Mitigate Distributed Denial Of Service Attacks Research Articles

Mitigation of DDOS and MiTM Attacks using Belief Based Secure Correlation Approach in SDN-Based IoT Networks

In recent years the domain of Internet of Things (IoT) has acquired great interest from the ICT community. Environmental observation and collecting information is one of the key reasons that IoT infrastructure facilitates the creation of many varieties of the latest business methods and applications. There are however still issues about security measures to be resolved to ensure adequate operation of devices. Distributed Denial of Service (DDoS) attacks are currently the most severe virtual threats that are causing serious damage to many IoT devices. With this in mind, numerous research projects were carried out to discover new methods and develop Novel techniques and solutions for DDOS attacks prevention. The use of new technology, such as software-defined networking (SDN) along with IoT devices has proven to be an innovative solution to mitigate DDoS attacks. In this article, we are using a novel data sharing system in IoT units that link IoT units with the SDN controller and encrypt information from IoT unit. We use conventional Redstone cryptographic algorithms to encrypt information from IoT devices in this framework. The Proposed Belief Based Secure Correlation methodology supports the prevention of DDOS attacks and other forms of data attacks. The system proposes new routes for transmission through the controller and communicates with approved switches for the safe transmission of data. To simulate our entire scenario, we proposed the algorithm Belief Based Secure Correlation (BBSC) implemented in SDN–IoT Testbed and verified IoT data is secure during transmission in the network.

Mitigating DDoS Attacks in SDN-Based IoT Networks Leveraging Secure Control and Data Plane Algorithm

Software-Defined Networking (SDN) and Internet of Things (IoT) are the trends of network evolution. SDN mainly focuses on the upper level control and management of networks, while IoT aims to bring devices together to enable sharing and monitoring of real-time behaviours through network connectivity. On the one hand, IoT enables us to gather status of devices and networks and to control them remotely. On the other hand, the rapidly growing number of devices challenges the management at the access and backbone layer and raises security concerns of network attacks, such as Distributed Denial of Service (DDoS). The combination of SDN and IoT leads to a promising approach that could alleviate the management issue. Indeed, the flexibility and programmability of SDN could help in simplifying the network setup. However, there is a need to make a security enhancement in the SDN-based IoT network for mitigating attacks involving IoT devices. In this article, we discuss and analyse state-of-the-art DDoS attacks under SDN-based IoT scenarios. Furthermore, we verify our SDN sEcure COntrol and Data plane (SECOD) algorithm to resist DDoS attacks on the real SDN-based IoT testbed. Our results demonstrate that DDoS attacks in the SDN-based IoT network are easier to detect than in the traditional network due to IoT traffic predictability. We observed that random traffic (UDP or TCP) is more affected during DDoS attacks. Our results also show that the probability of a controller becoming halt is 10%, while the probability of a switch getting unresponsive is 40%.

A Novel Feature-Based DDoS Detection and Mitigation Scheme in SDN Controller Using Queueing Theory

Software defined network (SDN) has attracted great interests as an emergent paradigm which aims to centralize the configuration of network devices by decoupling control layer and data layer. One considerable challenge in SDN is to protect against multiple attacks generated by distributed denial of service (DDoS) bots which attempt to make SDN controllers unavailable. The goal of this research is to propose a novel detect and mitigate DDoS attack in SDN controllers using traffic monitoring. Besides the advantages of queueing theory based model is exploited to evaluate the arrival flows and leveraging robust features and entropy, a distance-based classification is designed accurately to detect malicious packets from legitimate packets. The experimental results vividly demonstrate that our proposed detection scheme effectively yields high accuracy as well as high-efficiency controller utilization.

