A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark Article Swipe
Govind P. Gupta
,
Manish Kulariya
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2016.07.238
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2016.07.238
Due to increase in internet based services, the size of network traffic data has become so large and complex that it is very difficult to process with the traditional data processing tools. Fast and efficient cyber security intrusion detection is a very challenging problem due to big and complex nature of network traffic data. A realistic cyber security intrusion detection system should be able to process large size of network traffic data as fast as possible in order to detect the malicious traffic as early as possible. This paper used Apache Spark, a big data processing tool for processing the large size of network traffic data. In this paper, we have proposed a framework in which first a well-known feature selection algorithm is employed for selecting the most important features and then classification based intrusion detection method is used for fast and efficient detection of intrusion in the massive network traffic. In this work, we have used two well-known feature selection algorithm, namely, correlation based feature selection and Chi-squared feature selection and five well known classification based intrusion detection methods, namely, Logistic regression, Support vector machines, Random forest, Gradient Boosted Decision trees & Naive Bayes. A real time DARPA's KDD'99 data set is used to validate the proposed framework and performance comparison of classification based intrusion detection schemes are evaluated in terms of training time, prediction time, accuracy, sensitivity and specificity.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2016.07.238
- OA Status
- diamond
- Cited By
- 111
- References
- 32
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2507920413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2016.07.238Digital Object Identifier
- Title
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A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache SparkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
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2016-01-01Full publication date if available
- Authors
-
Govind P. Gupta, Manish KulariyaList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2016.07.238Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2016.07.238Direct OA link when available
- Concepts
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Computer science, Feature selection, Intrusion detection system, SPARK (programming language), Data mining, Random forest, Naive Bayes classifier, Support vector machine, Big data, Network security, Machine learning, Artificial intelligence, Computer security, Programming languageTop concepts (fields/topics) attached by OpenAlex
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111Total citation count in OpenAlex
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2025: 3, 2024: 5, 2023: 8, 2022: 22, 2021: 18Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.work, | 153 |
| abstract_inverted_index.Apache | 90 |
| abstract_inverted_index.Bayes. | 194 |
| abstract_inverted_index.KDD'99 | 199 |
| abstract_inverted_index.Random | 186 |
| abstract_inverted_index.Spark, | 91 |
| abstract_inverted_index.become | 14 |
| abstract_inverted_index.detect | 79 |
| abstract_inverted_index.method | 136 |
| abstract_inverted_index.nature | 49 |
| abstract_inverted_index.paper, | 108 |
| abstract_inverted_index.should | 61 |
| abstract_inverted_index.system | 60 |
| abstract_inverted_index.tools. | 31 |
| abstract_inverted_index.vector | 184 |
| abstract_inverted_index.Boosted | 189 |
| abstract_inverted_index.DARPA's | 198 |
| abstract_inverted_index.Support | 183 |
| abstract_inverted_index.complex | 18, 48 |
| abstract_inverted_index.feature | 119, 159, 165, 169 |
| abstract_inverted_index.forest, | 187 |
| abstract_inverted_index.massive | 148 |
| abstract_inverted_index.namely, | 162, 180 |
| abstract_inverted_index.network | 10, 51, 69, 103, 149 |
| abstract_inverted_index.problem | 43 |
| abstract_inverted_index.process | 25, 65 |
| abstract_inverted_index.schemes | 217 |
| abstract_inverted_index.traffic | 11, 52, 70, 82, 104 |
| abstract_inverted_index.Decision | 190 |
| abstract_inverted_index.Gradient | 188 |
| abstract_inverted_index.Logistic | 181 |
| abstract_inverted_index.employed | 123 |
| abstract_inverted_index.features | 129 |
| abstract_inverted_index.increase | 2 |
| abstract_inverted_index.internet | 4 |
| abstract_inverted_index.methods, | 179 |
| abstract_inverted_index.possible | 75 |
| abstract_inverted_index.proposed | 111, 207 |
| abstract_inverted_index.security | 36, 57 |
| abstract_inverted_index.traffic. | 150 |
| abstract_inverted_index.training | 223 |
| abstract_inverted_index.validate | 205 |
| abstract_inverted_index.accuracy, | 227 |
| abstract_inverted_index.algorithm | 121 |
| abstract_inverted_index.detection | 38, 59, 135, 143, 178, 216 |
| abstract_inverted_index.difficult | 23 |
| abstract_inverted_index.efficient | 34, 142 |
| abstract_inverted_index.evaluated | 219 |
| abstract_inverted_index.framework | 113, 208 |
| abstract_inverted_index.important | 128 |
| abstract_inverted_index.intrusion | 37, 58, 134, 145, 177, 215 |
| abstract_inverted_index.machines, | 185 |
| abstract_inverted_index.malicious | 81 |
| abstract_inverted_index.possible. | 86 |
| abstract_inverted_index.realistic | 55 |
| abstract_inverted_index.selecting | 125 |
| abstract_inverted_index.selection | 120, 160, 166, 170 |
| abstract_inverted_index.services, | 6 |
| abstract_inverted_index.algorithm, | 161 |
| abstract_inverted_index.comparison | 211 |
| abstract_inverted_index.prediction | 225 |
| abstract_inverted_index.processing | 30, 95, 98 |
| abstract_inverted_index.well-known | 118, 158 |
| abstract_inverted_index.Chi-squared | 168 |
| abstract_inverted_index.challenging | 42 |
| abstract_inverted_index.correlation | 163 |
| abstract_inverted_index.performance | 210 |
| abstract_inverted_index.regression, | 182 |
| abstract_inverted_index.sensitivity | 228 |
| abstract_inverted_index.traditional | 28 |
| abstract_inverted_index.specificity. | 230 |
| abstract_inverted_index.classification | 132, 175, 213 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5090381393 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I38335241 |
| citation_normalized_percentile.value | 0.98341495 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |