Distributed Denial Of Service Attacks In Cloud Research Articles

An integrated SDN framework for early detection of DDoS attacks in cloud computing

Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources. While SDN cloud networks offer numerous advantages, they are vulnerable to Distributed Denial-of-Service (DDoS) attacks. DDoS attacks try to stop genuine users from using services and drain network resources to reduce performance or shut down services. However, early-stage detection of DDoS attack patterns in cloud environments remains challenging. Current methods detect DDoS at the SDN controller level, which is often time-consuming. We recommend focusing on SDN switches for early detection. Due to the large volume of data from diverse sources, we recommend traffic clustering and traffic anomalies prediction which is of DDoS attacks at each switch. Furthermore, to consolidate the data from multiple clusters, event correlation is performed to understand network behavior and detect coordinated attack activities. Many existing techniques stay behind for early detection and integration of multiple techniques to detect DDoS attack patterns. In this paper, we introduce a more efficient and effectively integrated SDN framework that addresses a gap in previous DDoS solutions. Our framework enables early and accurate detection of DDoS traffic patterns within SDN-based cloud environments. In this framework, we use Recursive Feature Elimination (RFE), Density Based Spatial Clustering (DBSCAN), time series techniques like Auto Regressive Integrated Moving Average (ARIMA), Lyapunov exponent, exponential smoothing filter, dynamic threshold, and lastly, Rule-based classifier. We have evaluated the proposed RDAER model on the CICDDoS 2019 dataset, that achieved an accuracy level of 99.92% and a fast detection time of 20 s, outperforming existing methods.

Mitigating DDoS attacks in cloud computing using machine learning algorithms

Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and performance of cloud computing systems. As these attacks continue to evolve in complexity and scale, traditional mitigation techniques may prove insufficient. This research explores the application of machine learning algorithms as an intelligent and adaptive approach to enhance DDoS detection and mitigation in cloud environments.The study leverages the dynamic and scalable nature of cloud computing to implement a robust defence mechanism against DDoS attacks. Machine learning models, such as supervised and unsupervised learning algorithms, are trained on network traffic data to identify patterns indicative of DDoS activity. The proposed system adapts to evolving attack strategies and is capable of real-time analysis, ensuring swift responses to emerging threats.

Stochastic Intrusion Detection Game-Based Arrangement Using Controlled Markov Chain for Prevention of DoS and DDoS Attacks in Cloud

DoS (denial of service) assault is the most prevalent assault these days. It imposes a major risk to cybersecurity. At the point when this assault is propelled by numerous conveyed machines on a solitary server machine, it is called as a DDoS (distributed denial of service) assault. Additionally, DoS bypass on DHCP (dynamic host configuration protocol) server assault is a rising and famous assault in a system. The authors have proposed a stochastic intrusion detection game-based arrangement utilizing controlled Markov chain that figures the transition probabilities starting with one state then onto the next in a state transition diagram. At first, the authors have conjectured these assaults, and after that, they proposed a methodology that uses the idea of master and slave IPS (intrusion prevention system). This approach works well when mapped to these estimated assaults and accordingly helps in the recognition and counteractive action of these assaults in a cloud domain.

Optimized extreme learning machine for detecting DDoS attacks in cloud computing

Distributed denial of service (DDoS) attack is a serious security threat to cloud computing that affects the availability of cloud services. Therefore, defending against these attacks becomes imperative. In this paper, we present a DDoS attack detection system based on an improved Self-adaptive evolutionary extreme learning machine (SaE-ELM). SaE-ELM model is improved by incorporating two more features. Firstly, it can adapt the best suitable crossover operator. Secondly, it can automatically determine the appropriate number of hidden layer neurons. These features improve the learning and classification capabilities of the model. The proposed system is evaluated using four datasets namely, NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017. It achieves the detection accuracy of 86.80%, 98.90%, 89.17%, and 99.99% with NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017 datasets, respectively. The experiments show that the performance of the proposed attack detection system is better than the system based on original SaE-ELM and state-of-the-art techniques. However, it shows a longer training time than SaE-ELM based system.

