Visible to the public Biblio

Filters: Keyword is denial-of-service attack  [Clear All Filters]
2021-04-09
Chytas, S. P., Maglaras, L., Derhab, A., Stamoulis, G..  2020.  Assessment of Machine Learning Techniques for Building an Efficient IDS. 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH). :165—170.
Intrusion Detection Systems (IDS) are the systems that detect and block any potential threats (e.g. DDoS attacks) in the network. In this project, we explore the performance of several machine learning techniques when used as parts of an IDS. We experiment with the CICIDS2017 dataset, one of the biggest and most complete IDS datasets in terms of having a realistic background traffic and incorporating a variety of cyber attacks. The techniques we present are applicable to any IDS dataset and can be used as a basis for deploying a real time IDS in complex environments.
Usman, S., Winarno, I., Sudarsono, A..  2020.  Implementation of SDN-based IDS to protect Virtualization Server against HTTP DoS attacks. 2020 International Electronics Symposium (IES). :195—198.
Virtualization and Software-defined Networking (SDN) are emerging technologies that play a major role in cloud computing. Cloud computing provides efficient utilization, high performance, and resource availability on demand. However, virtualization environments are vulnerable to various types of intrusion attacks that involve installing malicious software and denial of services (DoS) attacks. Utilizing SDN technology, makes the idea of SDN-based security applications attractive in the fight against DoS attacks. Network intrusion detection system (IDS) which is used to perform network traffic analysis as a detection system implemented on SDN networks to protect virtualization servers from HTTP DoS attacks. The experimental results show that SDN-based IDS is able to detect and mitigate HTTP DoS attacks effectively.
Fadhilah, D., Marzuki, M. I..  2020.  Performance Analysis of IDS Snort and IDS Suricata with Many-Core Processor in Virtual Machines Against Dos/DDoS Attacks. 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP). :157—162.
The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.
2021-03-17
Kushal, T. R. B., Gao, Z., Wang, J., Illindala, M. S..  2020.  Causal Chain of Time Delay Attack on Synchronous Generator Control. 2020 IEEE Power Energy Society General Meeting (PESGM). :1—5.

Wide integration of information and communication technology (ICT) in modern power grids has brought many benefits as well as the risk of cyber attacks. A critical step towards defending grid cyber security is to understand the cyber-physical causal chain, which describes the progression of intrusion in cyber-space leading to the formation of consequences on the physical power grid. In this paper, we develop an attack vector for a time delay attack at load frequency control in the power grid. Distinct from existing works, which are separately focused on cyber intrusion, grid response, or testbed validation, the proposed attack vector for the first time provides a full cyber-physical causal chain. It targets specific vulnerabilities in the protocols, performs a denial-of-service (DoS) attack, induces the delays in control loop, and destabilizes grid frequency. The proposed attack vector is proved in theory, presented as an attack tree, and validated in an experimental environment. The results will provide valuable insights to develop security measures and robust controls against time delay attacks.

2021-03-09
Memos, V. A., Psannis, K. E..  2020.  AI-Powered Honeypots for Enhanced IoT Botnet Detection. 2020 3rd World Symposium on Communication Engineering (WSCE). :64—68.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

2021-02-23
Aydeger, A., Saputro, N., Akkaya, K..  2020.  Cloud-based Deception against Network Reconnaissance Attacks using SDN and NFV. 2020 IEEE 45th Conference on Local Computer Networks (LCN). :279—285.

An attacker's success crucially depends on the reconnaissance phase of Distributed Denial of Service (DDoS) attacks, which is the first step to gather intelligence. Although several solutions have been proposed against network reconnaissance attacks, they fail to address the needs of legitimate users' requests. Thus, we propose a cloud-based deception framework which aims to confuse the attacker with reconnaissance replies while allowing legitimate uses. The deception is based on for-warding the reconnaissance packets to a cloud infrastructure through tunneling and SDN so that the returned IP addresses to the attacker will not be genuine. For handling legitimate requests, we create a reflected virtual topology in the cloud to match any changes in the original physical network to the cloud topology using SDN. Through experimentations on GENI platform, we show that our framework can provide reconnaissance responses with negligible delays to the network clients while also reducing the management costs significantly.

2021-02-22
Li, Y., Liu, Y., Wang, Y., Guo, Z., Yin, H., Teng, H..  2020.  Synergetic Denial-of-Service Attacks and Defense in Underwater Named Data Networking. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1569–1578.
Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.
2021-02-16
Li, R., Wu, B..  2020.  Early detection of DDoS based on φ-entropy in SDN networks. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:731—735.
Software defined network (SDN) is an emerging network architecture. Its control logic and forwarding logic are separated. SDN has the characteristics of centralized management, which makes it easier for malicious attackers to use the security vulnerabilities of SDN networks to implement distributed denial Service (DDoS) attack. Information entropy is a kind of lightweight DDoS early detection method. This paper proposes a DDoS attack detection method in SDN networks based on φ-entropy. φ-entropy can adjust related parameters according to network conditions and enlarge feature differences between normal and abnormal traffic, which can make it easier to detect attacks in the early stages of DDoS traffic formation. Firstly, this article demonstrates the basic properties of φ-entropy, mathematically illustrates the feasibility of φ-entropy in DDoS detection, and then we use Mini-net to conduct simulation experiments to compare the detection effects of DDoS with Shannon entropy.
Başkaya, D., Samet, R..  2020.  DDoS Attacks Detection by Using Machine Learning Methods on Online Systems. 2020 5th International Conference on Computer Science and Engineering (UBMK). :52—57.
DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.
Sumantra, I., Gandhi, S. Indira.  2020.  DDoS attack Detection and Mitigation in Software Defined Networks. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—5.
This work aims to formulate an effective scheme which can detect and mitigate of Distributed Denial of Service (DDoS) attack in Software Defined Networks. Distributed Denial of Service attacks are one of the most destructive attacks in the internet. Whenever you heard of a website being hacked, it would have probably been a victim of a DDoS attack. A DDoS attack is aimed at disrupting the normal operation of a system by making service and resources unavailable to legitimate users by overloading the system with excessive superfluous traffic from distributed source. These distributed set of compromised hosts that performs the attack are referred as Botnet. Software Defined Networking being an emerging technology, offers a solution to reduce network management complexity. It separates the Control plane and the data plane. This decoupling provides centralized control of the network with programmability and flexibility. This work harness this programming ability and centralized control of SDN to obtain the randomness of the network flow data. This statistical approach utilizes the source IP in the network and various attributes of TCP flags and calculates entropy from them. The proposed technique can detect volume based and application based DDoS attacks like TCP SYN flood, Ping flood and Slow HTTP attacks. The methodology is evaluated through emulation using Mininet and Detection and mitigation strategies are implemented in POX controller. The experimental results show the proposed method have improved performance evaluation parameters including the Attack detection time, Delay to serve a legitimate request in the presence of attacker and overall CPU utilization.
Abdulkarem, H. S., Dawod, A..  2020.  DDoS Attack Detection and Mitigation at SDN Data Plane Layer. 2020 2nd Global Power, Energy and Communication Conference (GPECOM). :322—326.
In the coming future, Software-defined networking (SDN) will become a technology more responsive, fully automated, and highly secure. SDN is a way to manage networks by separate the control plane from the forwarding plane, by using software to manage network functions through a centralized control point. A distributed denial-of-service (DDoS) attack is the most popular malicious attempt to disrupt normal traffic of a targeted server, service, or network. The problem of the paper is the DDoS attack inside the SDN environment and how could use SDN specifications through the advantage of Open vSwitch programmability feature to stop the attack. This paper presents DDoS attack detection and mitigation in the SDN data-plane by applying a written SDN application in python language, based on the malicious traffic abnormal behavior to reduce the interference with normal traffic. The evaluation results reveal detection and mitigation time between 100 to 150 sec. The work also sheds light on the programming relevance with the open daylight controller over an abstracted view of the network infrastructure.
