Economic Denial Of Sustainability 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.

A Dual Security Protection Mechanism for Cloud-Based Data Storage and Sharing

Abstract: Cloud-based data storage service has drawn increasing interest from both academic and industry in recent years due to their efficient and low-cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. The most widely used method is encryption to protect sensitive data from being compromised. However, simply encrypting data (e.g., via AES) cannot fully address the practical need for data management. Besides, effective access control over download requests also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service.

SECURE DATA SHARING IN AWS CLOUD ENVIRONMENT

The main aim of this paper is to provide secure data sharing to authenticated users by the data owners by checking authorization. As it is effective and low-cost management, cloud-based data storage services have the interest in both academia and industry in recent years. Service providers must utilize secure data storage and sharing mechanisms to maintain data confidentiality and service user privacy because they deliver service through an open network. Encryption is the most widely used for protecting sensitive data from being hacked. However, simply encrypting data (for example, using AES) is insufficient to meet the actual need for data management. Also, effective access control over download requests must be considered to prevent Economic Denial of Service (EDoS) attacks from preventing users from using the service. In this work, we look at secure cloud-based storage, in the sense that we create a control mechanism that can handle both data access and download requests while holding security and efficiency using an attribute-based encryption algorithm. Keywords: Cloud, Economic Denial of Sustainability (EDOS), Advanced encrypted standard(AES), Denial of Service(DOS).

DoWTS – Denial-of-Wallet Test Simulator: Synthetic data generation for preemptive defence

The intentional targeting of components in a cloud based application, in order to artificially inflate usage bills, is an issue application owners have faced for many years. This has occurred under many guises, such as: Economic Denial of Sustainability (EDoS), Click Fraud and even secondary effects of Denial of Service (DoS) attacks. With the advent of commercial offerings of serverless computing circa 2015, a variant of the EDoS attack has emerged, termed, Denial-of-Wallet (DoW). We describe our development of a simulation tool as safe means to research these attacks as well as to generate datasets for the training of future mitigation systems to combat DoW. We believe that DoW may become increasingly prevalent as applications further utilise services based on a pay-per-invocation cost model. Given that the damage caused is purely financial, such attacks may not be disclosed as application users are not directly effected. As such, we believe that the development of an attack simulator and specific testing of security measures against this niche attack will be able to provide previously unavailable data and insights for the research community. We have developed a prototype DoW simulator that can emulate multiple months worth of API calls in a matter of hours for ease of training data generation. Our aspiration for the future of this work is to provide a system and starting point for research on this form of attack. We present our work on such a system Denial-of-Wallet Test Simulator (DoWTS) - a system that allows for safe testing of theorised DoW attacks against serverless applications via synthetic data generation. We also expand upon prior research on DoW and provide an analysis on the lack of specific safety measures for DoW.

Dual Access Control for Cloud-Based Data Sharing and Storage Security

Due to its effective and affordable management, cloud-based data storage has recently attracted growing interest from both academia and industry. Since services are delivered over 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 actual necessity for data management, however, cannot be fully met by merely encrypting data (for instance, using AES). Additionally, a strong access control over download requests must be taken into account to prevent Economic Denial of Sustainability (EDoS) assaults from being performed to prevent users from using the service. In this essay.In the context of cloud-based storage, we take into account dual access control in the sense that we create a control mechanism over both data access and download requests without sacrificing security and effectiveness. This paper presents the design of two dual access control systems, one for each intended environment. There is also a presentation of the systems' experimental and security analysis. Security Dual Access Control for Data Sharing and Storage in the Cloud

Dual Access Control for Cloud-Based Data Storage and Sharing

Due to how efficient and economical it is for the executives, cloud-based information capacity administration has recently attracted more attention from academia and business. Professional co-ops must use secure information storage and sharing technologies to manage client protection since it provides a variety of assistance in an open company. Encryption is the technique that is most frequently employed to stop the compromise of sensitive data. However, even information that has been properly scrambled is insufficient to fully satiate the device's actual information needs (using AES, for example). To prevent Economic Denial of Sustainability (EDoS) assaults that prevent users from taking use of administration, a robust access control over download demand should also be kept in mind. In this work, we investigate the impact of double access control on cloud-based capacity.

Economic Denial of Sustainability Attacks Mitigation in the Cloud

Cyber security is one of the most attention seeking issues with the increasing advancement of technology specifically when the network availability is threaten by attacks such as Denial of Service attacks (DoS), Distributed DoS attacks (DDoS), and Economic Denial of Sustainability (EDoS). The loss of the availability and accessibility of cloud services have greater impacts than those in the traditional enterprises networks. This paper introduces a new technique to mitigate the impacts of attacks which is called Enhanced DDoS-Mitigation System (Enhanced DDoS-MS) that helps in overcoming the determined security gap. The proposed technique is evaluated experimentally and the result shows that the proposed method adds lower delays as a result of the enhanced security. The paper also suggests some future directions to improve the proposed framework.

