Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

RISK ASSESSMENT METHOD OF CLOUD ENVIRONMENT

Cloud technology usage in nowadays companies constantly grows every year. Moreover, the COVID-19 situation caused even a higher acceleration of cloud adoption. A higher portion of deployed cloud services, however, means also a higher number of exploitable attack vectors. For that reason, risk assessment of the cloud environment plays a significant role for the companies. The target of this paper is to present a risk assessment method specialized in the cloud environment that supports companies with the identification and assessments of the cloud risks. The method itself is based on ISO/IEC 27005 standard and addresses a list of predefined cloud risks. Besides, the paper also presents the risk score calculation definition. The risk assessment method is then applied to an accounting company in a form of a case study. As a result, 24 risks are identified and assessed within the case study where each risk included also exemplary countermeasures. Further, this paper includes a description of the selected cloud risks. © 2022 Slovak Academy of Sciences. All rights reserved.

Secure E-health Framework in Cloud-Based Environment

Since December 2019, the world still fighting to beat coronavirus (COVID-19). However, coronavirus is continuing its spread in many countries and claimed the lives people. It is not easy to differentiate between COVID-19 symptoms and simple flu symptoms, especially at the first stage of the infection. This is the main challenge where we have to run many tests as possible and isolate any suspicious people 14 days at least to make sure that they are not carrying the virus. This will increase the cost and people may lose their jobs. Therefore, the economy has to continue. Companies and organization start running their business using online tools, this will draw different future and employee need to gain special task to continue their work. In order go back to the normal life, we have track the virus and stay away from infected area or people. In this paper, we propose a secure cloud-based health framework to record patients' readings, give initial diagnose to identify infected areas and control the spread of the virus. The proposed framework will be running in a secure environment to protect patient's records. © 2022 IEEE.

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.

Virtual Machine allocation in multiple Data Centers using Throttled Load Balancing to improve the performance in Cloud

Today, Cloud Computing is a distributed system environment. These days the services are available pay as you go model. Cloud users are paying as per their services in the cloud environment. The services available to the Cloud users are Infrastructure as a service, platform as a service, software as a service and security as a service. Nowadays, most users are migrating to cloud platforms. In Covid-19 pandemic situation, most large and small scale organizations operating their business using cloud platforms. On the other end due to industrial automation, the companies switched their operations to a cloud environments. Due to the rapid business migration, the demand for cloud computing increased. With the increase of demand in the cloud, the service providers are satisfied. On the other end, a challenging issue is resource allocation. The best resource allocation strategy will provide quick services to the cloud users and minimum cost to the cloud providers. In this paper, we will discuss, resource allocation procedure, the throttled load balancing algorithm and the results are compared with other resource optimization techniques. © 2022 IEEE.

Secret sharing based watermarking for copy-protection and ownership control of medical Image

Medical data transmission and sharing, especially during this COVID-19 pandemic period, on the open channel have become more important for remote diagnosis and treatment purpose. However, the alteration and unauthorized distribution of image data has become easier, and thus the big issue of copy-protection and ownership conflicts has attracted more attention for healthcare research community. Further, large amount of confidential and personal medical records is often stored on cloud environments. However, outsourcing medical data possibly brings the great security and privacy issue, since the confidential records are shared to the third party. In this paper, a robust X-Ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD). For watermark embedding, the maximum entropy component of X-Ray carrier image is firstly decomposed using NSCT. Then, low and high frequency details of carrier and mark image is obtained using MSVD. Further, conceal the watermark detail through modifying the detail of carrier image via the suitable factor. Finally, Shamir's (k, n) secret sharing algorithm is employed to obtain secure marked carrier image. Objective evaluations on 200 X-Ray images of COVID-19 patients demonstrate that the proposed algorithm has not only an excellent invisibility but a strong robustness against the various attacks. The results also show that our algorithm outperforms the related image watermarking algorithms, since it is also suitable for applications in the multi-cloud. © 2021 IEEE.