ATSSC:An Attack Tolerant System in Serverless Computing

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog Computing

Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge servers.It facil-itates the benefits of the network edge with the maximized probability of offering interaction with the cloud.However,the fog computing characteristics are suscep-tible to counteract the challenges of security.The issues present with the Physical Layer Security(PLS)aspect in fog computing which included authentication,integrity,and confidentiality has been considered as a reason for the potential issues leading to the security breaches.In this work,the Octonion Algebra-inspired Non-Commutative Ring-based Fully Homomorphic Encryption Scheme(NCR-FHE)was proposed as a secrecy improvement technique to overcome the impersonation attack in cloud computing.The proposed approach was derived through the benefits of Octonion algebra to facilitate the maximum security for big data-based applications.The major issues in the physical layer security which may potentially lead to the possible security issues were identified.The potential issues causing the impersonation attack in the Fog computing environment were identified.The proposed approach was compared with the existing encryption approaches and claimed as a robust approach to identify the impersonation attack for the fog and edge network.The computation cost of the proposed NCR-FHE is identified to be significantly reduced by 7.18%,8.64%,9.42%,and 10.36%in terms of communication overhead for varying packet sizes,when compared to the benchmarked ECDH-DH,LHPPS,BF-PHE and SHE-PABF schemes.

An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,etc.Although Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber criminals.Distributed Denial of Service(DDoS)is the most common type of attack on cloud computing.Consequently,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks.Since DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks.Further,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent.In this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD dataset.In Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction error.This deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM model.Moreover,it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks.The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

A Resource-Based Pricing Collaborative Approach for Mitigating DDoS Attack in Mobile Edge Computing

Resource-constrainted and located closer to users,edge servers are more vulnerable to Distributed Denial of Service(DDoS)attacks.In order to mitigate the impact of DDoS attacks on benign users,this paper designed a Resource-based Pricing Collaborative approach(RPC)in mobile edge computing.By introducing the influence of resource prices on requester in economics,a collaboration model based on resource pricing was established,and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in network.The article theoretically proved the existence and rationality of the Nash equilibrium.Finally,simulation results verified the effectiveness and feasibility of the proposed approach in two experimental scenes.Experimental results shows that RPC can effectively improve the network ability to mitigate DDoS attacks,and alleviate the adverse effects of server attacks under delay constraints.

Detecting Man-in-the-Middle Attack in Fog Computing for Social Media

Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Protecting Terminals by Security Domain Mechanism Based on Trusted Computing

Networks are composed with servers and rather larger amounts of terminals and most menace of attack and virus come from terminals. Eliminating malicious code and ac cess or breaking the conditions only under witch attack or virus can be invoked in those terminals would be the most effec tive way to protect information systems. The concept of trusted computing was first introduced into terminal virus immunity. Then a model of security domain mechanism based on trusted computing to protect computers from proposed from abstracting the general information systems. The principle of attack resistant and venture limitation of the model was demonstrated by means of mathematical analysis, and the realization of the model was proposed.

Denial of Service Due to Direct and Indirect ARP Storm Attacks in LAN Environment

ARP-based Distributed Denial of Service (DDoS) attacks due to ARP-storms can happen in local area networks where many computer systems are infected by worms such as Code Red or by DDoS agents. In ARP attack, the DDoS agents constantly send a barrage of ARP requests to the gateway, or to a victim computer within the same sub-network, and tie up the resource of attacked gateway or host. In this paper, we set to measure the impact of ARP-attack on resource exhaustion of computers in a local area network. Based on attack experiments, we measure the exhaustion of processing and memory resources of a victim computer and also other computers, which are located on the same network as the victim computer. Interestingly enough, it is observed that an ARP-attack not only exhausts resource of the victim computer but also significantly exhausts processing resource of other non-victim computers, which happen to be located on the same local area network as the victim computer.

COMPUTATION OF FIELD STRUCTURE AND AERODYNAMIC CHARACTERISTICS OF DELTA WINGS AT HIGH ANGLES OF ATTACK

A numerical investigation of the structure of the vortical flowfield over delta wings at high angles of attack in longitudinal and with small sideslip angle is presented. Three-dimensional Navier-Stokes numerical simulations were carried out to predict the complex leeward-side flowfield characteristics that are dominated by the effect of the breakdown of the leading-edge vortices. The methods that analyze the flowfield structure quantitatively were given by using flowfield data from the computational results. In the region before the vortex breakdown, the vortex axes are approximated as being straight line. As the angle of attack increases, the vortex axes are closer to the root chord, and farther away from the wing surface. Along the vortex axes, as the adverse pressure gradients occur, the axial velocity decreases, that is, A is negativee, so the vortex is unstable, and it is possible to breakdown. The occurrence of the breakdown results in the instability of lateral motion for a delta wing, and the lateral moment diverges after a small perturbation occurs at high angles of attack. However, after a critical angle of attack is reached the vortices breakdown completely at the wing apex, and the instability resulting from the vortex breakdown disappears.