Mitigating TCP SYN flooding based EDOS attack in cloud computing environment using binomial distribution in SDN

Cloud Computing provides an auto-scaling feature for dynamic resource utilization to cope with their customers’ requirements and charge as ‘pay-per-use’. Attackers get the benefit of this auto-scaling feature by flooding DDoS attacks on the VMs or instances in the cloud environments. Such DDoS attacks give financial damages to the customers because of massive amount of resource utilization, known as the Economic Denial of Sustainability (EDOS) attack. Transmission Control Protocol (TCP) SYN flooding attack is known to be the most challenging attack resulting EDOS attack. Software Defined Network (SDN) being a cost-efficient solution for cloud service providers, uses the OpenFlow switches and flow tables to make rules for each incoming flow. In SDN, it is feasible to classify an incoming flow as an attack and block forwarding of this flow to the targeted VM. This research work proposes an SDN based fast and computationally cost-efficient statistical anomaly detection model, named EDOS-TCP SYN mitigation model (EDOS-TSM) to mitigate TCP SYN flooding attack from a single user and spoofed IPs. EDOS-TSM uses binomial probability, TTL field value of IP packet header and multi-TCP SYN requests to detect source-based and spoofing based attacks. The proposed model is implemented, and the performance is evaluated on an OpenStack production-based cloud with real-time traffic and attack generation. The attack mitigation results are compared with existing models in the literature. The results show a large number of false negatives in existing models and efficiency of EDOS-TSM is proved in both source based and spoofing 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.

Game theoretic modeling of economic denial of sustainability (EDoS) attack in cloud computing

AbstractIn this paper, we develop a novel game theoretic model of the interactions between an EDoS attacker and the defender based on a signaling game that is a dynamic game of incomplete information. We then derive the best defense strategies for the network defender to respond to the EDoS attacks. That is, we compute the perfect Bayesian Nash Equilibrium (PBE) of the proposed game model such as the pooling PBE, separating PBE and mixed strategy PBE. In the pooling equilibrium, each type of the attacker takes the same action and the attacker's type is not revealed to the defender, whereas in the separating equilibrium, each type of the attacker uses different actions and hence the attacker's type is completely revealed to the defender. On the other hand, in the mixed strategy PBE, both the attacker and the defender randomize their strategies to optimize their payoffs. Numerical illustration is also presented to show the efficacy of the proposed model.

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.

A Profile-Based Novel Framework for Detecting EDoS Attacks in the Cloud Environment

The future of information technology mainly depends upon cloud computing. Hence security in cloud computing is highly essential for the consumers as well as the service providers of the particular cloud environment. There are many security threats are challenging the current cloud environment. One of the important security threat ever in cloud environment is considered to be the Distributed Denial of Service (DDoS) attack. Where cloud is of greater benefit in terms of providing on-demand services, a certain kind of attack named as Economic Denial of Sustainability (EDoS) occurs in pay per use payment model. Due to the occurrence of this attack the consumers are forced to pay additional amount for the services offered. EDoS attacks are similar to that of DDoS attacks Which is classified as-attacks associated with bandwidth consuming, application targeted attacks and the exhaustion of the connection layer. The main objective of the proposed work is to design a profile-based novel framework for maximizing the detection of various types of EDoS attacks. During this process, the proposed framework consisting Feature Classification (FC) algorithm ensures that false positives and negatives along with bandwidth and memory consumption are highly minimized. The proposed algorithm allows only the limited resources for allocation to the available virtual machines which increases the chances of the detecting the attack and preventing the misuse propagation of resources. The accuracy and efficiency of this approach is proven to be higher with lesser computational complexity when compare to the existing approaches.

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.

R-EDoS: Robust Economic Denial of Sustainability Detection in an SDN-Based Cloud Through Stochastic Recurrent Neural Network

