Venue & metadata

• Journal/Proceedings: Advances in Intelligent Systems and Computing
• Volume: 1150 AISC
• Pages: 890 – 901
• Note: Cited by: 8
• Author keywords: Cloud; Deep learning; Energy attacks; Generative Adversarial Networks; Microservices; Security; Serverless; Service degradation; Software services

Abstract

The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications. © 2020, Springer Nature Switzerland AG.

Keywords

Budget control GS Cost benefit analysis GS Cost effectiveness GS Intrusion detection GS Statistical tests GS Additional costs GS Cloud infrastructures GS Cost effective GS Online resources GS Remote resources GS Resource usage GS Software systems GS Third parties GS Cost estimating GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

{"key"=>"Campanile2020890", "type"=>"Conference paper", "bibtex"=>"@article{Campanile2020890,\n  author = {Campanile, Lelio and Iacono, Mauro and Martinelli, Fabio and Marulli, Fiammetta and Mastroianni, Michele and Mercaldo, Francesco and Santone, Antonella},\n  title = {Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources},\n  year = {2020},\n  journal = {Advances in Intelligent Systems and Computing},\n  volume = {1150 AISC},\n  pages = {890 – 901},\n  doi = {10.1007/978-3-030-44038-1_81},\n  type = {Conference paper}\n}\n", "author"=>"Campanile, Lelio and Iacono, Mauro and Martinelli, Fabio and Marulli, Fiammetta and Mastroianni, Michele and Mercaldo, Francesco and Santone, Antonella", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Mauro", "last"=>"Iacono", "prefix"=>"", "suffix"=>""}, {"first"=>"Fabio", "last"=>"Martinelli", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Michele", "last"=>"Mastroianni", "prefix"=>"", "suffix"=>""}, {"first"=>"Francesco", "last"=>"Mercaldo", "prefix"=>"", "suffix"=>""}, {"first"=>"Antonella", "last"=>"Santone", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Mauro", "author_1_last"=>"Iacono", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Fabio", "author_2_last"=>"Martinelli", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Fiammetta", "author_3_last"=>"Marulli", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Michele", "author_4_last"=>"Mastroianni", "author_4_prefix"=>"", "author_4_suffix"=>"", "author_5_first"=>"Francesco", "author_5_last"=>"Mercaldo", "author_5_prefix"=>"", "author_5_suffix"=>"", "author_6_first"=>"Antonella", "author_6_last"=>"Santone", "author_6_prefix"=>"", "author_6_suffix"=>"", "title"=>"Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources", "year"=>"2020", "journal"=>"Advances in Intelligent Systems and Computing", "volume"=>"1150 AISC", "pages"=>"890 – 901", "doi"=>"10.1007/978-3-030-44038-1_81", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083965494&doi=10.1007%2f978-3-030-44038-1_81&partnerID=40&md5=a31bc02a9b628120dbddc4b0286107e4", "abstract"=>"The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications. © 2020, Springer Nature Switzerland AG.", "author_keywords"=>"Cloud; Deep learning; Energy attacks; Generative Adversarial Networks; Microservices; Security; Serverless; Service degradation; Software services", "keywords"=>"Budget control; Cost benefit analysis; Cost effectiveness; Intrusion detection; Statistical tests; Additional costs; Cloud infrastructures; Cost effective; Online resources; Remote resources; Resource usage; Software systems; Third parties; Cost estimating", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 8"}
    

Suggested citation

Campanile, L., Iacono, M., Martinelli, F., Marulli, F., Mastroianni, M., Mercaldo, F., & Santone, A. (2020). Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources [Conference paper]. Advances in Intelligent Systems and Computing, 1150 AISC, 890–901. https://doi.org/10.1007/978-3-030-44038-1_81