{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:37:30Z","timestamp":1757702250385,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031160134"},{"type":"electronic","value":"9783031160141"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16014-1_47","type":"book-chapter","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T23:03:09Z","timestamp":1663714989000},"page":"598-610","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Towards Optimizing Malware Detection: An Approach Based on\u00a0Generative Adversarial Networks and\u00a0Transformers"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0274-8808","authenticated-orcid":false,"given":"Ayyub","family":"Alzahem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8236-8746","authenticated-orcid":false,"given":"Maha","family":"Driss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-7423","authenticated-orcid":false,"given":"Anis","family":"Koubaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4639-516X","authenticated-orcid":false,"given":"Iman","family":"Almomani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"47_CR1","doi-asserted-by":"crossref","unstructured":"A Ghaleb, F., et al.: Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for vanet. Electronics 9(9), 1411 (2020)","DOI":"10.3390\/electronics9091411"},{"issue":"1","key":"47_CR2","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.32604\/cmc.2022.018631","volume":"70","author":"I Almomani","year":"2021","unstructured":"Almomani, I., AlKhayer, A., El-Shafai, W.: Novel ransomware hiding model using HEVC steganography approach. CMC-Comput. Mater. Continua 70(1), 1209\u20131228 (2021)","journal-title":"CMC-Comput. Mater. Continua"},{"issue":"6","key":"47_CR3","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.3390\/s22062281","volume":"22","author":"I Almomani","year":"2022","unstructured":"Almomani, I., Alkhayer, A., El-Shafai, W.: A crypto-steganography approach for hiding ransomware within hevc streams in android iot devices. Sensors 22(6), 2281 (2022)","journal-title":"Sensors"},{"key":"47_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101663","volume":"89","author":"MK Alzaylaee","year":"2020","unstructured":"Alzaylaee, M.K., Yerima, S.Y., Sezer, S.: Dl-droid: deep learning based android malware detection using real devices. Comput. Secur. 89, 101663 (2020)","journal-title":"Comput. Secur."},{"key":"47_CR5","unstructured":"Arad Hudson, D., Zitnick, L.: Compositional transformers for scene generation. Advances in Neural Information Processing Systems 34 (2021)"},{"key":"47_CR6","doi-asserted-by":"publisher","first-page":"6249","DOI":"10.1109\/ACCESS.2019.2963724","volume":"8","author":"\u00d6A Aslan","year":"2020","unstructured":"Aslan, \u00d6.A., Samet, R.: A comprehensive review on malware detection approaches. IEEE Access 8, 6249\u20136271 (2020)","journal-title":"IEEE Access"},{"key":"47_CR7","unstructured":"Baig, M., Zavarsky, P., Ruhl, R., Lindskog, D.: The study of evasion of packed PE from static detection. In: World Congress on Internet Security (WorldCIS-2012), pp. 99\u2013104. IEEE (2012)"},{"issue":"9","key":"47_CR8","doi-asserted-by":"publisher","first-page":"8699","DOI":"10.1007\/s12652-020-02630-7","volume":"12","author":"I Bello","year":"2021","unstructured":"Bello, I., et al.: Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives. J. Ambient. Intell. Humaniz. Comput. 12(9), 8699\u20138717 (2021)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"11","key":"47_CR9","doi-asserted-by":"publisher","first-page":"4302","DOI":"10.3390\/s22114302","volume":"22","author":"S Ben Atitallah","year":"2022","unstructured":"Ben Atitallah, S., Driss, M., Almomani, I.: A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks. Sensors 22(11), 4302 (2022)","journal-title":"Sensors"},{"issue":"1","key":"47_CR10","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1002\/ima.22654","volume":"32","author":"S Ben Atitallah","year":"2022","unstructured":"Ben Atitallah, S., Driss, M., Boulila, W., Ben Ghezala, H.: Randomly initialized convolutional neural network for the recognition of covid-19 using x-ray images. Int. J. Imaging Syst. Technol. 32(1), 55\u201373 (2022)","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"2","key":"47_CR11","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1002\/ima.22653","volume":"32","author":"S Ben Atitallah","year":"2022","unstructured":"Ben Atitallah, S., Driss, M., Boulila, W., Koubaa, A., Ben Ghezala, H.: Fusion of convolutional neural networks based on dempster-shafer theory for automatic pneumonia detection from chest x-ray images. Int. J. Imaging Syst. Technol. 32(2), 658\u2013672 (2022)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"47_CR12","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.285","volume":"6","author":"FO Catak","year":"2020","unstructured":"Catak, F.O., Yaz\u0131, A.F., Elezaj, O., Ahmed, J.: Deep learning based sequential model for malware analysis using windows exe API calls. PeerJ Comput. Sci. 6, e285 (2020)","journal-title":"PeerJ Comput. Sci."},{"key":"47_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cosrev.2019.01.002","volume":"32","author":"SS Chakkaravarthy","year":"2019","unstructured":"Chakkaravarthy, S.S., Sangeetha, D., Vaidehi, V.: A survey on malware analysis and mitigation techniques. Comput. Sci. Rev. 32, 1\u201323 (2019)","journal-title":"Comput. Sci. Rev."