He, J., Tan, Y., Guo, W., Xian, M..  2020.  A Small Sample DDoS Attack Detection Method Based on Deep Transfer Learning. 2020 International Conference on Computer Communication and Network Security (CCNS). :47—50.
When using deep learning for DDoS attack detection, there is a general degradation in detection performance due to small sample size. This paper proposes a small-sample DDoS attack detection method based on deep transfer learning. First, deep learning techniques are used to train several neural networks that can be used for transfer in DDoS attacks with sufficient samples. Then we design a transferability metric to compare the transfer performance of different networks. With this metric, the network with the best transfer performance can be selected among the four networks. Then for a small sample of DDoS attacks, this paper demonstrates that the deep learning detection technique brings deterioration in performance, with the detection performance dropping from 99.28% to 67%. Finally, we end up with a 20.8% improvement in detection performance by deep transfer of the 8LANN network in the target domain. The experiment shows that the detection method based on deep transfer learning proposed in this paper can well improve the performance deterioration of deep learning techniques for small sample DDoS attack detection.
Yeom, S., Kim, K..  2020.  Improving Performance of Collaborative Source-Side DDoS Attack Detection. 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS). :239—242.
Recently, as the threat of Distributed Denial-of-Service attacks exploiting IoT devices has spread, source-side Denial-of-Service attack detection methods are being studied in order to quickly detect attacks and find their locations. Moreover, to mitigate the limitation of local view of source-side detection, a collaborative attack detection technique is required to share detection results on each source-side network. In this paper, a new collaborative source-side DDoS attack detection method is proposed for detecting DDoS attacks on multiple networks more correctly, by considering the detecting performance on different time zone. The results of individual attack detection on each network are weighted based on detection rate and false positive rate corresponding to the time zone of each network. By gathering the weighted detection results, the proposed method determines whether a DDoS attack happens. Through extensive evaluation with real network traffic data, it is confirmed that the proposed method reduces false positive rate by 35% while maintaining high detection rate.
Zhai, P., Song, Y., Zhu, X., Cao, L., Zhang, J., Yang, C..  2020.  Distributed Denial of Service Defense in Software Defined Network Using OpenFlow. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :1274—1279.
Software Defined Network (SDN) is a new type of network architecture solution, and its innovation lies in decoupling traditional network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the development trend of the network in the future. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats.This paper focuses on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to changes of the network. Second, to mitigate this threat, we analyze the historical behavior of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source.The experimental results show that the scheme studied in this paper can effectively detect and mitigate DDoS attacks.
2021-01-25
Zhang, T.-Y., Ye, D..  2020.  Distributed Secure Control Against Denial-of-Service Attacks in Cyber-Physical Systems Based on K-Connected Communication Topology. IEEE Transactions on Cybernetics. 50:3094–3103.
In this article, the security problem in cyber-physical systems (CPSs) against denial-of-service (DoS) attacks is studied from the perspectives of the designs of communication topology and distributed controller. To resist the DoS attacks, a new construction algorithm of the k-connected communication topology is developed based on the proposed necessary and sufficient criteria of the k-connected graph. Furthermore, combined with the k-connected topology, a distributed event-triggered controller is designed to guarantee the consensus of CPSs under mode-switching DoS (MSDoS) attacks. Different from the existing distributed control schemes, a new technology, that is, the extended Laplacian matrix method, is combined to design the distributed controller independent on the knowledge and the dwell time of DoS attack modes. Finally, the simulation example illustrates the superiority and effectiveness of the proposed construction algorithm and a distributed control scheme.
2020-12-01
Di, A., Ruisheng, S., Lan, L., Yueming, L..  2019.  On the Large-Scale Traffic DDoS Threat of Space Backbone Network. 2019 IEEE 5th 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). :192—194.

