Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework Article Swipe
Faiza Babar Khan
,
Muhammad Hanif Durad
,
Asifullah Khan
,
Farrukh Aslam Khan
,
Muhammad Rizwan
,
Aftab Ali
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3358454
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3358454
The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators. Our results showcase enhanced malware detection performance, yielding a 10% performance boost, thus affirming the efficacy of our approach compared to existing models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3358454
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414101.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391216250
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391216250Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3358454Digital Object Identifier
- Title
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Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network FrameworkWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, Farrukh Aslam Khan, Muhammad Rizwan, Aftab AliList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3358454Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414101.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414101.pdfDirect OA link when available
- Concepts
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Computer science, Adversarial system, Malware, Generative adversarial network, Network security, Generative grammar, Artificial intelligence, Computer security, Deep learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 1Per-year citation counts (last 5 years)
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72Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2024.3358454 |
| primary_location.id | doi:10.1109/access.2024.3358454 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10414101.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2024.3358454 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2108038164, https://openalex.org/W4297477879, https://openalex.org/W1544837488, https://openalex.org/W2990166555, https://openalex.org/W2998800431, https://openalex.org/W1996975221, https://openalex.org/W2896537475, https://openalex.org/W4383503498, https://openalex.org/W4387338457, https://openalex.org/W3096831136, https://openalex.org/W2155320991, https://openalex.org/W7017279194, https://openalex.org/W36091977, https://openalex.org/W6678051712, https://openalex.org/W2085807744, https://openalex.org/W2163931946, https://openalex.org/W2981446616, https://openalex.org/W2912755644, https://openalex.org/W3001129838, https://openalex.org/W3013896538, https://openalex.org/W3113816305, https://openalex.org/W3159415473, https://openalex.org/W6605658820, https://openalex.org/W1586252162, https://openalex.org/W2487301225, https://openalex.org/W2766428736, https://openalex.org/W2042957484, https://openalex.org/W1971139551, https://openalex.org/W2011009207, https://openalex.org/W3166610859, https://openalex.org/W2144112223, https://openalex.org/W2482374127, https://openalex.org/W2005662348, https://openalex.org/W3005994722, https://openalex.org/W3000953536, https://openalex.org/W2943383044, https://openalex.org/W2528572867, https://openalex.org/W6765673727, https://openalex.org/W3005941094, https://openalex.org/W6745847742, https://openalex.org/W6748204703, https://openalex.org/W3080490018, https://openalex.org/W4211033763, https://openalex.org/W2998668592, https://openalex.org/W3000918648, https://openalex.org/W6687892458, https://openalex.org/W2960616960, https://openalex.org/W6602761401, https://openalex.org/W2953040712, https://openalex.org/W3108921945, https://openalex.org/W2965893286, https://openalex.org/W4282040484, https://openalex.org/W4281394465, https://openalex.org/W2911883410, https://openalex.org/W4225768045, https://openalex.org/W2247776437, https://openalex.org/W2982355322, https://openalex.org/W3035322448, https://openalex.org/W3212159646, https://openalex.org/W3080551539, https://openalex.org/W2150188172, https://openalex.org/W2042058229, https://openalex.org/W2923724895, https://openalex.org/W2057036604, https://openalex.org/W2775486083, https://openalex.org/W3167041328, https://openalex.org/W2964197269, https://openalex.org/W2121749752, https://openalex.org/W2766108848, https://openalex.org/W2963215343, https://openalex.org/W2201105088, https://openalex.org/W3024566548 |
