Economic Denial Of Sustainability Attacks Research Articles

EfficientNet-deep quantum neural network-based economic denial of sustainability attack detection to enhance network security in cloud

ABSTRACT Cloud computing (CC) is a future revolution in the Information technology (IT) and Communication field. Security and internet connectivity are the common major factors to slow down the proliferation of CC. Recently, a new kind of denial of service (DDoS) attacks, known as Economic Denial of Sustainability (EDoS) attack, has been emerging. Though EDoS attacks are smaller at a moment, it can be expected to develop in nearer prospective in tandem with progression in the cloud usage. Here, EfficientNet-B3-Attn-2 fused Deep Quantum Neural Network (EfficientNet-DQNN) is presented for EDoS detection. Initially, cloud is simulated and thereafter, considered input log file is fed to perform data pre-processing. Z-Score Normalization ;(ZSN) is employed to carry out pre-processing of data. Afterwards, feature fusion (FF) is accomplished based on Deep Neural Network (DNN) with Kulczynski similarity. Then, data augmentation (DA) is executed by oversampling based upon Synthetic Minority Over-sampling Technique (SMOTE). At last, attack detection is conducted utilizing EfficientNet-DQNN. Furthermore, EfficientNet-DQNN is formed by incorporation of EfficientNet-B3-Attn-2 with DQNN. In addition, EfficientNet-DQNN attained 89.8% of F1-score, 90.4% of accuracy, 91.1% of precision and 91.2% of recall using BOT-IOT dataset at K-Fold is 9.

DUAL ACCESS CONTROL FOR CLOUD BASED DATA STORAGE AND SHARING

Due to its effective and affordable administration, cloud-based data storage has recently attracted growing interest from academia and business. As services are delivered via an open network, it is critical for service providers to adopt secure data storage and sharing mechanisms to protect user privacy and the confidentiality of data. The most popular technique for preventing the compromise of sensitive data is encryption. The practical necessity for data management, however, cannot be adequately addressed by just encrypting data. Moreover, a strong access control over download requests should be taken into account to prevent Economic Denial of Sustainability attacks from being conducted to prevent users from using the service. Each of the two dual access control systems is intended for a different planned context.We create a technique for controlling download requests without sacrificing security or effectiveness. There is also a presentation of the systems' experimental and security analyses. Keywords: Cloud-based data sharing, Access Control, Cloud Storage Service, Attribute Based Encryption.

Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments.

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks.

Two-Phase Deep Learning-Based EDoS Detection System

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.

An ADS-PAYG Approach Using Trust Factor Against Economic Denial of Sustainability Attacks in Cloud Storage

An ADS-PAYG Approach Using Trust Factor Against Economic Denial of Sustainability Attacks in Cloud Storage

Cloud based EDoS Attack Prevention System Using Dual Access Control Mechanism

Cloud based data storage is becoming very popular nowadays as one can easily download any document from anytime and anywhere. However documents security is very important in cloud storage as cloud is a third party server which can be accessed by administrators. There are many literatures available to improve document security on cloud and most of the literatures proposed various data encryption techniques. However, simply encrypting data (e.g., via AES) cannot fully address the practical need of data management. Besides, an effective access control over download request also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service. In this project, we consider the dual access control, in the context of cloud-based storage, in the sense that we design a control mechanism over both data access and download request without loss of security and efficiency. Along with dual access control we also focus on document security by using modified AES algorithm.

Accounting and Privacy Preserving of Data Owner in Cloud Storage

People use the support of distributed computing however can’t completely believe the cloud suppliers to have protection and confidential information. To guarantee secrecy, data owners relocate encoded information rather than plain texts. To divide the encoded documents with different clients, Ciphertext-Policy Attribute-based Encryption (CP-ABE) can be utilized. But this cannot become secure against some other assaults. Many other schemes did not gave guarantee that the cloud provider has the power to check whether a downloader can unscramble or not. Consequently, these files are accessible to everybody who is approachable to the cloud storage. An intentionally harmful assailant can download a great many records to start Economic Denial of Sustainability (EDoS) attacks, it will to a great extent expend the cloud asset. The owner will bear all the expenses for the cloud storage but the cloud provider doesn’t provide the whole information about the access or usage. There is no transparency for the owner. We have to solve these concerns. In order to this we are going to propose a solution for securing the encrypted data from EDoS attacks and providing the owner whole usage information about the cloud storage. We are implementing by using the arbitrary access policy of CP-ABE.