Safeguarding Cloud Computing Infrastructure:A Security Analysis

Cloud computing is the provision of hosted resources,comprising software,hardware and processing over the World Wide Web.The advantages of rapid deployment,versatility,low expenses and scalability have led to the widespread use of cloud computing across organizations of all sizes,mostly as a component of the combination/multi-cloud infrastructure structure.While cloud storage offers significant benefits as well as cost-effective alternatives for IT management and expansion,new opportunities and challenges in the context of security vulnerabilities are emerging in this domain.Cloud security,also recognized as cloud computing security,refers to a collection of policies,regulations,systematic processes that function together to secure cloud infrastructure systems.These security procedures are designed to safeguard cloud data,to facilitate regulatory enforcement and to preserve the confidentiality of consumers,as well as to lay down encryption rules for specific devices and applications.This study presents an overview of the innovative cloud computing and security challenges that exist at different levels of cloud infrastructure.In this league,the present research work would be a significant contribution in reducing the security attacks on cloud computing so as to provide sustainable and secure services.

Illegal Access Detection in the Cloud Computing Environment

In this paper detection method for the illegal access to the cloud infrastructure is proposed. Detection process is based on the collaborative filtering algorithm constructed on the cloud model. Here, first of all, the normal behavior of the user is formed in the shape of a cloud model, then these models are compared with each other by using the cosine similarity method and by applying the collaborative filtering method the deviations from the normal behavior are evaluated. If the deviation value is above than the threshold, the user who gained access to the system is evaluated as illegal, otherwise he is evaluated as a real user.

Evaluation of Hypervisor Stability towards Insider Attacks

Virtualization technology plays a key role in cloud computing.Thus,the security issues of virtualization tools（hypervisors,emulators,etc.） should be under precise consideration.However,threats of insider attacks are underestimated.The virtualization tools and hypervisors have been poorly protected from this type of attacks.Furthermore,hypervisor is one of the most critical elements in cloud computing infrastructure.Firstly,hypervisor vulnerabilities analysis is provided.Secondly,a formal model of insider attack on hypervisor is developed.Consequently,on the basis of the formal attack model,we propose a new methodology of hypervisor stability evaluation.In this paper,certain security countermeasures are considered that should be integrated in hypervisor software architecture.

Server-aided access control for cloud computing

With the massive diffusion of cloud computing, more and more sensitive data is being centralized into the cloud for sharing, which brings forth new challenges for the security and privacy of outsourced data. To address these challenges, the server-aided access control(SAAC) system was proposed. The SAAC system builds upon a variant of conditional proxy re-encryption(CPRE) named threshold conditional proxy re-encryption(TCPRE). In TCPRE, t out of n proxies can re-encrypt ciphertexts(satisfying some specified conditions) for the delegator(while up to t-1 proxies cannot), and the correctness of the re-encrypted ciphertexts can be publicly verified. Both features guarantee the trust and reliability on the proxies deployed in the SAAC system. The security models for TCPRE were formalized, several TCPRE constructions were proposed and that our final scheme was secure against chosen-ciphertext attacks was proved.

Adversarial attacks and defenses in physiological computing:a systematic review

Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even death.However,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to them.This study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

面向智算融合网络的自主防御范式研究

随着数字经济时代算力供给模式的变革,以算力为核心的新型网络基础设施已成为实现算力资源共享、支撑数字经济转型的重要动力.在算力网络中,多元异构用户终端通过多种方式高频接入网络以随时随地获取算力服务,网络的开放性和动态性增大,算力网络将面临更严峻的安全挑战.然而,基于传统网络的安全防御模式通常针对具体安全问题静态式增补安全防护组件,无法主动适配用户需求灵活调整防御策略,难以应对算力网络中的安全风险.因此,本文面向新型算力网络安全需求,将安全功能作为网络内部属性,基于智算融合网络提出一种多维协同自主防御范式.结合智算融合网络“三层”“三域”的设计思想,在“三层”中,以广义服务层定义安全固有服务,以映射适配层智慧适配安全功能,以融合组件层执行安全策略;在“三域”中,以实体域先导资源适配,以知识域驱动安全服务流程,以感控域实施具体安全技术,构建“检测”“溯源”“防御”三维一体的完整基础管控流程,其中安全策略与技术可根据场景扩展性与业务安全性进行灵活调整.最终,通过仿真实验对所提范式有效性进行了验证,为未来智算融合安全的进一步研究和应用提供参考.

可信赖云计算的通信防火墙攻击捕获系统设计

为提高通信防火墙攻击捕获的有效性,提出基于可信赖云计算的通信防火墙攻击捕获系统。系统硬件设计过滤器、内核防火墙和捕获器,过滤器执行数据分流过滤,内核防火墙执行iptables命令和ebtables命令完成信息匹配,捕获器对5种虚拟路径进行分析,完成信息捕获。可信赖云计算软件设计,构建不同数据包的概率密度函数,判读数据是否为攻击信息。实验结果表明,所设计系统能够保证计算密集型任务的执行成功率达到90%以上,降低计算过程的收敛程度。