Cloud based DDoS attack detection and defense system using statistical approach

In the recent era, business and IT domain rely on the cloud as it has evolved as the potential service model and lots of people jumped on the bandwagon to seek profit out of the cloud computing environment. The cloud is highly vulnerable and its risk associated with unpatched machines is exposed to distributed denial of service (DDoS) attacks. According to cloud security alliance group DDoS is the major security attack in the cloud and the impact and effects on virtual machines is much unexplored. Despite numerous DDoS solutions, there is a need for 'a dish fit for gods' in cloud. Hence, the proposed system defends the DDoS attacks in cloud by monitoring the performance distortion, detecting multilayer attacks using statistical method. Based on the attack variances with normal using chi-square statistics, DDoS attack sources are enlisted and communicated to the defence system to filter attack traffic and protect the cloud.

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

Fuzzy self organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing

The characteristic features of cloud computing deployment make it highly vulnerable to distributed denial of service (DDoS) attacks. The recent advancement in software-defined networking (SDN) enhances the possibilities for defeating DDoS attacks in cloud computing environments. This option to improve the probability of defeating DDoS attacks is made feasible through the striking features of SDN that include their capability for software-oriented traffic investigation, network global dimension, dynamically updating forwarding rules and centralized point of control. This paper presents a Fuzzy self organizing maps-based DDOS mitigation (FSOMDM) technique that is ideally and suitably designed for improving the SDN capabilities of cloud computing. FSOMDM is the enhanced neural network model that effectively replaces the neurons of the traditional Kohonen neural network model through updating fuzzy rules. The property of software-oriented traffic investigation is utilized in this process and the fuzzy rule is used for exploring the dimension of input space from which a single valued output is derived for enabling the mitigation of DDoS. In addition, FSOMDM incorporates an attack-response process that possesses the significance of dropping attack flows through its enforcement in the control plane of SDN. The performance investigation of FSOMDM confirms its significance by facilitating nearly 94% of classifier accuracy evaluated in terms of true positive rate (TPR).

A statistical and distributed packet filter against DDoS attacks in Cloud environment

Distributed Denial of Service (DDoS) attacks are a serious threat to Cloud. These attacks consume large amount of resources and increase the service usage cost by a significant factor. Due to multi-tenancy and self-provisioning properties of Cloud, traditional DDoS detection techniques cannot be directly applied. Hence, there is a need for Cloud-specific DDoS detection framework. In this paper, a statistical and distributed network packet filtering model is proposed against DDoS attacks in Cloud. The key idea of this scheme is to distribute multiple packet filters among individual virtual machines, which generate and share collective profile of normal behaviour with a coordinator node at constant intervals. Statistics of selected network attributes construct the normal behaviour profile. Based on the deviation from normal behaviour a decision is made whether to accept or reject the incoming packet. The coordinator node monitors filter and distribute the averaged profile to newly provisioned nodes. Individual profiles have low memory and storage requirements and are updated dynamically. Simulation study indicates the effectiveness of this scheme in detecting DDoS attacks in Cloud.

Security challenges in cloud computing: state-of-art

Cloud computing platform provides on-demand access of services, and these services can also be customised, which enables the customers to use these services as per their requirement and they also need to pay according to their uses. Recent advancements in the field of cloud computing and internet technologies have attracted the organisations to shift their businesses on cloud. However, moving data and applications to a third party services provider also raises critical concern for security and privacy. Availability of cloud services is one of the most important security issues which directly affect the business of the cloud service providers and also its customers. Distributed denial of service (DDoS) attack on cloud is the root cause that challenges the availability of cloud services. This paper presents taxonomy of security issues in cloud, taxonomy of DDoS attacks in cloud, and taxonomy of DDoS defence mechanisms in cloud environment. We have also discussed, analysed, and compared some well-known methods for DDoS attack defence in cloud on different parameters that will help the victim to understand the risk and also assists them to select the proper defence technique to protect their cloud system from DDoS attacks.

Security challenges in cloud computing: state-of-art

Cloud computing platform provides on-demand access of services, and these services can also be customised, which enables the customers to use these services as per their requirement and they also need to pay according to their uses. Recent advancements in the field of cloud computing and internet technologies have attracted the organisations to shift their businesses on cloud. However, moving data and applications to a third party services provider also raises critical concern for security and privacy. Availability of cloud services is one of the most important security issues which directly affect the business of the cloud service providers and also its customers. Distributed denial of service (DDoS) attack on cloud is the root cause that challenges the availability of cloud services. This paper presents taxonomy of security issues in cloud, taxonomy of DDoS attacks in cloud, and taxonomy of DDoS defence mechanisms in cloud environment. We have also discussed, analysed, and compared some well-known methods for DDoS attack defence in cloud on different parameters that will help the victim to understand the risk and also assists them to select the proper defence technique to protect their cloud system from DDoS attacks.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