Cloud computing is now known as the most cost-effective platform for delivering big data and artificial intelligence services over the Internet to enterprises and cloud consumers. However, despite many recent security developments, many cloud consumers continue to express great concern about using these platforms because they still have significant vulnerabilities. Typically, Economic Denial of Sustainability (EDoS) attacks exploit the pay-as-you-go billing mechanisms used by cloud service providers, so that a cloud customer is forced to pay an extra fee for the additional resources triggered by the attack activities. In our previous work, we already proposed an system to mitigate such EDoS attacks. Overall, this previous work presented an effective system for detecting abnormal events; however, the false-alarm rates still remain relatively high and detection rates are low, because abnormal events could be caused by the cloud customer. Furthermore, our previous work still consumes a large number of computing resources. Therefore, in this paper, we propose an enhanced scheme to detect and mitigate EDoS attacks efficiently and reliably. Our proposed scheme is composed of online and offline phases, implementing a gated recurrent unit, which not only can capture complex temporal dependence relations in the data, but also can reduce the vanishing gradient problems in time series. First, to reflect the normal patterns, our proposed scheme learns accurate representations of multivariate time series. Next, these representations are used to reconstruct input data. Finally, the reconstruction probabilities not only can be used to find anomalies, but also can provide interpretations. The proposed scheme also introduces a self-adjusting threshold to reduce error rates, whereas existing solutions normally use a hard threshold to analyze the anomalies, which causes increasing error rates. Our comprehensive analysis of the results shows outstanding performance compared to other solutions and our previous work.

R²PEDS: A Recoverable and Revocable Privacy-Preserving Edge Data Sharing Scheme

Edge servers (ESs) are utilized to achieve the storage and sharing of IoT data. However, even if ES brings us much benefit, it also leads to many serious privacy leakage issues because users’ data in ESs are out of control. Moreover, ES providers may also disclose user’s private-sensitive data. Hence, in this article, we present a privacy-preserving, recoverable, and revocable edge data sharing scheme. In this scheme, we propose a novel attribute revocation chain based on the blockchain technology to achieve attribute revocation in ciphertext-policy attribute-based encryption (CP-ABE). Meanwhile, a secret sharing scheme (SSS) is introduced to assist the data recovery. Especially, for the situation that a single ES is hijacked, we also propose a corresponding efficient detection mechanism and key updating policy to promise the subsequent security of the whole system. Moreover, this scheme also resists Economic Denial-of-Sustainability (EDoS) attacks which are launched by some malicious users. The analysis shows that the proposed scheme can protect user’s privacy and resist many attacks. Additionally, relevant experimental results demonstrate that our scheme has low computational overhead on the user side.

A proactive defense method for the stealthy EDoS attacks in a cloud environment

SummaryCloud computing technology provides flexibility to Cloud Service Provider (CSP) for providing the cloud resources based on the users' requirements. In on‐demand pricing model, the attackers exploit this feature and cause unwanted scaling‐up of the cloud resources without any intent to pay for them. The associated cost for the unpaid malicious usage burdens the CSP, and over a long period, economic losses occur at the CSP end. Thus, the resources and services offered by the CSP become unsustainable, and the attack is termed as Economic Denial‐of‐Sustainability (EDoS) attack. The existing defense approaches for EDoS attacks are reactive. Thus, the associated attack detection/mitigation cost is high; consequently, the approaches are not suitable for the Small and Medium Enterprises (SMEs). The aim of this paper is to detect and mitigate, internal and external, stealthy EDoS attacks proactively. The attack is detected using average CPU utilization threshold and utility function (in terms of cost for the utilized cloud computing resources) and mitigated using virtual firewalls. Amazon Elastic Compute Cloud (Amazon EC2) is used to evaluate the performance of the proposed approach. The proposed approach accurately detects the EDoS attack and mitigates its effect as well with reduced cost. It is observed that the approach provides competitive response time, victim service downtime, and attack reporting time. Thus, the overall performance is improved.

DNA Cryptography Based User Level Security for Cloud Computing and Applications

This paper proposed a novel cryptography method for enhancing the user level security to avoid malicious user entering into cloud applications. Existing research works have been proposed various cryptographic methods, algorithms and techniques for validating the user for accessing data or operating cloud applications. But still the malicious user activity like sybil, sinkhole, Denial of Service, Distributed Denial of Service, Economic Denial of Sustainability, selective forwarding and so on, is increasing day by day in cloud. This paper taken this problem as a major problem and motivated to provide a better solution which can eliminate the malicious user activity in cloud. To do this, this paper used DNA cryptography method for generating a strong key for user and data encryption – decryption process. User information is converted into human deoxyribonucleic acid form for generating strong key and data encryption. The implementation of the proposed approach is carried out in DOTNET framework and the experimental results are verified. Based on the results the performance is evaluated by comparing with the existing results.

Dual Access Control for Cloud-Based Data Storage and Sharing

Cloud-based data storage service has drawn increasing interests from both academic and industry in the recent years due to its efficient and low cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. To protect sensitive data from being compromised, the most widely used method is encryption. 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 paper, 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. Two dual access control systems are designed in this paper, where each of them is for a distinct designed setting. The security and experimental analysis for the systems are also presented.