An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms
Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of t...
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| Autor*in: |
Dabas, Namita [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 48(2022), 8 vom: 18. Okt., Seite 9749-9767 |
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| Übergeordnetes Werk: |
volume:48 ; year:2022 ; number:8 ; day:18 ; month:10 ; pages:9749-9767 |
| Links: |
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| DOI / URN: |
10.1007/s13369-022-07309-z |
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SPR052556255 |
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| 520 | |a Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. | ||
| 650 | 4 | |a Malware detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a API calls |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a API sequences |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Frequent patterns |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ahlawat, Prachi |0 (orcid)0000-0002-6241-7659 |4 aut | |
| 700 | 1 | |a Sharma, Prabha |4 aut | |
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| 773 | 1 | 8 | |g volume:48 |g year:2022 |g number:8 |g day:18 |g month:10 |g pages:9749-9767 |
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10.1007/s13369-022-07309-z doi (DE-627)SPR052556255 (SPR)s13369-022-07309-z-e DE-627 ger DE-627 rakwb eng Dabas, Namita verfasserin aut An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. Malware detection (dpeaa)DE-He213 API calls (dpeaa)DE-He213 API sequences (dpeaa)DE-He213 Frequent patterns (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ahlawat, Prachi (orcid)0000-0002-6241-7659 aut Sharma, Prabha aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 8 vom: 18. Okt., Seite 9749-9767 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:8 day:18 month:10 pages:9749-9767 https://dx.doi.org/10.1007/s13369-022-07309-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 8 18 10 9749-9767 |
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10.1007/s13369-022-07309-z doi (DE-627)SPR052556255 (SPR)s13369-022-07309-z-e DE-627 ger DE-627 rakwb eng Dabas, Namita verfasserin aut An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. Malware detection (dpeaa)DE-He213 API calls (dpeaa)DE-He213 API sequences (dpeaa)DE-He213 Frequent patterns (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ahlawat, Prachi (orcid)0000-0002-6241-7659 aut Sharma, Prabha aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 8 vom: 18. Okt., Seite 9749-9767 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:8 day:18 month:10 pages:9749-9767 https://dx.doi.org/10.1007/s13369-022-07309-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 8 18 10 9749-9767 |
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10.1007/s13369-022-07309-z doi (DE-627)SPR052556255 (SPR)s13369-022-07309-z-e DE-627 ger DE-627 rakwb eng Dabas, Namita verfasserin aut An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. Malware detection (dpeaa)DE-He213 API calls (dpeaa)DE-He213 API sequences (dpeaa)DE-He213 Frequent patterns (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ahlawat, Prachi (orcid)0000-0002-6241-7659 aut Sharma, Prabha aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 8 vom: 18. Okt., Seite 9749-9767 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:8 day:18 month:10 pages:9749-9767 https://dx.doi.org/10.1007/s13369-022-07309-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 8 18 10 9749-9767 |
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10.1007/s13369-022-07309-z doi (DE-627)SPR052556255 (SPR)s13369-022-07309-z-e DE-627 ger DE-627 rakwb eng Dabas, Namita verfasserin aut An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. Malware detection (dpeaa)DE-He213 API calls (dpeaa)DE-He213 API sequences (dpeaa)DE-He213 Frequent patterns (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ahlawat, Prachi (orcid)0000-0002-6241-7659 aut Sharma, Prabha aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 8 vom: 18. Okt., Seite 9749-9767 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:8 day:18 month:10 pages:9749-9767 https://dx.doi.org/10.1007/s13369-022-07309-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 8 18 10 9749-9767 |
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10.1007/s13369-022-07309-z doi (DE-627)SPR052556255 (SPR)s13369-022-07309-z-e DE-627 ger DE-627 rakwb eng Dabas, Namita verfasserin aut An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. Malware detection (dpeaa)DE-He213 API calls (dpeaa)DE-He213 API sequences (dpeaa)DE-He213 Frequent patterns (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ahlawat, Prachi (orcid)0000-0002-6241-7659 aut Sharma, Prabha aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 8 vom: 18. Okt., Seite 9749-9767 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:8 day:18 month:10 pages:9749-9767 https://dx.doi.org/10.1007/s13369-022-07309-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 8 18 10 9749-9767 |
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A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. 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An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms |
| abstract |
Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract With the advent of internet-based technology, there has been a surge in internet-enabled devices. These devices generate massive volumes of meaningful information to accomplish several tasks. Conversely, cyber-criminals leverage this information to perform cyber-attacks. Malware is one of the most prevalent attacks in the cyber threat landscape to fulfil malicious intents of cyber-criminals. Thus, it becomes imperative to detect and prevent these malware attacks precisely to minimize the damage. A number of researchers have proved that API calls can comprehend malware behaviour accurately and can be utilized with machine learning algorithms to effectively detect malware. Therefore, this paper proposes a novel malware detection method for Windows platform based on API calls, feature selection, and machine learning algorithms. It extracts API calls information in three forms: API calls usage, API calls frequency, and API calls sequences to create three feature sets. These feature sets are enriched using TF-IDF technique and combined to create a more extensive and robust feature set, API integrated feature set. A series of experiments were conducted and results showed that API integrated feature set outperformed other feature sets by attaining 99.6% and higher accuracy for all machine learning algorithms. To address the high-dimensionality concern of API integrated feature set, this work applied several feature selection techniques and results showed that we are able to achieve 99.6–99.9% accuracy with only 9% features of API integrated feature set using hybrid feature selection and machine learning algorithms. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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An Effective Malware Detection Method Using Hybrid Feature Selection and Machine Learning Algorithms |
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Ahlawat, Prachi Sharma, Prabha |
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10.1007/s13369-022-07309-z |
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| score |
7.3999653 |