},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299\u201312310 (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"issue":"4","key":"47_CR15","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/electronics10040485","volume":"10","author":"R Dama\u0161evi\u010dius","year":"2021","unstructured":"Dama\u0161evi\u010dius, R., Ven\u010dkauskas, A., Toldinas, J., Grigali\u016bnas, \u0160: Ensemble-based classification using neural networks and machine learning models for windows PE malware detection. Electronics 10(4), 485 (2021)","journal-title":"Electronics"},{"issue":"2","key":"47_CR16","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s10723-020-09510-6","volume":"18","author":"H Darabian","year":"2020","unstructured":"Darabian, H., et al.: Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. J. Grid Comput. 18(2), 293\u2013303 (2020)","journal-title":"J. Grid Comput."},{"key":"47_CR17","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1016\/j.procs.2021.09.007","volume":"192","author":"M Driss","year":"2021","unstructured":"Driss, M., Hasan, D., Boulila, W., Ahmad, J.: Microservices in IoT security: current solutions, research challenges, and future directions. Procedia Comput. Sci. 192, 2385\u20132395 (2021)","journal-title":"Procedia Comput. Sci."},{"key":"47_CR18","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-981-16-6597-4_7","volume-title":"Cyber Security: Issues and Current Trends","author":"N Dutta","year":"2022","unstructured":"Dutta, N., Jadav, N., Tanwar, S., Sarma, H.K.D., Pricop, E.: Introduction to malware analysis. In: Cyber Security: Issues and Current Trends. SCI, vol. 995, pp. 129\u2013141. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-6597-4_7"},{"issue":"2","key":"47_CR19","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3390\/iot1020030","volume":"1","author":"DW Fernando","year":"2020","unstructured":"Fernando, D.W., Komninos, N., Chen, T.: A study on the evolution of ransomware detection using machine learning and deep learning techniques. IoT 1(2), 551\u2013604 (2020)","journal-title":"IoT"},{"issue":"23","key":"47_CR20","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/rs11232852","volume":"11","author":"FA Ghaleb","year":"2019","unstructured":"Ghaleb, F.A., Maarof, M.A., Zainal, A., Al-rimy, B.A.S., Alsaeedi, A., Boulila, W.: Ensemble-based hybrid context-aware misbehavior detection model for vehicular ad hoc network. Remote Sens. 11(23), 2852 (2019)","journal-title":"Remote Sens."},{"key":"47_CR21","unstructured":"Hudson, D.A., Zitnick, L.: Generative adversarial transformers. In: International Conference on Machine Learning, pp. 4487\u20134499. PMLR (2021)"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Melhim, L.K.B., Jemmali, M., Alharbi, M.: Network monitoring enhancement based on mathematical modeling. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/CAIS.2019.8769583"},{"issue":"4","key":"47_CR23","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1504\/IJSNET.2020.109193","volume":"33","author":"LKB Melhim","year":"2020","unstructured":"Melhim, L.K.B., Jemmali, M., AsSadhan, B., Alquhayz, H.: Network traffic reduction and representation. Int. J. Sensor Networks 33(4), 239\u2013249 (2020)","journal-title":"Int. J. Sensor Networks"},{"key":"47_CR24","unstructured":"Oliveira, A.: Malware analysis datasets: Raw pe as image. IEEE dataport (2019)"},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Roseline, S.A., Geetha, S.: A comprehensive survey of tools and techniques mitigating computer and mobile malware attacks. Comput. Electr. Eng. 92, 107143 (2021)","DOI":"10.1016\/j.compeleceng.2021.107143"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Sarhan, A., Jemmali, M., Ben Hmida, A.: Two routers network architecture and scheduling algorithms under packet category classification constraint. In: The 5th International Conference on Future Networks & Distributed Systems, pp. 119\u2013127 (2021)","DOI":"10.1145\/3508072.3508092"},{"key":"47_CR27","doi-asserted-by":"crossref","unstructured":"Shamsolmoali, P., et al.: Image synthesis with adversarial networks: a comprehensive survey and case studies. Inf. Fusion 72, 126\u2013146 (2021)","DOI":"10.1016\/j.inffus.2021.02.014"},{"key":"47_CR28","doi-asserted-by":"publisher","first-page":"46717","DOI":"10.1109\/ACCESS.2019.2906934","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Venkatraman, S.: Robust intelligent malware detection using deep learning. IEEE Access 7, 46717\u201346738 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"47_CR29","doi-asserted-by":"publisher","first-page":"296","DOI":"10.3390\/sym14020296","volume":"14","author":"F Wang","year":"2022","unstructured":"Wang, F., Chai, G., Li, Q., Wang, C.: An efficient deep unsupervised domain adaptation for unknown malware detection. Symmetry 14(2), 296 (2022)","journal-title":"Symmetry"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Xing, X., Jin, X., Elahi, H., Jiang, H., Wang, G.: A malware detection approach using autoencoder in deep learning. IEEE Access (2022)","DOI":"10.1109\/ACCESS.2022.3155695"},{"key":"47_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, J., Masood, R., Seneviratne, S.: A review of computer vision methods in network security. IEEE Commun. Surv. Tutorials (2021)","DOI":"10.1109\/COMST.2021.3086475"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16014-1_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T12:12:19Z","timestamp":1678363939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16014-1_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031160134","9783031160141"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16014-1_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}