Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels.However,cyb...Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels.However,cybersecurity attacks are still increasing.An ID system can play a vital role in detecting such threats.Existing ID systems are unable to detect malicious threats,primarily because they adopt approaches that are based on traditional ML techniques,which are less concerned with the accurate classication and feature selection.Thus,developing an accurate and intelligent ID system is a priority.The main objective of this study was to develop a hybrid intelligent intrusion detection system(HIIDS)to learn crucial features representation efciently and automatically from massive unlabeled raw network trafc data.Many ID datasets are publicly available to the cybersecurity research community.As such,we used a spark MLlib(machine learning library)-based robust classier,such as logistic regression(LR),extreme gradient boosting(XGB)was used for anomaly detection,and a state-of-the-art DL,such as a long short-term memory autoencoder(LSTMAE)for misuse attack was used to develop an efcient and HIIDS to detect and classify unpredictable attacks.Our approach utilized LSTM to detect temporal features and an AE to more efciently detect global features.Therefore,to evaluate the efcacy of our proposed approach,experiments were conducted on a publicly existing dataset,the contemporary real-life ISCX-UNB dataset.The simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS signicantly outperformed existing ID approaches,achieving a high accuracy rate of up to 97.52%for the ISCX-UNB dataset respectively 10-fold crossvalidation test.It is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.展开更多
Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university...Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university’s big data.Taking the student card information as the research sample,using spark big data mining technology and K-Means clustering algorithm,taking scholarship evaluation as an example,the big data is analyzed.Data includes analysis of students’daily behavior from multiple dimensions,and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism,votes of teachers and students,etc.At the same time,students’absenteeism,physical health and psychological status in advance can be predicted,which makes student management work more active,accurate and effective.展开更多
基金supported by the MSIT(Ministry of Science,ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2016-0-00465)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)。
文摘Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels.However,cybersecurity attacks are still increasing.An ID system can play a vital role in detecting such threats.Existing ID systems are unable to detect malicious threats,primarily because they adopt approaches that are based on traditional ML techniques,which are less concerned with the accurate classication and feature selection.Thus,developing an accurate and intelligent ID system is a priority.The main objective of this study was to develop a hybrid intelligent intrusion detection system(HIIDS)to learn crucial features representation efciently and automatically from massive unlabeled raw network trafc data.Many ID datasets are publicly available to the cybersecurity research community.As such,we used a spark MLlib(machine learning library)-based robust classier,such as logistic regression(LR),extreme gradient boosting(XGB)was used for anomaly detection,and a state-of-the-art DL,such as a long short-term memory autoencoder(LSTMAE)for misuse attack was used to develop an efcient and HIIDS to detect and classify unpredictable attacks.Our approach utilized LSTM to detect temporal features and an AE to more efciently detect global features.Therefore,to evaluate the efcacy of our proposed approach,experiments were conducted on a publicly existing dataset,the contemporary real-life ISCX-UNB dataset.The simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS signicantly outperformed existing ID approaches,achieving a high accuracy rate of up to 97.52%for the ISCX-UNB dataset respectively 10-fold crossvalidation test.It is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.
基金Nanjing Key Laboratory of Intelligent Information Processing Open Fund Project(No.19AIP05)。
文摘Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university’s big data.Taking the student card information as the research sample,using spark big data mining technology and K-Means clustering algorithm,taking scholarship evaluation as an example,the big data is analyzed.Data includes analysis of students’daily behavior from multiple dimensions,and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism,votes of teachers and students,etc.At the same time,students’absenteeism,physical health and psychological status in advance can be predicted,which makes student management work more active,accurate and effective.