Satellite networks play an important role in realizing the combination of the space networks and ground networks as well as the global coverage of the Internet. However, due to the limitation of bandwidth resource, compared with ground network, space backbone networks are more likely to become victims of DDoS attacks. Therefore, we hypothesize an attack scenario that DDoS attackers make reflection amplification attacks, colluding with terminal devices accessing space backbone network, and exhaust bandwidth resources, resulting in degradation of data transmission and service delivery. Finally, we propose some plain countermeasures to provide solutions for future researchers.

2020-10-29
Chauhan, Gargi K, Patel, Saurabh M.  2018.  Public String Based Threshold Cryptography (PSTC) for Mobile Ad Hoc Networks (MANET). 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). :1—5.
Communication is an essential part of everyday life, both as a social interaction and collaboration to achieve goals. Wireless technology has effectively release the users to roam more freely to achieving collaboration and communication. The principle attraction of mobile ad hoc networks (MANET) are their set-up less and decentralized action. However, mobile ad hoc networks are seen as relatively easy targets for attackers. Security in mobile ad hoc network is provided by encrypting the data when exchanging messages and key management. Cryptography is therefore vital to ensure privacy of message and robustness against disruption. The proposed scheme public string based threshold cryptography (PSTC) describes the new scheme based on threshold cryptography that provides reasonably secure and robust cryptography scheme for mobile ad hoc networks. The scheme is implemented and simulated in ns-2. The scheme is based on trust value and analyze against Denial of Service attack as node found the attacker, the node reject all packet from that attacker. In proposed scheme whole network is compromised only when all nodes of network is compromised because threshold nodes only sharing public string not the master private key. The scheme provides confidentiality and integrity. The default threshold value selected is 2 according to time and space analysis.
2020-10-06
Kalwar, Abhishek, Bhuyan, Monowar H., Bhattacharyya, Dhruba K., Kadobayashi, Youki, Elmroth, Erik, Kalita, Jugal K..  2019.  TVis: A Light-weight Traffic Visualization System for DDoS Detection. 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). :1—6.

With rapid growth of network size and complexity, network defenders are facing more challenges in protecting networked computers and other devices from acute attacks. Traffic visualization is an essential element in an anomaly detection system for visual observations and detection of distributed DoS attacks. This paper presents an interactive visualization system called TVis, proposed to detect both low-rate and highrate DDoS attacks using Heron's triangle-area mapping. TVis allows network defenders to identify and investigate anomalies in internal and external network traffic at both online and offline modes. We model the network traffic as an undirected graph and compute triangle-area map based on incidences at each vertex for each 5 seconds time window. The system triggers an alarm iff the system finds an area of the mapped triangle beyond the dynamic threshold. TVis performs well for both low-rate and high-rate DDoS detection in comparison to its competitors.

2020-09-04
Asish, Madiraju Sairam, Aishwarya, R..  2019.  Cyber Security at a Glance. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:240—245.
The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.
2020-08-24
Sophakan, Natnaree, Sathitwiriyawong, Chanboon.  2019.  A Secured OpenFlow-Based Software Defined Networking Using Dynamic Bayesian Network. 2019 19th International Conference on Control, Automation and Systems (ICCAS). :1517–1522.
OpenFlow has been the main standard protocol of software defined networking (SDN) since the launch of this new networking paradigm. It is a programmable network protocol that controls traffic flows among switches and routers regardless of their platforms. Its security relies on the optional implementation of Transport Layer Security (TLS) which has been proven vulnerable. The aim of this research was to develop a secured OpenFlow, so-called Secured-OF. A stateful firewall was used to store state information for further analysis. Dynamic Bayesian Network (DBN) was used to learn denial-of-service attack and distributed denial-of-service attack. It analyzes packet states to determine the nature of an attack and adds that piece of information to the flow table entry. The proposed Secured-OF model in Ryu controller was evaluated with several performance metrics. The analytical evaluation of the proposed Secured-OF scheme was performed on an emulated network. The results showed that the proposed Secured-OF scheme offers a high attack detection accuracy at 99.5%. In conclusion, it was able to improve the security of the OpenFlow controller dramatically with trivial performance degradation compared to an SDN with no security implementation.
2020-07-16
Ni, Ming, Xue, Yusheng, Tong, Heqin, Li, Manli.  2018.  A cyber physical power system co-simulation platform. 2018 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). :1—5.

With the tighter integration of power system and Information and Communication Technology (ICT), power grid is becoming a typical cyber physical system (CPS). It is important to analyze the impact of the cyber event on power system, so that it is necessary to build a co-simulation system for studying the interaction between power system and ICT. In this paper, a cyber physical power system (CPPS) co-simulation platform is proposed, which includes the hardware-in-the-loop (HIL) simulation function. By using flexible interface, various simulation software for power system and ICT can be interconnected into the platform to build co-simulation tools for various simulation purposes. To demonstrate it as a proof, one simulation framework for real life cyber-attack on power system control is introduced. In this case, the real life denial-of-service attack on a router in automatic voltage control (AVC) is simulated to demonstrate impact of cyber-attack on power system.

