The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform

Manpyo Hong - One of the best experts on this subject based on the ideXlab platform.

  • content based File type identification using cosine similarity and a divide and conquer approach
    Iete Technical Review, 2010
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    AbstractIdentifying the File type (TXT, EXE, JPEG, etc.) is important for computer security applications such as computer forensics, steganalysis, and antivirus programs. The common approach for this is to use File Extensions, magic numbers, or other header information. However, these are susceptible to tampering or corruption; for instance, the File Extension can be easily spoofed and the magic numbers can be obfuscated. A more reliable approach may be to analyze the File content instead of using only the tip of the information (metadata). This paper proposes two methods based on the File content. First, we use the cosine distance as a similarity metric when comparing the File content rather than the Mahalanobis distance that is popular and has been used by the other related approaches. The cosine similarity (unlike the Mahalanobis distance) retains the classification accuracy on a small number of highly frequent byte patterns which leads to a smaller model size and faster detection rate. Second, we deco...

  • on improving the accuracy and performance of content based File type identification
    Australasian Conference on Information Security and Privacy, 2009
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    Types of Files (text, executables, Jpeg images, etc.) can be identified through File Extension, magic number, or other header information in the File. However, they are easy to be tampered or corrupted so cannot be trusted as secure ways to identify File types.In the presence of adversaries, analyzing the File content may be a more reliable way to identify File types, but existing approaches of File type analysis still need to be improved in terms of accuracy and speed. Most of them use byte-frequency distribution as a feature in building a representative model of a File type, and apply a distance metric to compare the model with byte-frequency distribution of the File in question. Mahalanobis distance is the most popular distance metric. In this paper, we propose 1) the cosine similarity as a better metric than Mahalanobis distance in terms of classification accuracy, smaller model size, and faster detection rate, and 2) a new type-identification scheme that applies recursive steps to identify types of Files. We compare the cosine similarity to Mahalanobis distance using Wei-Hen Li et al.'s single and multi-centroid modeling techniques, which showed 4.8% and 13.10% improvement in classification accuracy (single and multi-centroid respectively). The cosine similarity showed reduction of the model size by about 90% and improvement in the detection speed by 11%. Our proposed type identification scheme showed 37.78% and 31.47% improvement over Wei-Hen Li's single and multi-centroid modeling techniques respectively.

Irfan Ahmed - One of the best experts on this subject based on the ideXlab platform.

  • content based File type identification using cosine similarity and a divide and conquer approach
    Iete Technical Review, 2010
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    AbstractIdentifying the File type (TXT, EXE, JPEG, etc.) is important for computer security applications such as computer forensics, steganalysis, and antivirus programs. The common approach for this is to use File Extensions, magic numbers, or other header information. However, these are susceptible to tampering or corruption; for instance, the File Extension can be easily spoofed and the magic numbers can be obfuscated. A more reliable approach may be to analyze the File content instead of using only the tip of the information (metadata). This paper proposes two methods based on the File content. First, we use the cosine distance as a similarity metric when comparing the File content rather than the Mahalanobis distance that is popular and has been used by the other related approaches. The cosine similarity (unlike the Mahalanobis distance) retains the classification accuracy on a small number of highly frequent byte patterns which leads to a smaller model size and faster detection rate. Second, we deco...

  • on improving the accuracy and performance of content based File type identification
    Australasian Conference on Information Security and Privacy, 2009
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    Types of Files (text, executables, Jpeg images, etc.) can be identified through File Extension, magic number, or other header information in the File. However, they are easy to be tampered or corrupted so cannot be trusted as secure ways to identify File types.In the presence of adversaries, analyzing the File content may be a more reliable way to identify File types, but existing approaches of File type analysis still need to be improved in terms of accuracy and speed. Most of them use byte-frequency distribution as a feature in building a representative model of a File type, and apply a distance metric to compare the model with byte-frequency distribution of the File in question. Mahalanobis distance is the most popular distance metric. In this paper, we propose 1) the cosine similarity as a better metric than Mahalanobis distance in terms of classification accuracy, smaller model size, and faster detection rate, and 2) a new type-identification scheme that applies recursive steps to identify types of Files. We compare the cosine similarity to Mahalanobis distance using Wei-Hen Li et al.'s single and multi-centroid modeling techniques, which showed 4.8% and 13.10% improvement in classification accuracy (single and multi-centroid respectively). The cosine similarity showed reduction of the model size by about 90% and improvement in the detection speed by 11%. Our proposed type identification scheme showed 37.78% and 31.47% improvement over Wei-Hen Li's single and multi-centroid modeling techniques respectively.

Kyungsuk Lhee - One of the best experts on this subject based on the ideXlab platform.

  • content based File type identification using cosine similarity and a divide and conquer approach
    Iete Technical Review, 2010
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    AbstractIdentifying the File type (TXT, EXE, JPEG, etc.) is important for computer security applications such as computer forensics, steganalysis, and antivirus programs. The common approach for this is to use File Extensions, magic numbers, or other header information. However, these are susceptible to tampering or corruption; for instance, the File Extension can be easily spoofed and the magic numbers can be obfuscated. A more reliable approach may be to analyze the File content instead of using only the tip of the information (metadata). This paper proposes two methods based on the File content. First, we use the cosine distance as a similarity metric when comparing the File content rather than the Mahalanobis distance that is popular and has been used by the other related approaches. The cosine similarity (unlike the Mahalanobis distance) retains the classification accuracy on a small number of highly frequent byte patterns which leads to a smaller model size and faster detection rate. Second, we deco...

