The Experts below are selected from a list of 175650 Experts worldwide ranked by ideXlab platform
Youki Kadobayashi - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting `abnormal' or protecting `normal' traffic.
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String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Tao Ban, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting ‘abnormal’ or protecting ‘normal’ traffic.
Zbynek Michlovský - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting `abnormal' or protecting `normal' traffic.
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String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Tao Ban, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting ‘abnormal’ or protecting ‘normal’ traffic.
Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting `abnormal' or protecting `normal' traffic.
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String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Tao Ban, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting ‘abnormal’ or protecting ‘normal’ traffic.
Shaoning Pang - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting `abnormal' or protecting `normal' traffic.
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String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Tao Ban, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting ‘abnormal’ or protecting ‘normal’ traffic.
Tao Ban - One of the best experts on this subject based on the ideXlab platform.
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String Kernel Based SVM for Internet Security Implementation
Neural Information Processing, 2009Co-Authors: Zbynek Michlovský, Tao Ban, Shaoning Pang, Nikola Kasabov, Youki KadobayashiAbstract:For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet Security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network Security for either detecting ‘abnormal’ or protecting ‘normal’ traffic.