Detection and mitigation of attacks in SDN-based IoT network using SVM

Adapting Software Defined Networking (SDN) raises many challenges including scalability and security on the Internet of Things (IoT) network. Growing network size increases resulting in the network load in the SDN controller and facing security challenges. Distributed Denial of Service (DDoS) attacks are among the most acute threats in the present scenario. The attack scenario and security of multiple controller networks are simulated and evaluated in this research. Simulation has been conducted in Mininet-SDN emulator, hosting OS was Ubuntu Linux, Wireshark was used for analysing the network traffic, support vector machines were used to classify the traffic flows. DDoS attacks were detected, and mitigation has been done using a support vector machine learning-based approach. The results show that the support vector machine's sensitivity, specificity and accuracy are excellent in the range of 98.7% to 98.8%. Security solutions are fast and effective in mitigating DDoS attacks.

Detection and mitigation of attacks in SDN-based IoT network using SVM

Adapting Software Defined Networking (SDN) raises many challenges including scalability and security on the Internet of Things (IoT) network. Growing network size increases resulting in the network load in the SDN controller and facing security challenges. Distributed Denial of Service (DDoS) attacks are among the most acute threats in the present scenario. The attack scenario and security of multiple controller networks are simulated and evaluated in this research. Simulation has been conducted in Mininet-SDN emulator, hosting OS was Ubuntu Linux, Wireshark was used for analysing the network traffic, support vector machines were used to classify the traffic flows. DDoS attacks were detected, and mitigation has been done using a support vector machine learning-based approach. The results show that the support vector machine's sensitivity, specificity and accuracy are excellent in the range of 98.7% to 98.8%. Security solutions are fast and effective in mitigating DDoS attacks.

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial‐of‐service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software‐Defined Networking (SDN) controller and Neural Network Self‐Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.

Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks

Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet. With billions of such devices already in the market which have significant vulnerabilities, there is a dangerous threat to the Internet services and also some cyber-physical systems that are also connected to the Internet. Specifically, due to their existing vulnerabilities IoT devices are susceptible to being compromised and being part of a new type of stealthy Distributed Denial of Service (DDoS) attack, called Mongolian DDoS, which is characterized by its widely distributed nature and small attack size from each source. This article proposes a novel anomaly-based Intrusion Detection System (IDS) that is capable of timely detecting and mitigating this emerging type of DDoS attacks. The proposed IDS’s capability of detecting and mitigating stealthy DDoS attacks with even very low attack size per source is demonstrated through numerical and testbed experiments.

Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.

An SDN-Enabled Proactive Defense Framework for DDoS Mitigation in IoT Networks

The Internet of Things (IoT) is becoming truly ubiquitous in every domain of human lives, and a large number of objects can be connected and enabled to communicate with cloud servers at any time. However, complex connections and vulnerabilities of IoT devices introduce inevitable security threats, in which distributed denial-of-service (DDoS) attacks usually incur catastrophic results. Unfortunately, the existing DDoS mitigation methods cannot provide effective protection. Moreover, the amplifying complexity and increasing delay incurred by defense greatly affect the stability of IoT networks. To tackle these problems, we present a novel framework that can proactively adapt the attack surface of IoT networks, dynamically optimize defense strategies, and rapidly deploy the corresponding defense mechanisms. In particular, we establish hybrid proactive defense mechanisms combining Moving Target Defense (MTD) techniques with cyber deception to spread camouflage information to confuse attackers. Based on these mechanisms, we introduce a defender-led signaling game model to formalize defense scenarios and depict the interactions between the defender and the attacker. Besides, we present an optimal algorithm to solve decision problems and optimize defense implementation in a cost-effective manner. Our extensive experiments demonstrate that the proposed approach can effectively mitigate DDoS attacks and maintain a high level of performance in IoT networks with acceptable overhead.