  • on improving the accuracy and performance of content based File type identification
    Australasian Conference on Information Security and Privacy, 2009
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    Types of Files (text, executables, Jpeg images, etc.) can be identified through File Extension, magic number, or other header information in the File. However, they are easy to be tampered or corrupted so cannot be trusted as secure ways to identify File types.In the presence of adversaries, analyzing the File content may be a more reliable way to identify File types, but existing approaches of File type analysis still need to be improved in terms of accuracy and speed. Most of them use byte-frequency distribution as a feature in building a representative model of a File type, and apply a distance metric to compare the model with byte-frequency distribution of the File in question. Mahalanobis distance is the most popular distance metric. In this paper, we propose 1) the cosine similarity as a better metric than Mahalanobis distance in terms of classification accuracy, smaller model size, and faster detection rate, and 2) a new type-identification scheme that applies recursive steps to identify types of Files. We compare the cosine similarity to Mahalanobis distance using Wei-Hen Li et al.'s single and multi-centroid modeling techniques, which showed 4.8% and 13.10% improvement in classification accuracy (single and multi-centroid respectively). The cosine similarity showed reduction of the model size by about 90% and improvement in the detection speed by 11%. Our proposed type identification scheme showed 37.78% and 31.47% improvement over Wei-Hen Li's single and multi-centroid modeling techniques respectively.

Hyunjung Shin - One of the best experts on this subject based on the ideXlab platform.

  • content based File type identification using cosine similarity and a divide and conquer approach
    Iete Technical Review, 2010
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    AbstractIdentifying the File type (TXT, EXE, JPEG, etc.) is important for computer security applications such as computer forensics, steganalysis, and antivirus programs. The common approach for this is to use File Extensions, magic numbers, or other header information. However, these are susceptible to tampering or corruption; for instance, the File Extension can be easily spoofed and the magic numbers can be obfuscated. A more reliable approach may be to analyze the File content instead of using only the tip of the information (metadata). This paper proposes two methods based on the File content. First, we use the cosine distance as a similarity metric when comparing the File content rather than the Mahalanobis distance that is popular and has been used by the other related approaches. The cosine similarity (unlike the Mahalanobis distance) retains the classification accuracy on a small number of highly frequent byte patterns which leads to a smaller model size and faster detection rate. Second, we deco...

  • on improving the accuracy and performance of content based File type identification
    Australasian Conference on Information Security and Privacy, 2009
    Co-Authors: Irfan Ahmed, Kyungsuk Lhee, Hyunjung Shin, Manpyo Hong
    Abstract:

    Types of Files (text, executables, Jpeg images, etc.) can be identified through File Extension, magic number, or other header information in the File. However, they are easy to be tampered or corrupted so cannot be trusted as secure ways to identify File types.In the presence of adversaries, analyzing the File content may be a more reliable way to identify File types, but existing approaches of File type analysis still need to be improved in terms of accuracy and speed. Most of them use byte-frequency distribution as a feature in building a representative model of a File type, and apply a distance metric to compare the model with byte-frequency distribution of the File in question. Mahalanobis distance is the most popular distance metric. In this paper, we propose 1) the cosine similarity as a better metric than Mahalanobis distance in terms of classification accuracy, smaller model size, and faster detection rate, and 2) a new type-identification scheme that applies recursive steps to identify types of Files. We compare the cosine similarity to Mahalanobis distance using Wei-Hen Li et al.'s single and multi-centroid modeling techniques, which showed 4.8% and 13.10% improvement in classification accuracy (single and multi-centroid respectively). The cosine similarity showed reduction of the model size by about 90% and improvement in the detection speed by 11%. Our proposed type identification scheme showed 37.78% and 31.47% improvement over Wei-Hen Li's single and multi-centroid modeling techniques respectively.

Syahril Efendi - One of the best experts on this subject based on the ideXlab platform.

  • discrete sine transform analysis discrete cosine transform and discrete fourier transform for introduction to voice register
    International Journal of Research, 2020
    Co-Authors: Muhamad Yustisar, Poltak Sihombing, Syahril Efendi
    Abstract:

    Voice register is a division of human voice area based on sound source, resonant space sensation, shape, color, sound timbre, and high and low tones produced, with the type of voice tested is the female voice register for chest voice, head voice, falsetto and vocalfry. The data used in this study is a set of female voice register audio File recorded with the help of the adobe audition 1.5 application. The audio File Extension used in this study is File.wav. The choice of audio.wav File is due to the default audio format in audio processing on the windows operating system. In this study, the percentage of discrete sine transform (DST) transformations true detection rate ranged from 63% to 79%, the percentage of discrete cosine transform (DCT) transformations true detection rate ranged from 61% to 73% and the percentage of discrete fourier transform (DFT) transformation rates of true detection ranged from 58% to 61%. The average DST transformation is true detection rate of 73%, DCT transformation is an average true detection rate of 66% and the average DFT transformation is true detection rate of 59%. The best transformation method for introducing voice registers through female voices is DST and DFT.