基于虚拟散列安全访问路径VHSAP的云计算路由平台防御DDoS攻击方法

防御分布式拒绝服务DDoS（distributed denial of service）攻击是云计算平台安全保护中的一个关键问题。在研究大规模网络防御DDoS攻击的安全覆盖服务SOS（security overlay service）方法的基础上,揭示了SOS在节点被攻击时的退出机制存在的安全漏洞,根据云计算路由策略改进了一致性散列算法Chord,提出了适用于云计算路由平台三层架构的虚拟散列安全访问路径VHSAP（virtualization hash security access path）,在安全访问路径中引入了心跳机制,利用虚拟机技术实现弹性的虚拟节点,完成在云平台中被攻击节点之间的无缝切换,保证用户对云计算平台的安全访问。针对VHSAP防御DDoS的性能进行了仿真实验,重点研究了在散列安全访问路径HSAP中被攻击节点数和切换时延等参数,并将实验结果与SOS方法进行了比较。实验结果表明在DDoS攻击下,VHSAP具有较高的数据通过率,可以提高云计算平台的安全性。     

Research on low-rate DDoS attack of SDN network in cloud environment

Aiming at the problems of low-rate DDoS attack detection accuracy in cloud SDN network and the lack of unified framework for data plane and control plane low-rate DDoS attack detection and defense,a unified framework for low-rate DDoS attack detection was proposed.First of all,the validity of the data plane DDoS attacks in low rate was analyzed,on the basis of combining with low-rate of DDoS attacks in the aspect of communications,frequency characteristics,extract the mean value,maximum value,deviation degree and average deviation,survival time of ten dimensions characteristics of five aspects,to achieve the low-rate of DDoS attack detection based on bayesian networks,issued by the controller after the relevant strategies to block the attack flow.Finally,in OpenStack cloud environment,the detection rate of low-rate DDoS attack reaches 99.3% and the CPU occupation rate is 9.04%.It can effectively detect and defend low-rate DDoS attacks.     

Efficient authenticated key agreement protocols resistant to a denial‐of‐service attack

Malicious intruders may launch as many invalid requests as possible without establishing a server connection to bring server service to a standstill. This is called a denial‐of‐service (DoS) or distributed DoS (DDoS) attack. Until now, there has been no complete solution to resisting a DoS/DDoS attack. Therefore, it is an important network security issue to reduce the impact of a DoS/DDoS attack. A resource‐exhaustion attack on a server is one kind of denial‐of‐service attack. In this article we address the resource‐exhaustion problem in authentication and key agreement protocols. The resource‐exhaustion attack consists of both the CPU‐exhaustion attack and the storage‐exhaustion attack. In 2001, Hirose and Matsuura proposed an authenticated key agreement protocol (AKAP) that was the first protocol simultaneously resistant to both the CPU‐exhaustion attack and the storage‐exhaustion attack. However, their protocol is time‐consuming for legal users in order to withstand the DoS attack. Therefore, in this paper, we propose a slight modification to the Hirose–Matsuura protocol to reduce the computation cost. Both the Hirose–Matsuura and the modified protocols provide implicit key confirmation. Also, we propose another authenticated key agreement protocol with explicit key confirmation. The new protocol requires less computation cost. Because DoS/DDoS attacks come in a variety of forms, the proposed protocols cannot fully disallow a DoS/DDoS attack. However, they reduce the effect of such an attack and thus make it more difficult for the attack to succeed. Copyright © 2005 John Wiley & Sons, Ltd.     

Continuous leakage–resilient IBE in cloud computing

Cloud computing is an efficient tool in which cloud storage shares plenty of encrypted data with other data owners. In existing cloud computing scenarios, it may suffer from some new attacks like side channel attacks. Therefore, we are eager to introduce a new cryptographic scheme that can resist these new attacks. In this work, we exploit a new technique to build leakage‐resilient identity‐based encryption and use the stronger existing partial leakage model, such as continual leakage model. More specifically, our proposal is based on the underlying decisional bilinear Diffie‐Hellman assumption, but proven adaptively secure against adaptive chosen ciphertext attack in the standard model. Above all, a continuous leakage–resilient IBE scheme with adaptive security meets cloud computing with stronger security.     

