Biblio

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity.

Machine learning-based DDoS attack detection methods are mostly implemented at the packet level with expensive computational time costs, and the space cost of those sketch-based detection methods is uncertain. This paper proposes a two-stage DDoS attack detection algorithm combining time series-based multi-dimensional sketch and machine learning technologies. Besides packet numbers, total lengths, and protocols, we construct the time series-based multi-dimensional sketch with limited space cost by storing elephant flow information with the Boyer-Moore voting algorithm and hash index. For the first stage of detection, we adopt CNN to generate sketch-level DDoS attack detection results from the time series-based multi-dimensional sketch. For the sketch with potential DDoS attacks, we use RNN with flow information extracted from the sketch to implement flow-level DDoS attack detection in the second stage. Experimental results show that not only is the detection accuracy of our proposed method much close to that of packet-level DDoS attack detection methods based on machine learning, but also the computational time cost of our method is much smaller with regard to the number of machine learning operations.

The issues of development and legal regulation of cybersecurity in Ukraine are considered. The expediency of further improvement of the regulatory framework, its implementation and development of cybersecurity systems is substantiated. Further development of the theoretical base of cyber defense using spline functions is proposed. The characteristics of network traffic are considered from the point of view of detecting DDoS cyber attacks (SYN-Flood, ICMP-Flood, UDP-Flood) and predicting DDoS cyber-attacks using spline functions. The spline extrapolation method makes it possible to predict DDoS cyber attacks with great accuracy.

This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack.

DDoS attacks, one of the oldest forms of cyberthreats, continue to be a favorite tool of mass interruption, presenting cybersecurity hazards to practically every type of company, large and small. As a matter of fact, according to IDC, DDoS attacks are predicted to expand at an 18 percent compound annual growth rate (CAGR) through 2023, indicating that it is past time to enhance investment in strong mitigation systems. And while some firms may assume they are limited targets for a DDoS assault, the amount of structured internet access to power corporation services and apps exposes everyone to downtime and poor performance if the infrastructure is not protected against such attacks. We propose using correlations between missing packets to increase detection accuracy. Furthermore, to ensure that these correlations are calculated correctly.

Intrusion detection systems (IDS) are most efficient way of defending against network-based attacks aimed at system devices, especially wireless devices. These systems are used in almost all large-scale IT infrastructures components, and they effected with different types of network attacks such as DDoS attack. Distributed Denial of-Services (DDoS) attacks the protocols and systems that are intended to provide services (to the public) are inherently vulnerable to attacks like DDoS, which were launched against a number of important Internet sites where security precautions were in place.

This paper mainly explores the detection and defense of DDoS attacks in the SDN architecture of the 5G environment, and proposes a DDoS attack detection method based on the deep learning two-level model CNN-LSTM in the SDN network. Not only can it greatly improve the accuracy of attack detection, but it can also reduce the time for classifying and detecting network traffic, so that the transmission of DDoS attack traffic can be blocked in time to ensure the availability of network services.

Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given.

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

Cities are becoming increasingly smart as the Internet of Things (IoT) proliferates. With IoT devices interconnected, smart cities can offer novel and ubiquitous services as well as automate many of our daily lives (e.g., smart health, smart home). The abundance in the number of IoT devices leads to divergent types of security threats as well. One of such important attacks is the Distributed Denial of Service attack(DDoS). DDoS attacks have become increasingly common in the internet of things because of the rapid growth of insecure devices. These attacks slow down legitimate network requests. Although DDoS attacks were first reported in 1996, the sophistication of these attacks has increased significantly. In mid-August 2020, a 2 Terabytes per second(TBps) attack targeting critical infrastructure, such as finance, was reported. In the next two years, it is predicted that this number will double to 15 million attacks. Blockchain technology, whose development dates back to the advent of the internet, has become one of the most important advancements to come along since that time. Several applications can use this technology to secure exchanges. Using blockchain to mitigate DDoS attacks is discussed in this survey paper in diverse domains to date. Its purpose is to expose the strengths, weaknesses, and limitations of the different approaches to DDoS mitigation. As a research and development platform for DDoS mitigation, this paper will act as a central hub for a more comprehensive understanding of these approaches.

Software Defined Networking (SDN) is an emerging technology, which provides the flexibility in communicating among network. Software Defined Network features separation of the data forwarding plane from the control plane which includes controller, resulting centralized network. Due to centralized control, the network becomes more dynamic, and resources are managed efficiently and cost-effectively. Network Virtualization is transformation of network from hardware-based to software-based. Network Function Virtualization will permit implementation, adaptable provisioning, and even management of functions virtually. The use of virtualization of SDN networks permits network to strengthen the features of SDN and virtualization of NFV and has for that reason has attracted notable research awareness over the last few years. SDN platform introduces network security challenges. The network becomes vulnerable when a large number of requests is encapsulated inside packet\_in messages and passed to controller from switch for instruction, if it is not recognized by existing flow entry rules. which will limit the resources and become a bottleneck for the entire network leading to DDoS attack. It is necessary to have quick provisional methods to prevent the switches from breaking down. To resolve this problem, the researcher develops a mechanism that detects and mitigates flood attacks. This paper provides a comprehensive survey which includes research relating frameworks which are utilized for detecting attack and later mitigation of flood DDoS attack in Software Defined Network (SDN) with the help of NFV.

