, CSE-CIC-IDS2018, hyper-parameter optimization, realistic network traffic cyber dataset
Number of pages(تعداد صفحات) :
6
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