| referenced_works_count | 72 |
| abstract_inverted_index.a | 86, 145, 159, 194, 199, 225 |
| abstract_inverted_index.To | 119 |
| abstract_inverted_index.We | 182, 204 |
| abstract_inverted_index.an | 126 |
| abstract_inverted_index.as | 215 |
| abstract_inverted_index.be | 173 |
| abstract_inverted_index.by | 35 |
| abstract_inverted_index.do | 67 |
| abstract_inverted_index.in | 190 |
| abstract_inverted_index.is | 3, 32, 51 |
| abstract_inverted_index.of | 29, 38, 47, 60, 79, 101, 106, 112, 197, 233 |
| abstract_inverted_index.to | 16, 23, 43, 53, 72, 85, 97, 166, 175, 192, 237 |
| abstract_inverted_index.we | 123 |
| abstract_inverted_index.Our | 142, 217 |
| abstract_inverted_index.The | 0, 26 |
| abstract_inverted_index.all | 11 |
| abstract_inverted_index.and | 19, 58, 114, 138, 187, 212 |
| abstract_inverted_index.but | 104 |
| abstract_inverted_index.can | 157, 172 |
| abstract_inverted_index.day | 41 |
| abstract_inverted_index.due | 42 |
| abstract_inverted_index.for | 179 |
| abstract_inverted_index.how | 198 |
| abstract_inverted_index.not | 68 |
| abstract_inverted_index.our | 184, 206, 234 |
| abstract_inverted_index.the | 17, 36, 44, 55, 61, 70, 77, 99, 110, 209, 231 |
| abstract_inverted_index.GANs | 165 |
| abstract_inverted_index.Rate | 90 |
| abstract_inverted_index.This | 162 |
| abstract_inverted_index.both | 134 |
| abstract_inverted_index.each | 40 |
| abstract_inverted_index.have | 69, 124 |
| abstract_inverted_index.high | 87 |
| abstract_inverted_index.none | 105 |
| abstract_inverted_index.rise | 84 |
| abstract_inverted_index.that | 8, 132, 171 |
| abstract_inverted_index.them | 107 |
| abstract_inverted_index.this | 121 |
| abstract_inverted_index.thus | 229 |
| abstract_inverted_index.used | 174 |
| abstract_inverted_index.were | 94 |
| abstract_inverted_index.with | 5 |
| abstract_inverted_index.(AMS) | 130 |
| abstract_inverted_index.(GAN) | 149 |
| abstract_inverted_index.False | 88 |
| abstract_inverted_index.based | 150 |
| abstract_inverted_index.being | 33 |
| abstract_inverted_index.cyber | 1, 30 |
| abstract_inverted_index.data. | 25 |
| abstract_inverted_index.files | 170 |
| abstract_inverted_index.gives | 83 |
| abstract_inverted_index.issue | 100 |
| abstract_inverted_index.novel | 74 |
| abstract_inverted_index.realm | 2 |
| abstract_inverted_index.their | 80 |
| abstract_inverted_index.there | 93 |
| abstract_inverted_index.train | 176 |
| abstract_inverted_index.user, | 18 |
| abstract_inverted_index.users | 31 |
| abstract_inverted_index.using | 208 |
| abstract_inverted_index.which | 82, 156 |
| abstract_inverted_index.(FNR). | 91 |
| abstract_inverted_index.System | 129 |
| abstract_inverted_index.benign | 169 |
| abstract_inverted_index.better | 195 |
| abstract_inverted_index.boost, | 228 |
| abstract_inverted_index.causes | 21 |
| abstract_inverted_index.damage | 22 |
| abstract_inverted_index.detail | 191 |
| abstract_inverted_index.detect | 73 |
| abstract_inverted_index.issue, | 122 |
| abstract_inverted_index.models | 214 |
| abstract_inverted_index.system | 189, 202 |
| abstract_inverted_index.works. | 203 |
| abstract_inverted_index.Malware | 50, 151 |
| abstract_inverted_index.Network | 148 |
| abstract_inverted_index.address | 98, 120 |
| abstract_inverted_index.current | 27 |
| abstract_inverted_index.dataset | 211 |
| abstract_inverted_index.defense | 12 |
| abstract_inverted_index.dynamic | 6 |
| abstract_inverted_index.employs | 144 |
| abstract_inverted_index.machine | 115 |