Separating Monitoring from Control in SDN to Mitigate DDoS Attacks in Hybrid Clouds

Background & Objective: Detecting and mitigating Distributed Denial of Service (DDoS) attacks is a serious problem. In addition, new features and network deployments such as Software- Defined Networking (SDN) may open the door for new threats that did not previously exist. : Recent publications and patent are reviewed to find new techniques developed for integrating different mechanisms to secure networks against DDoS. Methods: This work presents a simple model for integrating different mechanisms to secure both SDN and legacy network in a hybrid cloud environment, it is called FocusON. It aims at mitigating DDoS attacks of a victim network. In addition, separating network monitoring from its control aims at mitigating DDoS attacks of a victim network. Traffic pattern analysis is apart from attack detection mechanism that gives a conceptual representation of a specific kind of DDoS attacks. DDoS detection is a completely automated process. Once called, for the reaction, the active response will be taken against the real IP source of the attacker. : The communication time overhead was tested in order to evaluate the remote server response time in case of deploying our proposed model mechanisms and without our proposed model. : Here we introduce a response mechanism that consists of an analysis of event logs, traffic patterns, and IP traceback. The proposed model categorizes the underlying network according to the location into a victim network and the source of attack (public cloud). Results & Conclusion:: The proposed model implemented in a hybrid cloud environment using the network of SDN and legacy network. The experimental setup was built using our network lab connected to the Amazon public cloud.

FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks

Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people’s activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.

Bringing Intelligence to Software Defined Networks: Mitigating DDoS Attacks

As one of the most devastating types of Distributed Denial of Service (DDoS) attacks, Domain Name System (DNS) amplification attack represents a big threat and one of the main Internet security problems to nowadays networks. Many protocols that form the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. DNS, one of the most critical elements of the Internet, is among these protocols. It is vulnerable to DDoS attacks mainly because all exchanges in this protocol use User Datagram Protocol (UDP). These attacks are difficult to defeat because attackers spoof the IP address of the victim and flood him with valid DNS responses coming from legitimate DNS servers. In this paper, we propose an efficient and scalable solution, called WisdomSDN, to effectively mitigate DNS amplification attack in the context of software defined networks (SDN). WisdomSDN covers both detection and mitigation of illegitimate DNS requests and responses. WisdomSDN consists of: (1) a novel proactive and stateful scheme (PAS) to perform one-to-one mapping between DNS requests and DNS responses; it operates proactively by sending only legitimate responses, excluding amplified illegitimate DNS responses; (2) a machine learning DDoS detection module to detect, in real-time, illegitimate DNS requests. This module consists of (a) Flow statistics collection scheme (FSC) to gather the features of flows in an efficient and scalable way using sFlow protocol; (b) Entropy calculation scheme (ECS) to measure randomness of network traffic; and (c) Bayes Network based Filtering scheme (BNF) to classify, based on entropy values, illegitimate DNS requests; and (3) DNS Mitigation scheme (DM) to effectively mitigate illegitimate DNS requests. The experimental results show that, compared to state-of-art, WisdomSDN can effectively detect/mitigate DNS amplification attack quickly with high detection rate, less false positive rate, and low overhead making it a promising solution to mitigate DNS amplification attack in a SDN environment.

Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

Neural Network-Based Approach for Detection and Mitigation of DDoS Attacks in SDN Environments

Software defined networking (SDN) is a networking paradigm that allows for the easy programmability of network devices by decoupling the data plane and the control plane. On the other hand, Distributed Denial of Service (DDoS) attacks remains one of the major concerns for organizational network infrastructures and Cloud providers. In this article, the authors propose a Neural Network based Traffic Flow Classifier (TFC-NN) for live DDoS detection in SDN environments. This study provides a live traffic analysis method with a neural network. The training of the TFC-NN model is performed by a labelled dataset constructed from SDN normal traffic and an-under DDoS traffic. The study also provides a live mitigation process combined with the live TFC-NN-based DDoS detection. The approach is deployed and evaluated on an SDN architecture based on different performance metrics with different under-DDoS attack scenarios.

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.

