Visible to the public Biblio

Found 932 results

Filters: First Letter Of Title is D  [Clear All Filters]
A B C [D] E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
D
Zhang, Z., Zhang, Q., Liu, T., Pang, Z., Cui, B., Jin, S., Liu, K..  2020.  Data-driven Stealthy Actuator Attack against Cyber-Physical Systems. 2020 39th Chinese Control Conference (CCC). :4395–4399.
This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.
Mavroeidis, Vasileios, Jøsang, Audun.  2018.  Data-Driven Threat Hunting Using Sysmon. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :82-88.
Threat actors can be persistent, motivated and agile, and they leverage a diversified and extensive set of tactics, techniques, and procedures to attain their goals. In response to that, organizations establish threat intelligence programs to improve their defense capabilities and mitigate risk. Actionable threat intelligence is integrated into security information and event management systems (SIEM) forming a threat intelligence platform. A threat intelligence platform aggregates log data from multiple disparate sources by deploying numerous collection agents and provides centralized analysis and reporting of an organization's security events for identifying malicious activity. Sysmon logs is a data source that has received considerable attention for endpoint visibility. Approaches for threat detection using Sysmon have been proposed mainly focusing on search engines (NoSQL database systems). This paper presents a new automated threat assessment system that relies on the analysis of continuous incoming feeds of Sysmon logs. The system is based on a cyber threat intelligence ontology and analyses Sysmon logs to classify software in different threat levels and augment cyber defensive capabilities through situational awareness, prediction, and automated courses of action.
Ahmed, Alaa H., Sadri, Fereidoon.  2018.  Datafusion: Taking Source Confidences into Account. Proceedings of the 8th International Conference on Information Systems and Technologies. :9:1–9:6.
Data fusion is a form of information integration where large amounts of data mined from sources such as web sites, Twitter feeds, Facebook postings, blogs, email messages, news streams, and the like are integrated. Such data is inherently uncertain and unreliable. The sources have different degrees of accuracy and the data mining process itself incurs additional uncertainty. The main goal of data fusion is to discover the correct data among the uncertain and possibly conflicting mined data. We investigate a data fusion approach that, in addition to the accuracy of sources, incorporates the correctness (confidence) measures that most data mining approaches associate with mined data. There are a number of advantages in incorporating these confidences. First, we do not require a training set. The initial training set is obtained using the confidence measures. More importantly, a more accurate fusion can result by taking the confidences into account. We present an approach to determine the correctness threshold using users' feedback, and show it can significantly improve the accuracy of data fusion. We evaluate of the performance and accuracy of our data fusion approach for two groups of experiments. In the first group data sources contain random (unintentional) errors. In the second group data sources contain intentional falsifications.
Pasarella, Edelmira, Lobo, Jorge.  2017.  A Datalog Framework for Modeling Relationship-based Access Control Policies. Proceedings of the 22Nd ACM on Symposium on Access Control Models and Technologies. :91–102.

Relationships like friendship to limit access to resources have been part of social network applications since their beginnings. Describing access control policies in terms of relationships is not particular to social networks and it arises naturally in many situations. Hence, we have recently seen several proposals formalizing different Relationship-based Access Control (ReBAC) models. In this paper, we introduce a class of Datalog programs suitable for modeling ReBAC and argue that this class of programs, that we called ReBAC Datalog policies, provides a very general framework to specify and implement ReBAC policies. To support our claim, we first formalize the merging of two recent proposals for modeling ReBAC, one based on hybrid logic and the other one based on path regular expressions. We present extensions to handle negative authorizations and temporal policies. We describe mechanism for policy analysis, and then discuss the feasibility of using Datalog-based systems as implementations.