Distributed denial of service attacks in software-defined networking with cloud computing

Although software-defined networking (SDN) brings numerous benefits by decoupling the control plane from the data plane, there is a contradictory relationship between SDN and distributed denial-of-service (DDoS) attacks. On one hand, the capabilities of SDN make it easy to detect and to react to DDoS attacks. On the other hand, the separation of the control plane from the data plane of SDN introduces new attacks. Consequently, SDN itself may be a target of DDoS attacks. In this paper, we first discuss the new trends and characteristics of DDoS attacks in cloud computing environments. We show that SDN brings us a new chance to defeat DDoS attacks in cloud computing environments, and we summarize good features of SDN in defeating DDoS attacks. Then we review the studies about launching DDoS attacks on SDN and the methods against DDoS attacks in SDN. In addition, we discuss a number of challenges that need to be addressed to mitigate DDoS attached in SDN with cloud computing. This work can help understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks.

An Adaptive Approach to Mitigate Ddos Attacks in Cloud

Distributed denial of service (DDOS) attack constitutes one of the prominent cyber threats and among the hardest security problems in modern cyber world. This research work focuses on reviewing DDOS detection techniques and developing a numeric stable theoretical framework used for detecting various DDOS attacks in cloud. Main sections in the paper are devoted to review and analysis of algorithms used for detection of DDOS attacks. The framework theorized here deals with the variability calculation method in conjunction with sampling, searching methods to find a current state of a particular parameter under observation for detecting DDOS attacks. This way a solution is to build that measure the performance and conduct the monitoring framework to capture adversity related to DDOS attacks. The described algorithm intends to capture the current context value of the parameters that determine the reliability of the detection algorithm and the online pass algorithm helps to maintain the variability of those collected values thus maintaining numerical stability by doing robust statistical operations at endpoints of traffic in cloud based network.

A multilevel thrust filtration defending mechanism against DDoS attacks in cloud computing environment

Cloud computing is a technology which not only gained advantages from ascendant technologies, but also suffered from its security breaches, of which availability is the most serious security issue. Distributed Denial of Service (DDoS) is a kind of resource-availability-related attack launched with the aim of subverting the Data Centre (DC) for resource unavailability to the legitimate clients. In this paper, we propose 'Multilevel Thrust Filtration (MTF) mechanism' as a solution, which authenticates the incoming requesters and detects the different types of DDoS attacks at different levels to detect the most intensive attack at an early stage to prevent the unnecessary traffic reaching the DC. A hybrid solution is proposed to detect four different kinds of attacks that have been taken into consideration. Profit analysis proved that the proposed mechanism is deployable at an attack-prone DC for resource protection, which would eventually result in beneficial service at slenderised revenue.

Entropy based Anomaly Detection System to Prevent DDoS Attacks in Cloud

Computing is a recent computing model; provides consistent access to wide area distributed resources. It revolutionized the IT world with its services provision infrastructure, less maintenance cost, data and service availability assurance, rapid accessibility and scalability. Grid and Cloud Computing Intrusion Detection System (GCCIDS) detects encrypted node communication and find the hidden attack trial which inspects and detects those attacks that network based and host based can't identify. It incorporates Knowledge and behavior analysis to identify specific intrusions. Signature based IDS monitor the packets in the network and identifies those threats by matching with database but It fails to detect those attacks that are not included in database. Signature based IDS will perform poor capturing in large volume of anomalies. Another problem is that Cloud Service Provider (CSP) hides the attack that is caused by intruder, due to distributed nature; cloud environment has high possibility for vulnerable resources. By impersonating legitimate users, the intruders can use a service's abundant resources maliciously. In Proposed System we combine few concepts which are available with new intrusion detection techniques. Here to merge Entropy based System with Anomaly detection System for providing multilevel Distributed Denial of Service (DDoS). This is done in two steps: First, Users are allowed to pass through router in network site in that it incorporates Detection Algorithm and detects for legitimate user. Second, again it pass through router placed in cloud site in that it incorporates confirmation Algorithm and checks for threshold value, if it's beyond the threshold value it considered as legitimate user, else it's an intruder found in environment. This System is represented and maintained by as third party. When attack happens in environment, it sends notification message for client and advisory report to Cloud Service Provider (CSP).