SNORT based early DDoS detection system using Opendaylight and open networking operating system in software defined networking

Software-defined networking (SDN) is an approach in the network that provides many advantages with the help of separating the intelligence of the network (controller) with the underlying network infrastructure (data plane). But this isolation also gives birth to many security concerns; therefore, the need to protect the network from various attacks is becoming mandatory. Distributed Denial of Service (DDoS) in SDN is one such attack that is becoming a hurdle to its growth. Before the mitigation of DDoS attacks, the primary step is to detect them. In this paper, an early DDoS detection tool is created by using SNORT IDS (Intrusion Detection System). This tool is integrated with popularly used SDN controllers (Opendaylight and Open Networking Operating System). For the experimental setup, five different network scenarios are considered. In each scenario number of hosts, switches and data packets vary. For the creation of different hosts, switches the Mininet emulation tool is used whereas for generating the data packets four different penetration tools such as Hping3, Nping, Xerxes, Tor Hammer, LOIC are used. The generated data packets are ranging from (50,000 per second–2,50,000 per second) and the number of hosts/switches are ranging from (50–250) in every scenario respectively. The data traffic is bombarded towards the controllers and the evaluation of these packets is achieved by making use of Wireshark. The analysis of our DDoS detection system is performed on the basis of various parameters such as time to detect the DDoS attack, Round Trip Time (RTT), percentage of packet loss and type of DDoS attack. It is found that ODL takes minimum time to detect the successful DDoS attack and more time to go down than ONOS. Our tool ensures the timely detection of fast DDoS attacks which delivers the better performance of the SDN controller and not compromising the overall functionality of the entire network.

A DDoS Attack Mitigation Scheme in ISP Networks Using Machine Learning Based on SDN

Keeping Internet users protected from cyberattacks and other threats is one of the most prominent security challenges for network operators nowadays. Among other critical threats, distributed denial-of-service (DDoS) becomes one of the most widespread attacks in the Internet, which is very challenging to mitigate appropriately as DDoS attacks cause the system to stop working by resource exhaustion. Software-defined networking (SDN) has recently emerged as a new networking technology offering unprecedented programmability that allows network operators to configure and manage their infrastructures dynamically. The flexible processing and centralized management of the SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a novel DDoS attack mitigation in SDN-based Internet Service Provider (ISP) networks for TCP-SYN and ICMP flood attacks utilizing machine learning approach, i.e., K-Nearest-Neighbor (KNN) and XGBoost. By deploying a testbed, we implement the proposed algorithms, evaluate their accuracy, and address the trade-off between the accuracy and mitigation efficiency. Through extensive experiments, the results show that the algorithms can efficiently mitigate the attack by over 98.0% while benign traffic is not affected.

Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN : An Experimental Approach.

The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.

MSIDN: Mitigation of Sophisticated Interest flooding-based DDoS attacks in Named Data Networking

Named Data Networking (NDN) is a promising candidate for Future Internet Architecture (FIA), where the focus of communication is the content itself rather than the source of the requested content. NDN is one of the implementations of Information-Centric Networking (ICN). Among other salient features, NDN provides intrinsic security where security is provided to the content directly, rather than securing the communication channel. However, despite promising features offered by NDN, it is still susceptible to various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). Various mitigation solutions exist in the literature; however, legitimate users and their traffic are usually affected by these solutions. In this paper, we propose a lightweight mechanism called MSIDN, to mitigate sophisticated interest flooding-based DoS and Distributed DoS (DDoS) attacks in NDN. MSIDN aims to mitigate attacks at the source of communication without affecting the legitimate users. MSIDN relies on data producers’ feedback which is used by the routers to employ precise rate-limiting and block the attackers. Extensive simulations were conducted to evaluate the proposed MSIDN in terms of its robustness during various attack scenarios, dealing with malicious traffic without affecting the legitimate requests, and mitigating attacks at the source side of the communication.

A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can largely damage the availability of the cloud services and can be effectively initiated by utilizing different tools, prompting financial harm or influencing the reputation. Consequently, there is a requirement for a more grounded and general approach to block these attacks. This paper proposes the use of artificial immune systems to alleviate DDoS attacks in cloud computing by identifying the most potential features of the attack. This methodology is capable of detecting threats and responding according to the behavior of the biological resistance mechanism in human beings. It is carried out by emulating the various immune reactions and the construction of the intrusion detection system. For the assessment, experiments with public domain datasets (KDD cup 99) were implemented. Based on broad theoretical and performance analysis, the proposed system is capable to identify the anomalous entries with high detection accuracy and low false alarm rate.