基于TPM的云计算平台双向认证方案

为了解决云计算服务环境中用户和云服务器之间的双向认证问题,提出一种基于可信平台模块的云计算平台双向认证方案。将可信计算技术和传统的智能卡口令认证方法相结合应用于云计算服务平台,实现云计算中双方身份的认证,协商生成会话密钥,同时对云服务器的平台可信状况进行了验证。实验分析表明,该方案可以抵抗常见的各种攻击,安全性较高。计算时间复杂度在云计算服务中能够满足要求。     

Identifying critical challenges in the adoption of cloud‐based services

Cloud computing provides a way to integrate and share information on a real‐time basis across an organization. The current organizations are adopting the cloud services to gain competitive advantage in real‐time data sharing. To meet the current demand in semiconductor industries, they must develop better techniques to produce electronic products at low cost and in a large scale. Adoption of cloud‐based services may resolve the fastest growing demand of technical advancement of semiconductor industries. The research presented in this paper is based on an analysis of the data obtained from the semiconductor sector. This study identifies the critical challenges associated with the cloud service adoption in semiconductor industries. Twelve critical challenges have been identified that need to be overcome for adopting the cloud services for any semiconductor industry. These are network/Internet availability, data security, integration of various services, monitoring of data and services, maintenance of computing performance, liability, power outage, service interruption, organizational change, business complexity, legal issues, and lack of awareness.     

Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Wireless Personal Communications - Distributed denial of service (DDoS) attacks disrupt the availability of cloud services. The detection of these attacks is a major challenge in the cloud...     

Game theory approach for detecting vulnerable data centers in cloud computing network

Nowadays, cloud computing has many benefits to accessibility, scalability, and cost‐effectiveness, leading to network security risks and vulnerabilities. Cloud computing is gaining in popularity with the advances and growth of its systems. Therefore, the security of this system and the identification of vulnerable data centers are more complicated than the past. Definitely discovering vulnerable data centers that are vulnerable to attacks can help to strengthen these data centers and provide a safer and more secure network structure. This paper examines the vulnerability of malware data centers in the infrastructure and cloud computing network structure. Based on the analysis of the cloud computing system in the field of game theory, we introduce a developed model for identifying vulnerable data centers in cloud computing. The developed model in this paper is based on the game theory as a mathematical tool. According to the game theory, we introduce a measure of the degree of vulnerability of data centers in the cloud computing network.     

A based on blinded CP‐ABE searchable encryption cloud storage service scheme

Cloud computing has great economical advantages and wide application, more and more data owners store their data in the cloud storage server (CSS) to avoid tedious local data management and insufficient storage resources. But the privacy of data owners faces enormous challenges. The most recent searchable encryption technology adopts the ciphertext‐policy attribute‐based encryption (CP‐ABE), which is one good method to deal with this security issue. However, the access attributes of the users are transmitted and assigned in plaintext form. In this paper, we propose a based on blinded CP‐ABE searchable encryption cloud storage service (BCP‐ABE‐SECSS) scheme, which can blind the access attributes of the users in order to prevent the collusion attacks of the CSS and the users. Data encryption and keyword index generation are performed by the data owners; meanwhile, we construct that CSS not only executes the access control policy of the data but also performs the pre‐decryption operation about the encrypted data to solve higher time cost of decryption calculation to the data users. Security proof results show that this scheme has access attribute security, data confidentiality, indistinguishable security against chosen keyword attack, and resisting the collusion attack between the data user and the CSS. Performance analysis and the experimental results show that this scheme can effectively reduce the computation time cost of the data owners and the data users.     

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

Cloud 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.     

