ABSTRACT
With the development of information and communication technologies and their application in the field of the gambling industry, consequently, there is a development and expansion of the electronic form of this type of service, better known as online gambling, which can be observed as a part of Industry 4.0 concept. Significant progress in online gambling has been monitored during the COVID-19 pandemic and numerous lockdowns worldwide. In such conditions, this form of service is growing in popularity, accompanied by a sharp increase in users. This also increases the risk of numerous cyber-attacks, the successful implementation of which can cause several negative consequences for end-users and the service provider. One example of maintaining security is penetration testing, in which an expert is placed in the role of an attacker to find security vulnerabilities within the system. This research aims to establish a straightforward penetration testing process applicable in the online gaming environment. Periodic and high-quality defined penetration testing can timely detect cyber vulnerabilities, mitigate cyber threats and reduce cybersecurity risks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Recently, as a result of the COVID-19 pandemic, the internet service has seen an upsurge in use. As a result, the usage of cloud computing apps, which offer services to end users on a subscription basis, rises in this situation. However, the availability and efficiency of cloud computing resources are impacted by DDoS attacks, which are designed to disrupt the availability and processing power of cloud computing services. Because there is no effective way for detecting or filtering DDoS attacks, they are a dependable weapon for cyber-attackers. Recently, researchers have been experimenting with machine learning (ML) methods in order to create efficient machine learning-based strategies for detecting DDoS assaults. In this context, we propose a technique for detecting DDoS attacks in a cloud computing environment using big data and deep learning algorithms. The proposed technique utilises big data spark technology to analyse a large number of incoming packets and a deep learning machine learning algorithm to filter malicious packets. The KDDCUP99 dataset was used for training and testing, and an accuracy of 99.73% was achieved. © 2021 IEEE.
ABSTRACT
In specific conditions and crisis situations such as the pandemic of coronavirus (SARS-CoV-2), or the COVID-19 disease, e-learning systems became crucial for the smooth performance of teaching and other educational processes. In such scenarios, the availability of e-learning ecosystem elements is further highlighted. An indicator of the importance of securing the availability of such an ecosystem is evident from the DDoS (Distributed Denial of Service) attack on AAI@EduHr as a key authentication service for a number of e-learning users in the Republic of Croatia. In doing so, numerous users (teachers/students/administrators) were prevented from implementing and participating in the planned teaching process. Given that DDoS as an anomaly of network traffic has been identified as one of the key threats to the e-learning ecosystem in crisis scenarios, this research focuses on the overview of methodology for developing a model for proactive detection of DDoS traffic. The challenge in detection is to effectively differentiate the increased traffic intensity and service requests caused by legitimate user activity (flash crowd) from the illegitimate traffic caused by a DDoS attack. The DDoS traffic detection model developed by the following analyzed methodology would serve as a basis for providing further guidelines and recommendations in the form of response to events that may negatively affect the availability of e-learning ecosystem elements such as DDoS attack. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.