2020-06-29
Luo, Wenliang, Han, Wenzhi.  2019.  DDOS Defense Strategy in Software Definition Networks. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :186–190.
With the advent of the network economy and the network society, the network will enter a ubiquitous and omnipresent situation. Economic, cultural, military and social life will strongly depend on the network, while network security issues have become a common concern of all countries in the world. DDOS attack is undoubtedly one of the greatest threats to network security and the defense against DDOS attack is very important. In this paper, the principle of DDOS attack is summarized from the defensive purpose. Then the attack prevention in software definition network is analyzed, and the source, intermediate network, victim and distributed defense strategies are elaborated.
Kaljic, Enio, Maric, Almir, Njemcevic, Pamela.  2019.  DoS attack mitigation in SDN networks using a deeply programmable packet-switching node based on a hybrid FPGA/CPU data plane architecture. 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT). :1–6.
The application of the concept of software-defined networks (SDN) has, on the one hand, led to the simplification and reduction of switches price, and on the other hand, has created a significant number of problems related to the security of the SDN network. In several studies was noted that these problems are related to the lack of flexibility and programmability of the data plane, which is likely first to suffer potential denial-of-service (DoS) attacks. One possible way to overcome this problem is to increase the flexibility of the data plane by increasing the depth of programmability of the packet-switching nodes below the level of flow table management. Therefore, this paper investigates the opportunity of using the architecture of deeply programmable packet-switching nodes (DPPSN) in the implementation of a firewall. Then, an architectural model of the firewall based on a hybrid FPGA/CPU data plane architecture has been proposed and implemented. Realized firewall supports three models of DoS attacks mitigation: DoS traffic filtering on the output interface, DoS traffic filtering on the input interface, and DoS attack redirection to the honeypot. Experimental evaluation of the implemented firewall has shown that DoS traffic filtering at the input interface is the best strategy for DoS attack mitigation, which justified the application of the concept of deep network programmability.
Rahman, Md. Mahmudur, Roy, Shanto, Yousuf, Mohammad Abu.  2019.  DDoS Mitigation and Intrusion Prevention in Content Delivery Networks using Distributed Virtual Honeypots. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). :1–6.

Content Delivery Networks(CDN) is a standout amongst the most encouraging innovations that upgrade performance for its clients' websites by diverting web demands from browsers to topographically dispersed CDN surrogate nodes. However, due to the variable nature of CDN, it suffers from various security and resource allocation issues. The most common attack which is used to bring down a whole network as well as CDN without even finding a loophole in the security is DDoS. In this proposal, we proposed a distributed virtual honeypot model for diminishing DDoS attacks and prevent intrusion in securing CDN. Honeypots are specially utilized to imitate the primary server with the goal that the attack is alleviated to the fake rather than the main server. Our proposed layer based model utilizes honeypot to be more effective reducing the cost of the system as well as maintaining the smooth delivery in geographically dispersed servers without performance degradation.

Ahalawat, Anchal, Dash, Shashank Sekhar, Panda, Abinas, Babu, Korra Sathya.  2019.  Entropy Based DDoS Detection and Mitigation in OpenFlow Enabled SDN. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1–5.
Distributed Denial of Service(DDoS) attacks have become most important network security threat as the number of devices are connected to internet increases exponentially and reaching an attack volume approximately very high compared to other attacks. To make the network safe and flexible a new networking infrastructure such as Software Defined Networking (SDN) has come into effect, which relies on centralized controller and decoupling of control and data plane. However due to it's centralized controller it is prone to DDoS attacks, as it makes the decision of forwarding of packets based on rules installed in switch by OpenFlow protocol. Out of all different DDoS attacks, UDP (User Datagram Protocol) flooding constitute the most in recent years. In this paper, we have proposed an entropy based DDoS detection and rate limiting based mitigation for efficient service delivery. We have evaluated using Mininet as emulator and Ryu as controller by taking switch as OpenVswitch and obtained better result in terms of bandwidth utilization and hit ratio which consume network resources to make denial of service.