DDoS attacks produce a lot of traffic on the network. DDoS attacks may be fought in a novel method thanks to the rise of Software Defined Networking (SDN). DDoS detection and data gathering may lead to larger system load utilization among SDN as well as systems, much expense of SDN, slow reaction period to DDoS if they are conducted at regular intervals. Using the Identification Retrieval algorithm, we offer a new DDoS detection framework for detecting resource scarcity type DDoS attacks. In designed to check low-density DDoS attacks, we employ a combination of network traffic characteristics. The KSVD technique is used to generate a dictionary of network traffic parameters. In addition to providing legitimate and attack traffic models for dictionary construction, the suggested technique may be used to network traffic as well. Matching Pursuit and Wavelet-based DDoS detection algorithms are also implemented and compared using two separate data sets. Despite the difficulties in identifying LR-DoS attacks, the results of the study show that our technique has a detection accuracy of 89%. DDoS attacks are explained for each type of DDoS, and how SDN weaknesses may be exploited. We conclude that machine learning-based DDoS detection mechanisms and cutoff point DDoS detection techniques are the two most prevalent methods used to identify DDoS attacks in SDN. More significantly, the generational process, benefits, and limitations of each DDoS detection system are explained. This is the case in our testing environment, where the intrusion detection system (IDS) is able to block all previously identified threats

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer.

From the past few years, DDoS attack incidents are continuously rising across the world. DDoS attackers have also shifted their target towards cloud environments as majority of services have shifted their operations to cloud. Various authors proposed distinct solutions to minimize the DDoS attacks effects on victim services and co-located services in cloud environments. In this work, we propose an approach by utilizing incoming request separation at the container-level. In addition, we advocate to employ scale-inside out [10] approach for all the suspicious requests. In this manner, we achieve the request serving of all the authenticated benign requests even in the presence of an attack. We also improve the usages of scale-inside out approach by applying it to a container which is serving the suspicious requests in a separate container. The results of our proposed technique show a significant decrease in the response time of benign users during the DDoS attack as compared with existing solutions.

DDoS attacks still represent a severe threat to network services. While there are more or less workable solutions to defend against these attacks, there is a significant space for further research regarding automation of reactions and subsequent management. In this paper, we focus on one piece of the whole puzzle. We strive to automatically infer filtering rules which are specific to the current DoS attack to decrease the time to mitigation. We employ a machine learning technique to create a model of the traffic mix based on observing network traffic during the attack and normal period. The model is converted into the filtering rules. We evaluate our approach with various setups of hyperparameters. The results of our experiments show that the proposed approach is feasible in terms of the capability of inferring successful filtering rules.

In recent decades, a Distributed Denial of Service (DDoS) attack is one of the most expensive attacks for business organizations. The DDoS is a form of cyber-attack that disrupts the operation of computer resources and networks. As technology advances, the styles and tools used in these attacks become more diverse. These attacks are increased in frequency, volume, and intensity, and they can quickly disrupt the victim, resulting in a significant financial loss. In this paper, it is described the significance of DDOS attacks and propose a new method for detecting and mitigating the DDOS attacks by analyzing the traffics coming to the server from the BOTNET in attacking system. The process of analyzing the requests coming from the BOTNET uses the Machine learning algorithm in the decision making. The simulation is carried out and the results analyze the DDOS attack.

Network security is a prominent topic that is gaining international attention. Distributed Denial of Service (DDoS) attack is often regarded as one of the most serious threats to network security. Software Defined Network (SDN) decouples the control plane from the data plane, which can meet various network requirements. But SDN can also become the object of DDoS attacks. This paper proposes an automated DDoS attack mitigation method that is based on the programmability of the Ryu controller and the features of the OpenFlow switch flow tables. The Mininet platform is used to simulate the whole process, from SDN traffic generation to using a K-Nearest Neighbor model for traffic classification, as well as identifying and mitigating DDoS attack. The packet counts of the victim's malicious traffic input port are significantly lower after the mitigation method is implemented than before the mitigation operation. The purpose of mitigating DDoS attack is successfully achieved.

Distributed Denial of Service (DDoS) attacks aim to make a server unresponsive by flooding the target server with a large volume of packets (Volume based DDoS attacks), by keeping connections open for a long time and exhausting the resources (Low and Slow DDoS attacks) or by targeting protocols (Protocol based attacks). Volume based DDoS attacks that flood the target server with a large number of packets are easier to detect because of the abnormality in packet flow. Low and Slow DDoS attacks, however, make the server unavailable by keeping connections open for a long time, but send traffic similar to genuine traffic, making detection of such attacks difficult. This paper proposes a solution to detect and mitigate one such Low and slow DDoS attack, Slowloris in an SDN (Software Defined Networking) environment. The proposed solution involves communication between the detection and mitigation module and the controller of the Software Defined Network to get data to detect and mitigate low and slow DDoS attack.

Wireless sensor networks are used in many areas such as war field surveillance, monitoring of patient, controlling traffic, environmental and building surveillance. Wireless technology, on the other hand, brings a load of new threats with it. Because WSNs communicate across radio frequencies, they are more susceptible to interference than wired networks. The authors of this research look at the goals of WSNs in terms of security as well as DDOS attacks. The majority of techniques are available for detecting DDOS attacks in WSNs. These alternatives, on the other hand, stop the assault after it has begun, resulting in data loss and wasting limited sensor node resources. The study finishes with a new method for detecting the UDP Reflection Amplification Attack in WSN, as well as instructions on how to use it and how to deal with the case.

The new paradigm software-defined networking (SDN) supports network innovation and makes the control of network operations more agile. The flow table is the main component of SDN switch which contains a set of flow entries that define how new flows are processed. Low-rate distributed denial-of-service (LR-DDoS) attacks are difficult to detect and mitigate because they behave like legitimate users. There are many detection methods for LR DDoS attacks in the literature, but none of these methods detect single-packet LR DDoS attacks. In fact, LR DDoS attackers exploit vulnerabilities in the mechanism of congestion control in TCP to either periodically retransmit burst attack packets for a short time period or to continuously launch a single attack packet at a constant low rate. In this paper, the proposed scheme detects LR-DDoS by examining all incoming packets and filtering the single packets sent from different source IP addresses to the same destination at a constant low rate. Sending single packets at a constant low rate will increase the number of flows at the switch which can make it easily overflowed. After detecting the single attack packets, the proposed scheme prevents LR-DDoS at its early stage by deleting the flows created by these packets once they reach the threshold. According to the results of the experiment, the scheme achieves 99.47% accuracy in this scenario. In addition, the scheme has simple logic and simple calculation, which reduces the overhead of the SDN controller.

Distributed Denial-of-Service (DDoS) attacks aim to cause downtime or a lack of responsiveness for web services. DDoS attacks targeting the application layer are amongst the hardest to catch as they generally appear legitimate at lower layers and attempt to take advantage of common application functionality or aspects of the HTTP protocol, rather than simply send large amounts of traffic like with volumetric flooding. Attacks can focus on functionality such as database operations, file retrieval, or just general backend code. In this paper, we examine common forms of application layer attacks, preventative and detection measures, and take a closer look specifically at HTTP Flooding attacks by the High Orbit Ion Cannon (HOIC) and “low and slow” attacks through slowloris.

Computer and Vehicular networks, both are prone to multiple information security breaches because of many reasons like lack of standard protocols for secure communication and authentication. Distributed Denial of Service (DDoS) is a threat that disrupts the communication in networks. Detection and prevention of DDoS attacks with accuracy is a necessity to make networks safe.In this paper, we have experimented two machine learning-based techniques one each for attack detection and attack prevention. These detection & prevention techniques are implemented in different environments including vehicular network environments and computer network environments. Three different datasets connected to heterogeneous environments are adopted for experimentation. The first dataset is the NSL-KDD dataset based on the traffic of the computer network. The second dataset is based on a simulation-based vehicular environment, and the third CIC-DDoS 2019 dataset is a computer network-based dataset. These datasets contain different number of attributes and instances of network traffic. For the purpose of attack detection AdaBoostM1 classification algorithm is used in WEKA and for attack prevention Logit Model is used in STATA. Results show that an accuracy of more than 99.9% is obtained from the simulation-based vehicular dataset. This is the highest accuracy rate among the three datasets and it is obtained within a very short period of time i.e., 0.5 seconds. In the same way, we use a Logit regression-based model to classify packets. This model shows an accuracy of 100%.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.

Cloud computing provides a great platform for the users to utilize the various computational services in order accomplish their requests. However it is difficult to utilize the computational storage services for the file handling due to the increased protection issues. Here Distributed Denial of Service (DDoS) attacks are the most commonly found attack which will prevent from cloud service utilization. Thus it is confirmed that the DDoS attack detection and load balancing in cloud are most extreme issues which needs to be concerned more for the improved performance. This attained in this research work by measuring up the trust factors of virtual machines in order to predict the most trustable VMs which will be combined together to form the trustable source vector. After trust evaluation, in this work Bat algorithm is utilized for the optimal load distribution which will predict the optimal VM resource for the task allocation with the concern of budget. This method is most useful in the process of detecting the DDoS attacks happening on the VM resources. Finally prevention of DDOS attacks are performed by introducing the Fuzzy Extreme Learning Machine Classifier which will learn the cloud resource setup details based on which DDoS attack detection can be prevented. The overall performance of the suggested study design is performed in a Java simulation model to demonstrate the superiority of the proposed algorithm over the current research method.