Visible to the public Biblio

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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.
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.
2021-03-29
Chauhan, R., Heydari, S. Shah.  2020.  Polymorphic Adversarial DDoS attack on IDS using GAN. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.
Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks.
2021-03-09
Lee, T., Chang, L., Syu, C..  2020.  Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

2021-02-16
Wang, L., Liu, Y..  2020.  A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:1084—1088.
Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.
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.
2021-02-03
Ceron, J. M., Scholten, C., Pras, A., Santanna, J..  2020.  MikroTik Devices Landscape, Realistic Honeypots, and Automated Attack Classification. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.

In 2018, several malware campaigns targeted and succeed to infect millions of low-cost routers (malwares e.g., VPN-Filter, Navidade, and SonarDNS). These routers were used, then, for all sort of cybercrimes: from DDoS attacks to ransomware. MikroTik routers are a peculiar example of low-cost routers. These routers are used to provide both last mile access to home users and are used in core network infrastructure. Half of the core routers used in one of the biggest Internet exchanges in the world are MikroTik devices. The problem is that vulnerable firmwares (RouterOS) used in homeusers houses are also used in core networks. In this paper, we are the first to quantify the problem that infecting MikroTik devices would pose to the Internet. Based on more than 4 TB of data, we reveal more than 4 million MikroTik devices in the world. Then, we propose an easy-to-deploy MikroTik honeypot and collect more than 17 millions packets, in 45 days, from sensors deployed in Australia, Brazil, China, India, Netherlands, and the United States. Finally, we use the collected data from our honeypots to automatically classify and assess attacks tailored to MikroTik devices. All our source-codes and analysis are publicly available. We believe that our honeypots and our findings in this paper foster security improvements in MikroTik devices worldwide.

2020-12-14
Kyaw, A. T., Oo, M. Zin, Khin, C. S..  2020.  Machine-Learning Based DDOS Attack Classifier in Software Defined Network. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :431–434.
Due to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
2020-11-23
Ramapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K..  2019.  Anomaly Detection Models for Smart Home Security. 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). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.
2020-11-02
Siddiqui, Abdul Jabbar, Boukerche, Azzedine.  2018.  On the Impact of DDoS Attacks on Software-Defined Internet-of-Vehicles Control Plane. 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC). :1284—1289.

To enhance the programmability and flexibility of network and service management, the Software-Defined Networking (SDN) paradigm is gaining growing attention by academia and industry. Motivated by its success in wired networks, researchers have recently started to embrace SDN towards developing next generation wireless networks such as Software-Defined Internet of Vehicles (SD-IoV). As the SD-IoV evolves, new security threats would emerge and demand attention. And since the core of the SD-IoV would be the control plane, it is highly vulnerable to Distributed Denial of Service (DDoS) Attacks. In this work, we investigate the impact of DDoS attacks on the controllers in a SD-IoV environment. Through experimental evaluations, we highlight the drastic effects DDoS attacks could have on a SD-IoV in terms of throughput and controller load. Our results could be a starting point to motivate further research in the area of SD-IoV security and would give deeper insights into the problems of DDoS attacks on SD-IoV.

2020-09-28
Killer, Christian, Rodrigues, Bruno, Stiller, Burkhard.  2019.  Security Management and Visualization in a Blockchain-based Collaborative Defense. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :108–111.
A cooperative network defense is one approach to fend off large-scale Distributed Denial-of-Service (DDoS) attacks. In this regard, the Blockchain Signaling System (BloSS) is a multi-domain, blockchain-based, cooperative DDoS defense system, where each Autonomous System (AS) is taking part in the defense alliance. Each AS can exchange attack information about ongoing attacks via the Ethereum blockchain. However, the currently operational implementation of BloSS is not interactive or visualized, but the DDoS mitigation is automated. In realworld defense systems, a human cybersecurity analyst decides whether a DDoS threat should be mitigated or not. Thus, this work presents the design of a security management dashboard for BloSS, designed for interactive use by cyber security analysts.
2020-09-11
Al-Ghushami, Abdullah, Karie, NIckson, Kebande, Victor.  2019.  Detecting Centralized Architecture-Based Botnets using Travelling Salesperson Non-Deterministic Polynomial-Hard problem-TSP-NP Technique. 2019 IEEE Conference on Application, Information and Network Security (AINS). :77—81.
The threats posed by botnets in the cyber-space continues to grow each day and it has become very hard to detect or infiltrate bots given that the botnet developers each day keep changing the propagation and attack techniques. Currently, most of these attacks have been centered on stealing computing energy, theft of personal information and Distributed Denial of Service (DDoS attacks). In this paper, the authors propose a novel technique that uses the Non-Deterministic Polynomial-Time Hardness (NP-Hard Problem) based on the Traveling Salesperson Person (TSP) that depicts that a given bot, bj, is able to visit each host on a network environment, NE, and then it returns to the botmaster in form of instruction(command) through optimal minimization of the hosts that are or may be attacked. Given that bj represents a piece of malicious code and based on TSP-NP Hard Problem which forms part of combinatorial optimization, the authors present an effective approach for the detection of the botnet. It is worth noting that the concentration of this study is basically on the centralized botnet architecture. This holistic approach shows that botnet detection accuracy can be increased with a degree of certainty and potentially decrease the chances of false positives. Nevertheless, a discussion on the possible applicability and implementation has also been given in this paper.
2020-07-03
Jia, Guanbo, Miller, Paul, Hong, Xin, Kalutarage, Harsha, Ban, Tao.  2019.  Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :458—465.