| abstract_inverted_index.malware | 7, 39, 64, 75, 102, 160, 200, 221 |
| abstract_inverted_index.models. | 239 |
| abstract_inverted_index.provide | 193 |
| abstract_inverted_index.results | 218 |
| abstract_inverted_index.system. | 62 |
| abstract_inverted_index.systems | 66 |
| abstract_inverted_index.without | 76 |
| abstract_inverted_index.(MCOGAN) | 154 |
| abstract_inverted_index.AI-based | 139 |
| abstract_inverted_index.Figshare | 210 |
| abstract_inverted_index.Internet | 48 |
| abstract_inverted_index.Negative | 89 |
| abstract_inverted_index.approach | 143, 207, 235 |
| abstract_inverted_index.attempts | 96 |
| abstract_inverted_index.combined | 109 |
| abstract_inverted_index.compared | 236 |
| abstract_inverted_index.covertly | 20 |
| abstract_inverted_index.describe | 183 |
| abstract_inverted_index.efficacy | 232 |
| abstract_inverted_index.engines. | 118 |
| abstract_inverted_index.enhanced | 220 |
| abstract_inverted_index.evaluate | 205 |
| abstract_inverted_index.existing | 238 |
| abstract_inverted_index.external | 177 |
| abstract_inverted_index.generate | 167 |
| abstract_inverted_index.modules. | 141 |
| abstract_inverted_index.numerous | 95 |
| abstract_inverted_index.operates | 14 |
| abstract_inverted_index.optimize | 158 |
| abstract_inverted_index.privacy, | 57 |
| abstract_inverted_index.promptly | 9 |
| abstract_inverted_index.proposed | 185 |
| abstract_inverted_index.showcase | 219 |
| abstract_inverted_index.utilizes | 164 |
| abstract_inverted_index.yielding | 224 |
| abstract_inverted_index.Optimizer | 153 |
| abstract_inverted_index.affirming | 230 |
| abstract_inverted_index.detection | 65, 117, 137, 140, 201, 222 |
| abstract_inverted_index.developed | 125 |
| abstract_inverted_index.dispersed | 52 |
| abstract_inverted_index.framework | 163, 186 |
| abstract_inverted_index.integrity | 59 |
| abstract_inverted_index.pervasive | 45 |
| abstract_inverted_index.potential | 71 |
| abstract_inverted_index.purposes. | 181 |
| abstract_inverted_index.security, | 56 |
| abstract_inverted_index.sensitive | 24 |
| abstract_inverted_index.10% | 226 |
| abstract_inverted_index.Classifier | 152 |
| abstract_inverted_index.Generative | 146 |
| abstract_inverted_index.detection, | 103 |
| abstract_inverted_index.fabricated | 168 |
| abstract_inverted_index.framework, | 155 |
| abstract_inverted_index.generation | 28 |
| abstract_inverted_index.infiltrate | 54 |
| abstract_inverted_index.integrated | 127 |
| abstract_inverted_index.penetrates | 10 |
| abstract_inverted_index.victimized | 34 |
| abstract_inverted_index.Adversarial | 147 |
| abstract_inverted_index.Previously, | 92 |
| abstract_inverted_index.classifier. | 161 |
| abstract_inverted_index.effectively | 108 |
| abstract_inverted_index.mechanisms, | 13 |
| abstract_inverted_index.overwhelmed | 4 |
| abstract_inverted_index.performance | 227 |
| abstract_inverted_index.progression | 46 |
| abstract_inverted_index.signatures, | 81 |
| abstract_inverted_index.Anti-Malware | 128 |
| abstract_inverted_index.Conventional | 63 |
| abstract_inverted_index.anti-malware | 188 |
| abstract_inverted_index.architecture | 131 |
| abstract_inverted_index.capabilities | 111 |
| abstract_inverted_index.conventional | 135 |
| abstract_inverted_index.incorporates | 133 |
| abstract_inverted_index.optimization | 180 |
| abstract_inverted_index.performance, | 223 |
| abstract_inverted_index.accessibility | 78 |
| abstract_inverted_index.connectivity. | 49 |
| abstract_inverted_index.interpolation | 37 |
| abstract_inverted_index.unapprehended | 15 |
| abstract_inverted_index.understanding | 196 |
| abstract_inverted_index.discriminators | 178 |
| abstract_inverted_index.learning-based | 116 |
| abstract_inverted_index.discriminators. | 216 |
| abstract_inverted_index.signature-based | 113, 136 |
| abstract_inverted_index.state-of-the-art | 213 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.95619889 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |