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Mao, Lina, Tang, Linyan.  2021.  The Design of the Hybrid Intrusion Detection System ABHIDS. 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). :354–358.
Information system security is very important and very complicated, security is to prevent potential crisis. To detect both from external invasion behavior, also want to check the internal unauthorized behavior. Presented here ABHIDS hybrid intrusion detection system model, designed a component Agent, controller, storage, filter, manager component (database), puts forward a new detecting DDoS attacks (trinoo) algorithm and the implementation. ABHIDS adopts object-oriented design method, a study on intrusion detection can be used as a working mechanism of the algorithms and test verification platform.
Wu, Zhijun, Cui, Weihang, Gao, Pan.  2021.  Filtration method of DDoS attacks based on time-frequency analysis. 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :75–80.
Traditional DDoS attacks mainly send massive data packets through the attacking machine, consuming the network resources or server resources of the target server, making users unable to use server resources to achieve the purpose of denial of service. This type of attack is called a Flooding-based DDoS (FDDoS) attack. It has the characteristics of large traffic and suddenness. However, Low-rate DDoS (LDDoS) attack is a new type of DDoS attack. LDDoS utilize the TCP congestion control mechanism and sends periodic pulses to attack, which can seriously reduce the TCP flow throughput of the attacked link. It has the characteristics of small traffic and strong concealment. Each of these two DDoS attack methods has its own hard-to-handle characteristics, so that there is currently no particularly effective method to prevent such attacks. This paper uses time-frequency analysis to classify and filter DDoS traffic. The proposed filtering method is designed as a system in the actual environment. Experimental results show that the designed filtering algorithm can resist not only FDDoS attacks, but also LDDoS attacks.
Mani, Santosh, Nene, Manisha J.  2021.  Self-organizing Software Defined Mesh Networks to Counter Failures and Attacks. 2021 International Conference on Intelligent Technologies (CONIT). :1–7.
With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.
Wang, Xin, Ma, Xiaobo, Qu, Jian.  2021.  A Link Flooding Attack Detection Method based on Non-Cooperative Active Measurement. 2021 8th International Conference on Dependable Systems and Their Applications (DSA). :172–177.
In recent years, a new type of DDoS attacks against backbone routing links have appeared. They paralyze the communication network of a large area by directly congesting the key routing links concerning the network accessibility of the area. This new type of DDoS attacks make it difficult for traditional countermeasures to take effect. This paper proposes and implements an attack detection method based on non-cooperative active measurement. Experiments show that our detection method can efficiently perceive changes of network link performance and assist in identifying such new DDoS attacks. In our testbed, the network anomaly detection accuracy can reach 93.7%.
Gupta, B. B., Gaurav, Akshat, Peraković, Dragan.  2021.  A Big Data and Deep Learning based Approach for DDoS Detection in Cloud Computing Environment. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). :287–290.
Recently, as a result of the COVID-19 pandemic, the internet service has seen an upsurge in use. As a result, the usage of cloud computing apps, which offer services to end users on a subscription basis, rises in this situation. However, the availability and efficiency of cloud computing resources are impacted by DDoS attacks, which are designed to disrupt the availability and processing power of cloud computing services. Because there is no effective way for detecting or filtering DDoS attacks, they are a dependable weapon for cyber-attackers. Recently, researchers have been experimenting with machine learning (ML) methods in order to create efficient machine learning-based strategies for detecting DDoS assaults. In this context, we propose a technique for detecting DDoS attacks in a cloud computing environment using big data and deep learning algorithms. The proposed technique utilises big data spark technology to analyse a large number of incoming packets and a deep learning machine learning algorithm to filter malicious packets. The KDDCUP99 dataset was used for training and testing, and an accuracy of 99.73% was achieved.
Singh, Praneet, P, Jishnu Jaykumar, Pankaj, Akhil, Mitra, Reshmi.  2021.  Edge-Detect: Edge-Centric Network Intrusion Detection using Deep Neural Network. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1—6.
Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing Network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate `Edge-Detect' model, which detects Distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring Neural Network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99% even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time.
Abdiyeva-Aliyeva, Gunay, Hematyar, Mehran, Bakan, Sefa.  2021.  Development of System for Detection and Prevention of Cyber Attacks Using Artifıcial Intelligence Methods. 2021 2nd Global Conference for Advancement in Technology (GCAT). :1—5.
Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage with the possibility of having significantly autonomous models that can detect and prevent cyberattacks – even though there still exist some degree of human interventions. AI technologies have been utilized in gathering data which can then be processed into information that are valuable in the prevention of cyberattacks. These AI-based cybersecurity frameworks have commendable scalability about them and are able to detect malicious activities within the cyberspace in a prompter and more efficient manner than conventional security architectures. However, our one or two completed studies did not provide a complete and clear analyses to apply different machine learning algorithms on different media systems. Because of the existing methods of attack and the dynamic nature of malware or other unwanted software (adware etc.) it is important to automatically and systematically create, update and approve malicious packages that can be available to the public. Some of Complex tests have shown that DNN performs maybe can better than conventional machine learning classification. Finally, we present a multiple, large and hybrid DNN torrent structure called Scale-Hybrid-IDS-AlertNet, which can be used to effectively monitor to detect and review the impact of network traffic and host-level events to warn directly or indirectly about cyber-attacks. Besides this, they are also highly adaptable and flexible, with commensurate efficiency and accuracy when it comes to the detection and prevention of cyberattacks.There has been a multiplicity of AI-based cyber security architectures in recent years, and each of these has been found to show varying degree of effectiveness. Deep Neural Networks, which tend to be more complex and even more efficient, have been the major focus of research studies in recent times. In light of the foregoing, the objective of this paper is to discuss the use of AI methods in fighting cyberattacks like malware and DDoS attacks, with attention on DNN-based models.
Sulaga, D Tulasi, Maag, Angelika, Seher, Indra, Elchouemi, Amr.  2021.  Using Deep learning for network traffic prediction to secure Software networks against DDoS attacks. 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA). :1—10.
Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches.
Govindaraj, Logeswari, Sundan, Bose, Thangasamy, Anitha.  2021.  An Intrusion Detection and Prevention System for DDoS Attacks using a 2-Player Bayesian Game Theoretic Approach. 2021 4th International Conference on Computing and Communications Technologies (ICCCT). :319—324.
Distributed Denial-of-Service (DDoS) attacks pose a huge risk to the network and threaten its stability. A game theoretic approach for intrusion detection and prevention is proposed to avoid DDoS attacks in the internet. Game theory provides a control mechanism that automates the intrusion detection and prevention process within a network. In the proposed system, system-subject interaction is modeled as a 2-player Bayesian signaling zero sum game. The game's Nash Equilibrium gives a strategy for the attacker and the system such that neither can increase their payoff by changing their strategy unilaterally. Moreover, the Intent Objective and Strategy (IOS) of the attacker and the system are modeled and quantified using the concept of incentives. In the proposed system, the prevention subsystem consists of three important components namely a game engine, database and a search engine for computing the Nash equilibrium, to store and search the database for providing the optimum defense strategy. The framework proposed is validated via simulations using ns3 network simulator and has acquired over 80% detection rate, 90% prevention rate and 6% false positive alarms.
Mishra, Sarthak, Chatterjee, Pinaki Sankar.  2021.  D3: Detection and Prevention of DDoS Attack Using Cuckoo Filter. 2021 19th OITS International Conference on Information Technology (OCIT). :279—284.
DDoS attacks have grown in popularity as a tactic for potential hackers, cyber blackmailers, and cyberpunks. These attacks have the potential to put a person unconscious in a matter of seconds, resulting in severe economic losses. Despite the vast range of conventional mitigation techniques available today, DDoS assaults are still happening to grow in frequency, volume, and intensity. A new network paradigm is necessary to meet the requirements of today's tough security issues. We examine the available detection and mitigation of DDoS attacks techniques in depth. We classify solutions based on detection of DDoS attacks methodologies and define the prerequisites for a feasible solution. We present a novel methodology named D3 for detecting and mitigating DDoS attacks using cuckoo filter.
Vieira, Alfredo Menezes, Junior, Rubens de Souza Matos, Ribeiro, Admilson de Ribamar Lima.  2021.  Systematic Mapping on Prevention of DDoS Attacks on Software Defined Networks. 2021 IEEE International Systems Conference (SysCon). :1—8.
Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.
He, Gaofeng, Si, Yongrui, Xiao, Xiancai, Wei, Qianfeng, Zhu, Haiting, Xu, Bingfeng.  2021.  Preventing IoT DDoS Attacks using Blockchain and IP Address Obfuscation. 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP). :1—5.
With the widespread deployment of Internet of Things (IoT) devices, hackers can use IoT devices to launch large-scale distributed denial of service (DDoS) attacks, which bring great harm to the Internet. However, how to defend against these attacks remains to be an open challenge. In this paper, we propose a novel prevention method for IoT DDoS attacks based on blockchain and obfuscation of IP addresses. Our observation is that IoT devices are usually resource-constrained and cannot support complicated cryptographic algorithms such as RSA. Based on the observation, we employ a novel authentication then communication mechanism for IoT DDoS attack prevention. In this mechanism, the attack targets' IP addresses are encrypted by a random security parameter. Clients need to be authenticated to obtain the random security parameter and decrypt the IP addresses. In particular, we propose to authenticate clients with public-key cryptography and a blockchain system. The complex authentication and IP address decryption operations disable IoT devices and thus block IoT DDoS attacks. The effectiveness of the proposed method is analyzed and validated by theoretical analysis and simulation experiments.
Kumar, Shubham, Chandavarkar, B.R..  2021.  DDOS prevention in IoT. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). :1—6.
Connecting anything to the Internet is one of the main objectives of the Internet of Things (IoT). It enabled to access any device from anywhere at any time without any human intervention. There are endless applications of IoT involving controlling home applications to industry. This rapid growth of this technology and innovations of its application results due to improved technology of developing these tiny devices with its back-end software. On the other side, internal resources such as memory, processing power, battery life are the significant constraints of these devices. Introducing lightweight cryptography helped secure data transmission across various devices while protecting these devices from getting attacked for DDoS attack is still a significant concern. This paper primarily focuses on elaborating on DDoS attack and the malware used to initiate a DDoS attack on IoT devices. Further, this paper mainly focuses on providing solutions that would help to prevent DDoS attack from IoT network.
Kesavulu, G. Chenna.  2021.  Preventing DDoS attacks in Software Defined Networks. 2021 2nd International Conference on Range Technology (ICORT). :1—4.
In this paper we discuss distributed denial of service attacks on software defined networks, software defined networking is a network architecture approach that enables the network to be intelligently and centrally controlled using software applications. These days the usage of internet is increased because high availability of internet and low cost devices. At the same time lot of security challenges are faced by network monitors and administrators to tackle the frequent network access by the users. The main idea of SDN is to separate the control plane and the data plane, as a result all the devices in the data plane becomes forwarding devices and all the decision making activities transferred to the centralized system called controller. Openflow is the standardized and most important protocol among many SDN protocols. In this article given the overview of distributed denial of service attacks and prevention mechanisms to these malicious attacks.
Gera, Jaideep, Rejeti, Venkata Kishore Kumar, Sekhar, Jaladi N Chandra, Shankar, A Siva.  2021.  Distributed Denial of Service Attack Prevention from Traffic Flow for Network Performance Enhancement. 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). :406—413.
Customer Relationship Management (CRM), Supply Chain Management (SCM), banking, and e-commerce are just a few of the internet-primarily based commercial enterprise programmes that make use of distributed computing generation. These programmes are the principal target of large-scale attacks known as DDoS attacks, which cause the denial of service (DoS) of resources to legitimate customers. Servers that provide dependable services to real consumers in distributed environments are vulnerable to such attacks, which send phoney requests that appear legitimate. Flash crowd, on the other hand, is a massive collection of traffic generated by flash events that imitate Distributed Denial of Service assaults. Detecting and distinguishing between Distributed Denial of Service assaults and flash crowds is a difficult problem to tackle, as is preventing DDoS attacks. Existing solutions are generally intended for DDoS attacks or flash crowds, and more research is required to have a thorough understanding. This study presents a technique for distinguishing between different types of Distributed Denial of Service attacks and Flash Crowds. This research work has suggested an approach to prevent DDOS attacks in addition to detecting and discriminating. The performance of the suggested technique is validated using NS-2 simulations.
Whittle, Cameron S., Liu, Hong.  2021.  Effectiveness of Entropy-Based DDoS Prevention for Software Defined Networks. 2021 IEEE International Symposium on Technologies for Homeland Security (HST). :1—7.
This work investigates entropy-based prevention of Distributed Denial-of-Service (DDoS) attacks for Software Defined Networks (SDN). The experiments are conducted on a virtual SDN testbed setup within Mininet, a Linux-based network emulator. An arms race iterates on the SDN testbed between offense, launching botnet-based DDoS attacks with progressive sophistications, and defense who is deploying SDN controls with emerging technologies from other faucets of cyber engineering. The investigation focuses on the transmission control protocol’s synchronize flood attack that exploits vulnerabilities in the three-way TCP handshake protocol, to lock up a host from serving new users.The defensive strategy starts with a common packet filtering-based design from the literature to mitigate attacks. Utilizing machine learning algorithms, SDNs actively monitor all possible traffic as a collective dataset to detect DDoS attacks in real time. A constant upgrade to a stronger defense is necessary, as cyber/network security is an ongoing front where attackers always have the element of surprise. The defense further invests on entropy methods to improve early detection of DDoS attacks within the testbed environment. Entropy allows SDNs to learn the expected normal traffic patterns for a network as a whole using real time mathematical calculations, so that the SDN controllers can sense the distributed attack vectors building up before they overwhelm the network.This work reveals the vulnerabilities of SDNs to stealthy DDoS attacks and demonstrates the effectiveness of deploying entropy in SDN controllers for detection and mitigation purposes. Future work includes provisions to use these entropy detection methods, as part of a larger system, to redirect traffic and protect networks dynamically in real time. Other types of DoS, such as ransomware, will also be considered.
Dalvi, Jai, Sharma, Vyomesh, Shetty, Ruchika, Kulkarni, Sujata.  2021.  DDoS Attack Detection using Artificial Neural Network. 2021 International Conference on Industrial Electronics Research and Applications (ICIERA). :1—5.
Distributed denial of service (DDoS) attacks is one of the most evolving threats in the current Internet situation and yet there is no effective mechanism to curb it. In the field of DDoS attacks, as in all other areas of cybersecurity, attackers are increasingly using sophisticated methods. The work in this paper focuses on using Artificial Neural Network to detect various types of DDOS attacks(UDP-Flood, Smurf, HTTP-Flood and SiDDoS). We would be mainly focusing on the network and transport layer DDoS attacks. Additionally, the time and space complexity is also calculated to further improve the efficiency of the model implemented and overcome the limitations found in the research gap. The results obtained from our analysis on the dataset show that our proposed methods can better detect the DDoS attack.
Mishra, Anupama, Gupta, B. B., Peraković, Dragan, Peñalvo, Francisco José García, Hsu, Ching-Hsien.  2021.  Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing. 2021 IEEE International Conference on Consumer Electronics (ICCE). :1—4.
Distributed Denial of service attack(DDoS)is a network security attack and now the attackers intruded into almost every technology such as cloud computing, IoT, and edge computing to make themselves stronger. As per the behaviour of DDoS, all the available resources like memory, cpu or may be the entire network are consumed by the attacker in order to shutdown the victim`s machine or server. Though, the plenty of defensive mechanism are proposed, but they are not efficient as the attackers get themselves trained by the newly available automated attacking tools. Therefore, we proposed a classification based machine learning approach for detection of DDoS attack in cloud computing. With the help of three classification machine learning algorithms K Nearest Neighbor, Random Forest and Naive Bayes, the mechanism can detect a DDoS attack with the accuracy of 99.76%.
Alotaibi, Faisal, Lisitsa, Alexei.  2021.  Matrix profile for DDoS attacks detection. 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS). :357—361.
Several previous studies have focused on Distributed Denial of Service (DDoS) attacks, which are a crucial problem in computer network security. In this paper we explore the applicability of a a time series method known as a matrix profile to the anomaly based DDoS attacks detection. The study thus examined how the matrix profile method performed in diverse situations related to DDoS attacks, as well as identifying those features that are most applicable in various scenarios. Based on reported empirical evaluation the matrix profile method is shown to be efficient against most of the considered types of DDoS attacks.
Chu, Hung-Chi, Yan, Chan-You.  2021.  DDoS Attack Detection with Packet Continuity Based on LSTM Model. 2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE). :44—47.
Most information systems rely on the Internet to provide users with various services. Distributed Denial-of-Service (DDoS) attacks are currently one of the main cyber threats, which causes the system or network disabled. To ensure that the information system can provide services for users normally, it is important to detect the occurrence of DDoS attacks quickly and accurately. Therefore, this research proposes a system based on packet continuity to detect DDoS attacks. On average, it only takes a few milliseconds to collect a certain number of consecutive packets, and then DDoS attacks can be detected. Experimental results show that the accuracy of detecting DDoS attacks based on packet continuity is higher than 99.9% and the system response time is about 5 milliseconds.
Bozorov, Suhrobjon.  2021.  DDoS Attack Detection via IDS: Open Challenges and Problems. 2021 International Conference on Information Science and Communications Technologies (ICISCT). :1—4.
This paper discusses DDoS attacks, their current threat level and IDS systems, which are one of the main tools to protect against them. It focuses on the problems encountered by IDS systems in detecting DDoS attacks and the difficulties and challenges of integrating them with artificial intelligence systems today.
Liu, Luo, Jiang, Wang, Li, Jia.  2021.  A CGAN-based DDoS Attack Detection Method in SDN. 2021 International Wireless Communications and Mobile Computing (IWCMC). :1030—1034.
Distributed denial of service (DDoS) attack is a common way of network attack. It has the characteristics of wide distribution, low cost and difficult defense. The traditional algorithms of machine learning (ML) have such shortcomings as excessive systemic overhead and low accuracy in detection of DDoS. In this paper, a CGAN (conditional generative adversarial networks, conditional GAN) -based method is proposed to detect the attack of DDoS. On off-line training, five features are extracted in order to adapt the input of neural network. On the online recognition, CGAN model is adopted to recognize the packets of DDoS attack. The experimental results demonstrate that our proposed method obtains the better performance than the random forest-based method.
Nugraha, Beny, Kulkarni, Naina, Gopikrishnan, Akash.  2021.  Detecting Adversarial DDoS Attacks in Software- Defined Networking Using Deep Learning Techniques and Adversarial Training. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :448—454.
In recent years, Deep Learning (DL) has been utilized for cyber-attack detection mechanisms as it offers highly accurate detection and is able to overcome the limitations of standard machine learning techniques. When applied in a Software-Defined Network (SDN) environment, a DL-based detection mechanism shows satisfying detection performance. However, in the case of adversarial attacks, the detection performance deteriorates. Therefore, in this paper, first, we outline a highly accurate flooding DDoS attack detection framework based on DL for SDN environments. Second, we investigate the performance degradation of our detection framework when being tested with two adversary traffic datasets. Finally, we evaluate three adversarial training procedures for improving the detection performance of our framework concerning adversarial attacks. It is shown that the application of one of the adversarial training procedures can avoid detection performance degradation and thus might be used in a real-time detection system based on continual learning.
Zhou, Yansen, Chen, Qi, Wang, Yumiao.  2021.  Research on DDoS Attack Detection based on Multi-dimensional Entropy. 2021 IEEE 9th International Conference on Computer Science and Network Technology (ICCSNT). :65—69.
DDoS attack detection in a single dimension cannot cope with complex and new attacks. Aiming at the problems existing in single dimension detection, this paper proposes an algorithm to detect DDoS attack based on multi-dimensional entropy. Firstly, the algorithm selects multiple dimensions and establishes corresponding decision function for each dimension and calculates its information entropy. Secondly, the multidimensional sliding window CUSUM algorithm without parameters is used to synthesize the detection results of three dimensions to determine whether it is attacked by DDoS. Finally, the data set published by MIT Lincoln Laboratory is used for testing. Experimental results show that compared with single dimension detection algorithm, this method has good detection rate and low false alarm rate.
Arthi, R, Krishnaveni, S.  2021.  Design and Development of IOT Testbed with DDoS Attack for Cyber Security Research. 2021 3rd International Conference on Signal Processing and Communication (ICPSC). :586—590.
The Internet of Things (IoT) is clubbed by networking of sensors and other embedded electronics. As more devices are getting connected, the vulnerability of getting affected by various IoT threats also increases. Among the IoT threads, DDoS attacks are causing serious issues in recent years. In IoT, these attacks are challenging to detect and isolate. Thus, an effective Intrusion Detection System (IDS) is essential to defend against these attacks. The traditional IDS is based on manual blacklisting. These methods are time-consuming and will not be effective to detect novel intrusions. At present, IDS are automated and programmed to be dynamic which are aided by machine learning & deep learning models. The performance of these models mainly depends on the data used to train the model. Majority of IDS study is performed with non-compatible and outdated datasets like KDD 99 and NSL KDD. Research on specific DDoS attack datasets is very less. Therefore, in this paper, we first aim to examine the effect of existing datasets in the IoT environment. Then, we propose a real-time data collection framework for DNS amplification attacks in IoT. The generated network packets containing DDoS attack is captured through port mirroring.