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DDoS attacks, also known as distributed denial-of-service attacks, pose a significant risk to networks in the cloud. The attackers aim to flood the target system with an overwhelming amount of data and requests until it becomes completely overloaded and unable to function properly. These attacks are becoming smarter and more dangerous all the time. A low-rate DDoS attack is one such strategy that makes detection difficult. At the same time, cloud infrastructure is rapidly evolving. Container-based technology makes it possible for cloud computing to use resources efficiently and scale services in a flexible way. Existing methods for detecting DDoS attacks in cloud computing are insufficient when adversaries use low-rate DDoS attacks. A method is required that can not only identify the attack but also prevent it to some extent. A Low-Rate DDoS Attack Detection Framework (LRDADF) was proposed for this purpose when adversaries use low-rate DDoS attacks. A comprehensive approach is required because low-rate DDoS attacks are difficult to detect. In addition to employing deep learning methods to detect such attacks, we proposed a mathematical model to realize a mitigation strategy. As a result, we proposed a new algorithm called the Hybrid Approach for Low-Rate DDoS Detection (HA-LRDD). The algorithm employs an AI-enabled method comprised of deep convolutional neural networks (CNN) and a deep auto encoder. We defined another algorithm called Dynamic Low-Rate DDoS Mitigation (DLDM), which mitigates the impact of an attack once it has been identified. It also ensures that the attack is defeated and that the infrastructure continues to operate. A comprehensive simulation study revealed that the proposed framework is capable of detecting and mitigating low-rate DDoS attacks to ensure an acceptable level of service in cloud computing environments.

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN's protection against LDDoS attacks. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.

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. [ABSTRACT FROM AUTHOR]

The multi-homed mobile devices in the mobile cloud computing (MCC) systems can improve their throughput by allocating the application data over several paths simultaneously, enabled by the promising Multipath TCP (MPTCP) technology. Meanwhile, network attacks against current Internet infrastructures are likely to increase, especially with the widely deployment of MCC systems. When a MPTCP connection is under network attacks and becomes a poor-performing path or a broken path, it can significantly affect other stable paths and in the absence of related schemes to handle this, MPTCP will undoubtedly suffer from serious performance degradation. Moreover, applying MPTCP to cloud data delivery may generally lead to higher energy consumption and is not favorable to a power-constrained mobile device. In this paper, we propose MPTCP-La/E2, a low-rate distributed denial-of-service (LDDoS) attack-aware energy-efficient MPTCP solution aiming at: 1) avoiding the LDDoS-caused performance degradation of cloud multipath transmission, which has been seldom considered in existing MPTCP solutions and 2) optimizing the energy usage while still maintaining user's perceived quality of cloud multipathing services. The simulation results show that MPTCP-La/E2 outperforms the baseline MPTCP in terms of QoS and energy-savings in a multihomed MCC network environment.