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.

2020-06-29
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.
Tran, Thang M., Nguyen, Khanh-Van.  2019.  Fast Detection and Mitigation to DDoS Web Attack Based on Access Frequency. 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF). :1–6.

We have been investigating methods for establishing an effective, immediate defense mechanism against the DDoS attacks on Web applications via hacker botnets, in which this defense mechanism can be immediately active without preparation time, e.g. for training data, usually asked for in existing proposals. In this study, we propose a new mechanism, including new data structures and algorithms, that allow the detection and filtering of large amounts of attack packets (Web request) based on monitoring and capturing the suspect groups of source IPs that can be sending packets at similar patterns, i.e. with very high and similar frequencies. The proposed algorithm places great emphasis on reducing storage space and processing time so it is promising to be effective in real-time attack response.

Das, Saikat, Mahfouz, Ahmed M., Venugopal, Deepak, Shiva, Sajjan.  2019.  DDoS Intrusion Detection Through Machine Learning Ensemble. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :471–477.
Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight against these attackers' new approaches and patterns of DDoS attack. In this paper, we propose a NIDS which can detect existing as well as new types of DDoS attacks. The key feature of our NIDS is that it combines different classifiers using ensemble models, with the idea that each classifier can target specific aspects/types of intrusions, and in doing so provides a more robust defense mechanism against new intrusions. Further, we perform a detailed analysis of DDoS attacks, and based on this domain-knowledge verify the reduced feature set [27, 28] to significantly improve accuracy. We experiment with and analyze NSL-KDD dataset with reduced feature set and our proposed NIDS can detect 99.1% of DDoS attacks successfully. We compare our results with other existing approaches. Our NIDS approach has the learning capability to keep up with new and emerging DDoS attack patterns.
Nenova, Maria, Atanasov, Denis, Kassev, Kiril, Nenov, Andon.  2019.  Intrusion Detection System Model Implementation against DDOS attacks. 2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS). :1–4.
In the paper is presented implementation of a system for detecting intrusion actions. An implementation of intrusion detection systems (IDS), their architectures, and intrusion detection methods are investigated. Analyzed are methods for SNORT (IDS) bandwidth traffic analysis in intrusion detection and prevention systems. The main requirements for Installation and configuration of the system are also discussed. Then the configuration of the firewall policy and specifics there, are also presented. It is also described the database structure, the operating modes, and analysis of the rules. Two of the most commonly implemented attacks and model for defense against them is proposed.
Wehbi, Khadijeh, Hong, Liang, Al-salah, Tulha, Bhutta, Adeel A.  2019.  A Survey on Machine Learning Based Detection on DDoS Attacks for IoT Systems. 2019 SoutheastCon. :1–6.
Internet of Things (IoT) is transforming the way we live today, improving the quality of living standard and growing the world economy by having smart devices around us making decisions and performing our daily tasks and chores. However, securing the IoT system from malicious attacks is a very challenging task. Some of the most common malicious attacks are Denial of service (DoS), and Distributed Denial of service (DDoS) attacks, which have been causing major security threats to all networks and specifically to limited resource IoT devices. As security will always be a primary factor for enabling most IoT applications, developing a comprehensive detection method that effectively defends against DDoS attacks and can provide 100% detection for DDoS attacks in IoT is a primary goal for the future of IoT. The development of such a method requires a deep understanding of the methods that have been used thus far in the detection of DDoS attacks in the IoT environment. In our survey, we try to emphasize some of the most recent Machine Learning (ML) approaches developed for the detection of DDoS attacks in IoT networks along with their advantage and disadvantages. Comparison between the performances of selected approaches is also provided.
Liang, Xiaoyu, Znati, Taieb.  2019.  An empirical study of intelligent approaches to DDoS detection in large scale networks. 2019 International Conference on Computing, Networking and Communications (ICNC). :821–827.
Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. The intensity and frequency of these attacks are increasing at an alarming rate. Numerous schemes have been proposed to mitigate the impact of DDoS attacks. This paper presents a comprehensive empirical evaluation of Machine Learning (ML)based DDoS detection techniques, to gain better understanding of their performance in different types of environments. To this end, a framework is developed, focusing on different attack scenarios, to investigate the performance of a class of ML-based techniques. The evaluation uses different performance metrics, including the impact of the “Class Imbalance Problem” on ML-based DDoS detection. The results of the comparative analysis show that no one technique outperforms all others in all test cases. Furthermore, the results underscore the need for a method oriented feature selection model to enhance the capabilities of ML-based detection techniques. Finally, the results show that the class imbalance problem significantly impacts performance, underscoring the need to address this problem in order to enhance ML-based DDoS detection capabilities.
2020-05-15
Aydeger, Abdullah, Saputro, Nico, Akkaya, Kemal.  2018.  Utilizing NFV for Effective Moving Target Defense Against Link Flooding Reconnaissance Attacks. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :946—951.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

