Barik, KousikMisra, SanjayKonar, KarabiFernandez-Sanz, LuisMurat, KoyuncuInformation Systems EngineeringComputer Engineering2024-07-052024-07-05202250883-95141087-654510.1080/08839514.2022.20553992-s2.0-85127243379https://doi.org/10.1080/08839514.2022.2055399https://hdl.handle.net/20.500.14411/1850Koyuncu, Murat/0000-0003-1958-5945; Fernandez-Sanz, Luis/0000-0003-0778-0073; Misra, Sanjay/0000-0002-3556-9331; Barik, Kousik/0000-0001-9296-9561Cyber attacks are increasing rapidly due to advanced digital technologies used by hackers. In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly growing field. Although machine learning techniques worked well in solving large-scale cybersecurity problems, an emerging concept of deep learning (DL) that caught on during this period caused information security specialists to improvise the result. The deep learning techniques analyzed in this study are convolution neural networks, recurrent neural networks, and deep neural networks in the context of cybersecurity.A framework is proposed, and a real-time laboratory setup is performed to capture network packets and examine this captured data using various DL techniques. A comparable interpretation is presented under the DL techniques with essential parameters, particularly accuracy, false alarm rate, precision, and detection rate. The DL techniques experimental output projects improvise the performance of various real-time cybersecurity applications on a real-time dataset. CNN model provides the highest accuracy of 98.64% with a precision of 98% with binary class. The RNN model offers the second-highest accuracy of 97.75%. CNN model provides the highest accuracy of 98.42 with multiclass class. The study shows that DL techniques can be effectively used in cybersecurity applications. Future research areas are being elaborated, including the potential research topics to improve several DL methodologies for cybersecurity applications.eninfo:eu-repo/semantics/openAccess[No Keyword Available]ReviewQ2361WOS:000773163700001