Availability and reliability analysis of cloud computing under economic denial of sustainability (EDoS) attack: a semi-Markov approach

Economic denial of sustainability (EDoS) attack is a new type of distributed denial of service (DDoS) attack which targets the economic resources of cloud adopters by exploiting the auto-scaling features of the cloud. EDoS attack has become a significant threat to cloud adopters as it can lead to bankruptcy or withdrawal from cloud services and this in turn entails unavailability of the cloud services. In fact, availability has been one of the most significant issues of cloud computing securities. To address this issue caused by an EDoS attack, we develop a novel state-based availability model of the cloud under the EDoS attack with the help of a semi-Markov process. Based on the proposed model, we determine the steady state availability of the cloud under the EDoS attack. In addition, we derive the mean time to failure (or reliability) of the cloud in the presence of the EDoS attack to characterize the period of time for which the cloud adopter experiences the state of being bankrupt or service unsustainable situation. Moreover, we present a cost management strategy to prevent the cloud adopter from undergoing bankruptcy or experiencing unexpected exorbitant bill by incorporating the cloud shutdown feature into the proposed model. Numerical illustrations are presented which give insights into how availability and reliability of the cloud services are affected by the EDoS attack. The proposed model is also validated via simulation evidences.

The impact and mitigation of ICMP based economic denial of sustainability attack in cloud computing environment using software defined network

High availability in network services is a crucial requirement for quality of experience. Denial of Service (DoS) and Distribute Denial of Service (DDoS) attacks are under contemplation by many researchers across the globe because these attacks directly target services availability. For this reason, cloud providers use the auto-scaling feature in Cloud Computing Environments (CCE), in which cloud resources scale dynamically on demand. DoS/DDoS attacks on CCE, using auto-scaling, do not deny services but cause high resource usage and substantial financial damages that become an Economic Denial of Sustainability (EDOS) attack. One of the DoS/DDoS attacks, resulting EDOS attack is the Internet Control Messaging Protocol (ICMP) flooding attack. In this paper, a novel technique, ICMP detection and mitigation model (EDOS-IDM) is proposed that can detect and mitigate Volumetric and Normal Behavioral ICMP traffic attacks. The results from the proposed technique are compared with the Normal Behavioral ICMP traffic attack because it causes least resource usage among all the mitigation techniques. According to our study, there is no such technique that can handle normal behavioral ICMP traffic attack. The technique is practically tested and evaluated on OpenStack production Cloud Environment test bed. According to the results, the technique is proved to save extra resource consumption and customer's bills in a cloud computing environment.

Secure Cloud Storage of Text and Image Files by Giving Access Control to Users

Cloud computing is extensively used but it’s not completely trustworthy. It can be in terms of protecting or securing the confidential data from malicious attacks as well as from cloud providers. Also once the data is deployed on cloud, the user has no control over it. So rather than deploying the original text file the user encrypts the file and then uploads on the cloud. When this file needs to be shared with other it can be done using Cipher text Policy Attribute Based Encryption (CP-ABE). This gives the central access to the owner and secures the data from external attacks. Besides, the cloud provider serves both as the payee of resource consumption fee, lacking the transparency to data owners. So to reduce these factors a specific encryption strategy is been used and the time for encryption is also taken into account. The files are also encrypted by the priority that are given by the user during the file uploading section. A text file of size 1GB with the CP-ABE encryption strategy requires 174 seconds whereas encryption done using AES is 473 seconds and that of RSA is 330 seconds. Hence, this dissertation work proposes methodology to secure or protect encrypted cloud storage from Economic Denial of Sustainability attacks and give an efficient time constraint approach.