Carr, Scott A., Payer, Mathias.  2017.  DataShield: Configurable Data Confidentiality and Integrity. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :193–204.
Applications written in C/C++ are prone to memory corruption, which allows attackers to extract secrets or gain control of the system. With the rise of strong control-flow hijacking defenses, non-control data attacks have become the dominant threat. As vulnerabilities like HeartBleed have shown, such attacks are equally devastating. Data Confidentiality and Integrity (DCI) is a low-overhead non-control-data protection mechanism for systems software. DCI augments the C/C++ programming languages with an- notations, allowing the programmer to protect selected data types. The DCI compiler and runtime system prevent illegal reads (confidentiality) and writes (integrity) to instances of these types. The programmer selects types that contain security critical information such as passwords, cryptographic keys, or identification tokens. Protecting only this critical data greatly reduces performance overhead relative to complete memory safety. Our prototype implementation of DCI, DataShield, shows the applicability and efficiency of our approach. For SPEC CPU2006, the performance overhead is at most 16.34%. For our case studies, we instrumented mbedTLS, astar, and libquantum to show that our annotation approach is practical. The overhead of our SSL/TLS server is 35.7% with critical data structures protected at all times. Our security evaluation shows DataShield mitigates a recently discovered vulnerability in mbedTLS.
Ping, Haoyue, Stoyanovich, Julia, Howe, Bill.  2017.  DataSynthesizer: Privacy-Preserving Synthetic Datasets. Proceedings of the 29th International Conference on Scientific and Statistical Database Management. :42:1–42:5.
To facilitate collaboration over sensitive data, we present DataSynthesizer, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees. The data owners need not release their data, while potential collaborators can begin developing models and methods with some confidence that their results will work similarly on the real dataset. The distinguishing feature of DataSynthesizer is its usability — the data owner does not have to specify any parameters to start generating and sharing data safely and effectively. DataSynthesizer consists of three high-level modules — DataDescriber, DataGenerator and ModelInspector. The first, DataDescriber, investigates the data types, correlations and distributions of the attributes in the private dataset, and produces a data summary, adding noise to the distributions to preserve privacy. DataGenerator samples from the summary computed by DataDescriber and outputs synthetic data. ModelInspector shows an intuitive description of the data summary that was computed by DataDescriber, allowing the data owner to evaluate the accuracy of the summarization process and adjust any parameters, if desired. We describe DataSynthesizer and illustrate its use in an urban science context, where sharing sensitive, legally encumbered data between agencies and with outside collaborators is reported as the primary obstacle to data-driven governance. The code implementing all parts of this work is publicly available at https://github.com/DataResponsibly/DataSynthesizer.
Kumar, B., Kumar, P., Mundra, A., Kabra, S..  2015.  DC scanner: Detecting phishing attack. 2015 Third International Conference on Image Information Processing (ICIIP). :271–276.

Data mining has been used as a technology in various applications of engineering, sciences and others to analysis data of systems and to solve problems. Its applications further extend towards detecting cyber-attacks. We are presenting our work with simple and less efforts similar to data mining which detects email based phishing attacks. This work digs html contents of emails and web pages referred. Also domains and domain related authority details of these links, script codes associated to web pages are analyzed to conclude for the probability of phishing attacks.

Chariton, Antonios A., Degkleri, Eirini, Papadopoulos, Panagiotis, Ilia, Panagiotis, Markatos, Evangelos P..  2016.  DCSP: Performant Certificate Revocation a DNS-based Approach. Proceedings of the 9th European Workshop on System Security. :1:1–1:6.

Trust in SSL-based communication on the Internet is provided by Certificate Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) making sure that it is not revoked. Currently, Certificate Revocation checks (i.e. step (iii) above) are done either via Certificate Revocation Lists (CRLs) or Online Certificate Status Protocol (OCSP) servers. Unfortunately, both current approaches tend to incur such a high overhead that several browsers (including almost all mobile ones) choose not to check certificate revocation status, thereby exposing their users to significant security risks. To address this issue, we propose DCSP: a new low-latency approach that provides up-to-date and accurate certificate revocation information. DCSP capitalizes on the existing scalable and high-performance infrastructure of DNS. DCSP minimizes end user latency while, at the same time, requiring only a small number of cryptographic signatures by the CAs. Our design and initial performance results show that DCSP has the potential to perform an order of magnitude faster than the current state-of-the-art alternatives.

Deepali, Bhushan, K..  2017.  DDoS attack defense framework for cloud using fog computing. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT). :534–538.

Cloud is the requirement of today's competitive world that demand flexible, agile and adaptable technology to be at par with rapidly changing IT industry. Cloud offers scalable, on-demand, pay-as-you-go services to enterprise and has hence become a part of growing trend of organizations IT service model. With emerging trend of cloud the security concerns have further increased and one of the biggest concerns related to cloud is DDoS attack. DDoS attack tends to exhaust all the available resources and leads to unavailability of services in cloud to legitimate users. In this paper the concept of fog computing is used, it is nothing but an extension to cloud computing that performs analysis at the edge of the network, i.e. bring intelligence at the edge of the network for quick real time decision making and reducing the amount of data that is forwarded to cloud. We have proposed a framework in which DDoS attack traffic is generated using different tools which is made to pass through fog defender to cloud. Furthermore, rules are applied on fog defender to detect and filter DDoS attack traffic targeted to cloud.

