Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

DDoS Attack using SYN Flooding: A Case Study

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer. © 2022 Bharati Vidyapeeth, New Delhi.

A Long Short-Term Memory (LSTM)-Based Distributed Denial of Service (DDoS) Detection and Defense System Design in Public Cloud Network Environment

The fact that cloud systems are under the increasing risks of cyber attacks has made the phenomenon of information security first a need and then a necessity for these systems. Distributed Denial of Service (DDoS) attacks can exploit, disrupt, change, prevent or damage cloud services. Accurate and timely detection and prevention of these attacks are very important in terms of ensuring information security. During the COVID-19 period, the increase in the use of information technologies and especially the internet has made cyber attacks a real concern. Deep learning (DL) has become widely used for the purpose of detecting and preventing cyber attacks to provide information security. In this study, a Long Short-Term Memory (LSTM) based system (LSTM-CLOUD) which was designed for the detection and prevention of DDoS attacks in a public cloud network environment was proposed. The design of the system is based on a signature-based attack detection approach. The LSTM-CLOUD has two modules defined in the study: detection and defense. The function of the first module of the system was determined as detecting the occurrence of DDoS attacks with the LSTM DL model developed in this study with an accuracy rate of 99.83% on the CICDDoS2019 data set. The function of the second module was determined as activating the defense mechanism to protect the cloud systems when attacks are detected. The comparison results showed that our LSTM model had a performance as good as those in the previous studies conducted with different DL algorithms on the same and different datasets. The results obtained show the effectiveness of the LSTM model developed in this study in detecting the occurrence of attacks.

Top Reported Data Security Risks in the Age of COVID-19

Data has been collected and stored for thousands of years. Securing data during the digital age has remained difficult. Research shows that in 2018 there was over 33 zettabytes of data, which is approximately an equivalent to 129 billion 256GB mobile devices of data. Risk management in recent years has made attempts at balancing data security risks with organizational business and budgetary requirements. This research examines high probability data security threats and mitigations. It then reports on the threats in connection with the top United States healthcare data breaches reported during the COVID outbreak to the Health and Human Services (HHS) between June 11, 2020 and June 11, 2021. The data analysis shows that there were nine breaches of over a million affected individuals reported to HHS affecting 15,936,679 individuals in total. Five-million individuals is approximately larger than the populations of Los Angeles, New York, and Chicago combined. We connect the common security risks with the reports of these incidents to gain insights into common network security concerns and inform future network architectures and risk mitigations. © 2021 IEEE.