Combining Data Owner-Side and Cloud-Side Access Control for Encrypted Cloud Storage

People endorse the great power of cloud computing, but cannot fully trust the cloud providers to host privacy-sensitive data, due to the absence of user-to-cloud controllability. To ensure confidentiality, data owners outsource encrypted data instead of plaintexts. To share the encrypted files with other users, ciphertext-policy attribute-based encryption (CP-ABE) can be utilized to conduct fine-grained and owner-centric access control. But this does not sufficiently become secure against other attacks. Many previous schemes did not grant the cloud provider the capability to verify whether a downloader can decrypt. Therefore, these files should be available to everyone accessible to the cloud storage. A malicious attacker can download thousands of files to launch economic denial of sustainability (EDoS) attacks, which will largely consume the cloud resource. The payer of the cloud service bears the expense. Besides, the cloud provider serves both as the accountant and the payee of resource consumption fee, lacking the transparency to data owners. These concerns should be resolved in real-world public cloud storage. In this paper, we propose a solution to secure encrypted cloud storages from EDoS attacks and provide resource consumption accountability. It uses CP-ABE schemes in a black-box manner and complies with arbitrary access policy of the CP-ABE. We present two protocols for different settings, followed by performance and security analysis.

Attribution of Economic Denial of Sustainability Attacks in Public Clouds

Attribution of Economic Denial of Sustainability Attacks in Public Clouds

Mitigating Economic Denial of Sustainability Attacks to Secure Cloud Computing Environments

In cloud computing environment where the infrastructure is shared by millions of users, attackers have the opportunity to ensure more damage to the compromised resources. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to customers. The Distributed Denial-of-service (DDoS) attack is considered one of the largest threats to the Quality of Service (QoS) of cloud services which is used to deny access for legitimate users of an online service. However, Economic Denial of Sustainability (EDoS) attack is a special breed of DDoS attack that attack exploits auto scaling feature of cloud. The Cloud Service Provider (CSP) scales the architecture automatically to serve those requests for which cloud consumer is charged. A consumer expects a sustainable profit after hosting his application on cloud. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs by increasing the cost and decreasing the profit. Hence, in this paper we propose a novel mitigation system against the EDoS attacks. Our system consists of source Checking, Counting, and Turing Test. The obtained simulation results show that our system works efficiently to mitigate the EDoS attack in cloud environment.

Enhanced Mechanism to Detect and Mitigate Economic Denial of Sustainability (EDoS) Attack in Cloud Computing Environments

Cloud computing (CC) is the next revolution in the Information and Communication Technology arena. CC is often provided as a service comparable to utility services such as electricity, water, and telecommunications. Cloud service providers (CSP) offers tailored CC services which are delivered as subscription-based services, in which customers pay based on the usage. Many organizations and service providers have started shifting from traditional server-cluster infrastructure to cloud-based infrastructure. Nevertheless, security is one of the main factors that inhibit the proliferation of cloud computing. The threat of Distributed Denial of Service (DDoS) attack continues to wreak havoc in these cloud infrastructures. In addition to DDoS attacks, a new form of attack known as Economic Denial of Sustainability (EDoS) attack has emerged in recent years. DDoS attack in conventional computing setup usually disrupts the service, which affects the client reputation, and results in financial loss. In CC environment, service disruption is very rare due to the auto-scalability (Elasticity), capability, and availability of service level agreements (SLA). However, auto scalability utilize more computing resources in event of a DDoS attack, exceeding the economic bounds for service delivery, thereby triggering EDoS for the organization targeted. Although EDoS attacks are small at the moment, it is expected to grow in the near future in tandem with the growth in cloud usage. There are few EDoS detection and mitigation techniques available but they have weaknesses and are not efficient in mitigating EDoS. Hence, an enhanced EDoS mitigation mechanism (EDoS-EMM) has been proposed. The aim of this mechanism is to provide a real-time detection and effective mitigation of EDoS attack.

An Experimental Evaluation of the EDoS-Shield Mitigation Technique for Securing the Cloud

Security of cloud services is of utmost importance for contemporary cloud providers. In addition to the traditional malicious attacks that have targeted cloud datacenters in the past, new and persistent threats have changed the landscape of cyber-attacks in recent times. Economic Denial of Sustainability (EDoS) attacks are one of such variant attack types with serious implications and consequences. Such attacks exploit the scalability and elasticity characteristics of the cloud to enforce unwanted resource allocation with the aim of causing economic losses to the cloud service owner. In this paper, we present an experimental study to evaluate the effectiveness of the popular EDoS-Shield technique which is designed to mitigate EDoS attacks. The effectiveness of EDoS-Shield is studied in terms of the needed VM compute resources, response time, and CPU utilization.