Cloud computing is a progressive technology that offers computing resources as Internet-based services, revolutionized information, and communication technologies.From an economic standpoint, this transformation is beneficial because it allows them to streamline technology infrastructure and capital costs. However, economical denial of service (EDoS) potential is a crucial impediment to cloud computing success. Several improved ways to detect EDoS and distributed denial of service (DDoS) attacks in the cloud have been presented; nevertheless, these approaches still result in a considerable reduction in detection accuracy when employed in a cloud setting. Because selecting relevant features and precise classifiers for attack detection is a challenge.We recommend using an EDoS and DDoS attack identification framework in the cloud based on optimized deep learning techniques for higher detection accuracy. The experimental results reveal True Positive Rate (TPR) varies from 98.9% to 99.8% when using deep belief network with support vector machine as a learning mechanism, while True Negative Rate (TNR) from 99.6% to 99.9%. TPR and TNR were found to have average values of 99.32% and 99.67%, respectively. At 1600 requests/s, the maximum accuracy achieved and the overall accuracy of the proposed strategy was 99.78%. K E Y W O R D Sdeep belief network (DBN), distributed denial of service (DDoS), economical denial of service (EDoS), support vector machine (SVM) INTRODUCTIONCloud computing is currently widely regarded as the most cost-effective platform for delivering big data and artificial intelligence capabilities to businesses and cloud customers through the Internet. As a result, there is no need to invest in an extensive computer system because cloud computing delivers inexpensive and scalable on-demand system requirements. 1 In contrast to the commencement of the cloud computing era, which occurred shortly after the high-performance era; today's cloud computing has evolved into a significant associate for Internet of Things (IoT), big data analytics, and intelligence business solutions, in addition to its traditional fundamental facilities. 2However, in this new technology, security is a significant worry. 3 A denial of service (DoS) attack is a common challenge in cloud security. DoS assaults raise the load on the server and render the system unusable. Because of these security concerns, many clients are hesitant to use it for their business and other uses. 4 As a result, cloud computing's popularity has been hampered by security concerns. The network that interconnects the system in a cloud, for example, must be secure. Without a doubt, sharing your data and executing your software on someone else's hard drive can look to be hazardous, posing significant hazards to an organization's data. 5 Cloud computing allows an attacker attempting to extort money from
The art of influence operations as a subset to information operations as well as personnel that practice influence operations for the military have been doctrinally removed from conducting cyber operations. With the openness that the military has created to social networking as a tool for soldiers, the fact that many of the greatest cyber espionage tactics involve a form of influence operations tactic, often in the form of social engineering, to gain, maintain and exploit networks. Continued ignorance in this subject area will lead to exploitable vulnerabilities as well as reduce the military capability to utilize potential attack vectors. This research presents the reasons behind the separation, and a methodology for getting the two independent operational capabilities to re-integrate, and justifies this as the first step towards a
In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, etc. The anticipated s − LSTM and b − LSTM model intends to ensure Quality of Service (QoS) even in changing network topology. The performance of the proposed b − LSTM and s − LSTM is measured by comparing the significance of the model with various prevailing approaches. Sometimes, the performance is measured with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for measuring the error rate of the model. The prediction of error rate is made with Learning-based Stochastic Gradient Descent (L − SGD). This gradual gradient descent intends to predict the maximal or minimal error through successive iterations. The simulation is performed in a MATLAB 2020a environment, and the model performance is evaluated with diverse approaches. The anticipated model intends to give superior performance in contrast to prevailing approaches.
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