Shield Techniques for Application Layer DDoS Attack in MANET: A Methodological Review

In today’s world of wireless networks the mobile ad-hoc networks are widely preferred as a communication medium as these are infrastructure less networks. The application layer of these networks is targeted by attackers because it is responsible for actual data exchange with end users. As human dependency on wireless networks is increasing the DDoS attacks i.e. distributed denial of service attack which becomes a nightmare for the researchers. This attack is one of the most devastating attacks that can be executed on web-servers and congest the network keys like socket connections, CPU cycles, and memory database. In this current mobile computing world the necessity of DDOS attack management is significantly increased because this attack can degrade the entire web experience. Further, this DDOS attack is commenced along with the legitimate requests so it is also important to differentiate DDoS attack from other similar Events. This review endeavors to explore with more emphasis on application layer DDoS attack and its management stages like prevention, detection, mitigation and Differentiation along with comparative statement of prominent techniques discovered in each stage. This methodological survey report shall lead the way to researchers and network designers to suit the specific management scheme to provide the complete protection of wireless networks from DDoS attack.

     

Detection of Low-Rate Cloud DDoS Attacks in Frequency Domain Using Fast Hartley Transform

Distributed Denial-of-Service (DDoS) attack has been a serious threat to the availability feature of cloud computing. As traditional DDoS attacks are implemented using a huge volume of malicious traffic, the detection of such attacks becomes a naive task. To evade this detection, attackers are moving towards the Low-Rate DDoS (LRDDoS) attacks. The stealthy behavior of LRDDoS attack makes it difficult to get detected due to its low volume traffic. The existing frequency-domain approaches for LRDDoS detection are not feasible in terms of computational and storage requirements. This paper aims to propose a lightweight, accurate, and adaptive approach for the detection of LRDDoS attacks in frequency-domain. In this paper, the LRDDoS attack is detected by analyzing the power spectral distribution. The novelty of the proposed approach is to calculate the power spectral density using Fast Hartley Transform (FHT). The FHT processes real-valued input data, and has low computational and storage complexities. The approach is implemented on OpenStack cloud platform, and the aggregate network traffic (external and internal) is captured and analyzed. Experimental results show that the computational and storage complexities involved in FHT are lower than other transformation algorithms’ complexities. Thus, the approach provides faster response with an average detection time of 60.16 s. The average true negative and true positive rates obtained by the proposed approach are 99.83% and 99.46% respectively, which are competitive.

     

Efficient DDoS attack detection and prevention scheme based on SDN in cloud environment

For addressing the problem of two typical types of distributed denial of service (DDoS) attacks in cloud environment,a DDoS attack detection and prevention scheme called SDCC based on software defined network (SDN) architecture was proposed.SDCC used a combination of bandwidth detection and data flow detection,utilized confidence-based filtering (CBF) method to calculate the CBF score of packets,judged the packet of CBF score below the threshold as an attacking packet,added its attribute information to the attack flow feature library,and sent the flow table to intercept it through SDN controller.Simulation results show that SDCC can detect and prevent different types of DDoS attacks effectively,and it has high detection efficiency,reduces the controller’s computation overhead,and achieves a low false positive rate.     

Chaotic map‐based time‐aware multi‐keyword search scheme with designated server

The cloud storage service has been widely used in daily life because of its convenience. However, the service frequently suffers confidentiality problems. To address this problem, some efforts have been made on keyword search over encrypted data schemes. For instance, the chaotic‐based keyword search scheme over encrypted data has been proposed recently. However, the scheme just only support single‐ keyword search each time, which severely limits its utilization in cloud storage. This article proposes a novel chaotic‐based time‐aware multi‐keyword search scheme with designated server. Inner product similarity is adopted in our scheme to realize multiple keyword search and remove the constraint of single‐keyword search each time. Timed‐release encryption is integrated into the proposed scheme at the same time, which enables the data sender to specify the time when the cloud servers can search the encrypted data. Analysis indicates that our scheme not only can counter off‐line guessing attacks to the ciphertext and trapdoor, but also supports ranked search with a reasonable computational cost. Copyright © 2015 John Wiley & Sons, Ltd.     