2020-04-13
Lange, Thomas, Kettani, Houssain.  2019.  On Security Threats of Botnets to Cyber Systems. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). :176–183.
As the dynamics of cyber warfare continue to change, it is very important to be aware of the issues currently confronting cyberspace. One threat which continues to grow in the danger it poses to cyber security are botnets. Botnets can launch massive Distributed Denial of Service (DDoS) attacks against internet connected hosts anonymously, undertake intricate spam campaigns, launch mass financial fraud campaigns, and even manipulate public opinion via social media bots. The network topology and technology undergirding each botnet varies greatly, as do the motivations commonly behind such networks. Furthermore, as botnets have continued to evolve, many newer ones demonstrate increased levels of anonymity and sophistication, making it more difficult to effectively counter them. Increases in the production of vulnerable Internet of Things (IoT) devices has made it easier for malicious actors to quickly assemble sizable botnets. Because of this, the steps necessary to stop botnets also vary, and in some cases, it may be extremely difficult to effectively defeat a fully functional and sophisticated botnet. While in some cases, the infrastructure supporting the botnet can be targeted and remotely disabled, other cases require the physical assistance of law enforcement to shut down the botnet. In the latter case, it is often a significant challenge to cheaply end a botnet. On the other hand, there are many steps and mitigations that can be taken by end-users to prevent their own devices from becoming part of a botnet. Many of these solutions involve implementing basic cybersecurity practices like installing firewalls and changing default passwords. More sophisticated botnets may require similarly sophisticated intrusion detection systems, to detect and remove malicious infections. Much research has gone into such systems and in recent years many researchers have begun to implement machine learning techniques to defeat botnets. This paper is intended present a review on botnet evolution, trends and mitigations, and offer related examples and research to provide the reader with quick access to a broad understanding of the issues at hand.
2020-03-23
Alaoui, Sadek Belamfedel, El Houssaine, Tissir, Noreddine, Chaibi.  2019.  Modelling, analysis and design of active queue management to mitigate the effect of denial of service attack in wired/wireless network. 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM). :1–7.
Mitigating the effect of Distributed Denial of Service (DDoS) attacks in wired/wireless networks is a problem of extreme importance. The present paper investigates this problem and proposes a secure AQM to encounter the effects of DDoS attacks on queue's router. The employed method relies on modelling the TCP/AQM system subjected to different DoS attack rate where the resulting closed-loop system is expressed as new Markovian Jump Linear System (MJLS). Sufficient delay-dependent conditions which guarantee the syntheses of a stabilizing control for the closed-loop system with a guaranteed cost J* are derived. Finally, a numerical example is displayed.
Triantopoulou, Stamatia, Papanikas, Dimitrios, Kotzanikolaou, Panayiotis.  2019.  An Experimental Analysis of Current DDoS attacks Based on a Provider Edge Router Honeynet. 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA). :1–5.

This paper presents an experimental analysis of current Distributed Denial of Service attacks. Our analysis is based on real data collected by a honeynet system that was installed on an ISP edge router, for a four-month period. In the examined scenario, we identify and analyze malicious activities based on packets captured and analyzed by a network protocol sniffer and signature-based attack analysis tools. Our analysis shows that IoT-based DDoS attacks are one of the latest and most proliferating attack trends in network security. Based on the analysis of the attacks, we describe some mitigation techniques that can be applied at the providers' network to mitigate the trending attack vectors.

2020-02-26
Sanjeetha, R., Benoor, Pallavi, Kanavalli, Anita.  2019.  Mitigation of DDoS Attacks in Software Defined Networks at Application Level. 2019 PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–3.

Software-Defined Network's (SDN) core working depends on the centralized controller which implements the control plane. With the help of this controller, security threats like Distributed Denial of Service (DDoS) attacks can be identified easily. A DDoS attack is usually instigated on servers by sending a huge amount of unwanted traffic that exhausts its resources, denying their services to genuine users. Earlier research work has been carried out to mitigate DDoS attacks at the switch and the host level. Mitigation at switch level involves identifying the switch which sends a lot of unwanted traffic in the network and blocking it from the network. But this solution is not feasible as it will also block genuine hosts connected to that switch. Later mitigation at the host level was introduced wherein the compromised hosts were identified and blocked thereby allowing genuine hosts to send their traffic in the network. Though this solution is feasible, it will block the traffic from the genuine applications of the compromised host as well. In this paper, we propose a new way to identify and mitigate the DDoS attack at the application level so that only the application generating the DDoS traffic is blocked and other genuine applications are allowed to send traffic in the network normally.