云安全研究进展综述

 随着云计算在学术界和工业界的兴起,云计算也不可避免的带来了一些安全问题.本文对云计算的安全需求进行了总结,指出云计算不仅在机密性、数据完整性、访问控制和身份认证等传统安全性上存在需求,而且在可信性、配置安全性、虚拟机安全性等方面具有新的安全需求.我们对云计算的两个典型产品Amazon Web Services和Windows Azure的安全状况进行了总结,并阐述了针对云计算的拒绝服务攻击和旁通道攻击.基于云计算的安全需求和面临的攻击,对现有安全机制进行了优缺点分析,系统的总结了现有的安全机制.     

Efficient and low‐cost defense against distributed denial‐of‐service attacks in SDN‐based networks

Distributed are common threats in many networks, where attackers attempt to make victim servers unavailable to other users by flooding them with worthless requests. These attacks cannot be easily stopped by firewalls, since they forge lots of connections to victims with various IP addresses. The paper aims to exploit the software‐defined networking (SDN) technique to defend against DDoS attacks. However, the controller has to handle lots of connections launched by DDoS attacks, which burdens it with a heavy load and degrades SDN's performance. Therefore, the paper proposes an efficient and low‐cost DDoS defense (ELD) mechanism for SDN. It adopts a nested reverse‐exponential data storage scheme to help the controller efficiently record the information of packets in the limited memory. Once there are many packets with high IP variability sent to a certain server and this situation lasts for a while, then a DDoS attack is likely happening. In this case, the controller asks switches to block malicious connections by installing flow rules. Experimental results verify that the ELD mechanism rapidly recognizes protocol‐based DDoS attacks and stops them in time, including TCP SYN flood, UDP flood, and ICMP flood, and also greatly reduces the overhead for the controller to defend against attacks. Moreover, ELD can distinguish DDoS flows from legitimate ones with similar features such as elephant flows and impulse flows, thereby eliminating false alarms.     

Leveraging countermeasures as a service for VoIP security in the cloud

The emergence of cloud computing is contributing to the integration of multiple services, in particular VoIP services. While the cloud has recently been used for performing security attacks targeting IP telephony, it also provides new opportunities for supporting the security of this service. In that context, we propose a risk management strategy for VoIP cloud based on security countermeasures that may be outsourced as services. We present the architecture of our solution and its components in the context of services implementing the SIP protocol. We describe the mathematical modelling supporting our approach and detail different treatment strategies for the application of countermeasures. Finally, we quantify the benefits and limits of these strategies based on extensive simulation results. When a countermeasure fails, these strategies allow us to maintain the risk level low at an additional cost of up to 7%, or to accept an additional risk of up to 12%. They can also be combined to obtain a trade‐off between cost and performance. Copyright © 2013 John Wiley & Sons, Ltd.     

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.

     

An overview of network virtualization and cloud network as a service

Cloud computing services delivery and consumption model is based on communication infrastructure (network). The network serves as a linkage between the end‐users consuming cloud services and the providers of data centers providing the cloud services. In addition, in large‐scale cloud data centers, tens of thousands of compute and storage nodes are connected by a data center network to deliver a single‐purpose cloud service. To this end, some questions could be raised, such as the following: How do network architectures affect cloud computing? How will network architecture evolve to support better cloud computing and cloud‐based service delivery? What is the network's role in reliability, performance, scalability, and security of cloud computing? Should the network be a dumb transport pipe or an intelligent stack that is cloud workload aware? This paper focuses on the networking aspect in cloud computing and shall provide insights to these questions. Researchers can use this paper to accelerate their research on devising mechanisms for the following: (i) provisioning cloud network as a service and (ii) engineering network of data centers. Copyright © 2014 John Wiley & Sons, Ltd.     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.