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.
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.
Bhaya, W., EbadyManaa, M..  2017.  DDoS attack detection approach using an efficient cluster analysis in large data scale. 2017 Annual Conference on New Trends in Information Communications Technology Applications (NTICT). :168–173.

Distributed Denial of Service (DDoS) attack is a congestion-based attack that makes both the network and host-based resources unavailable for legitimate users, sending flooding attack packets to the victim's resources. The non-existence of predefined rules to correctly identify the genuine network flow made the task of DDoS attack detection very difficult. In this paper, a combination of unsupervised data mining techniques as intrusion detection system are introduced. The entropy concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in network flow. The data is mainly collected from DARPA2000, CAIDA2007 and CAIDA2008 datasets. The proposed approach has been evaluated and compared with several existing approaches in terms of accuracy, false alarm rate, detection rate, F. measure and Phi coefficient. Results indicates the superiority of the proposed approach with four out five detected phases, more than 99% accuracy rate 96.29% detection rate, around 0% false alarm rate 97.98% F-measure, and 97.98% Phi coefficient.

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.
Baek, Ui-Jun, Ji, Se-Hyun, Park, Jee Tae, Lee, Min-Seob, Park, Jun-Sang, Kim, Myung-Sup.  2019.  DDoS Attack Detection on Bitcoin Ecosystem using Deep-Learning. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1—4.
Since Bitcoin, the first cryptocurrency that applied blockchain technology was developed by Satoshi Nakamoto, the cryptocurrency market has grown rapidly. Along with this growth, many vulnerabilities and attacks are threatening the Bitcoin ecosystem, which is not only at the bitcoin network-level but also at the service level that applied it, according to the survey. We intend to analyze and detect DDoS attacks on the premise that bitcoin's network-level data and service-level DDoS attacks with bitcoin are associated. We evaluate the results of the experiment according to the proposed metrics, resulting in an association between network-level data and service-level DDoS attacks of bitcoin. In conclusion, we suggest the possibility that the proposed method could be applied to other blockchain systems.
Ahuja, Nisha, Singal, Gaurav.  2019.  DDOS Attack Detection Prevention in SDN using OpenFlow Statistics. 2019 IEEE 9th International Conference on Advanced Computing (IACC). :147–152.
Software defined Network is a network defined by software, which is one of the important feature which makes the legacy old networks to be flexible for dynamic configuration and so can cater to today's dynamic application requirement. It is a programmable network but it is prone to different type of attacks due to its centralized architecture. The author provided a solution to detect and prevent Distributed Denial of service attack in the paper. Mininet [5] which is a popular emulator for Software defined Network is used. We followed the approach in which collection of the traffic statistics from the various switches is done. After collection we calculated the packet rate and bandwidth which shoots up to high values when attack take place. The abrupt increase detects the attack which is then prevented by changing the forwarding logic of the host nodes to drop the packets instead of forwarding. After this, no more packets will be forwarded and then we also delete the forwarding rule in the flow table. Hence, we are finding out the change in packet rate and bandwidth to detect the attack and to prevent the attack we modify the forwarding logic of the switch flow table to drop the packets coming from malicious host instead of forwarding it.
Meng, B., Andi, W., Jian, X., Fucai, Z..  2017.  DDOS Attack Detection System Based on Analysis of Users' Behaviors for Application Layer. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 1:596–599.

Aiming at the problem of internal attackers of database system, anomaly detection method of user behaviour is used to detect the internal attackers of database system. With using Discrete-time Markov Chains (DTMC), an anomaly detection system of user behavior is proposed, which can detect the internal threats of database system. First, we make an analysis on SQL queries, which are user behavior features. Then, we use DTMC model extract behavior features of a normal user and the detected user and make a comparison between them. If the deviation of features is beyond threshold, the detected user behavior is judged as an anomaly behavior. The experiments are used to test the feasibility of the detction system. The experimental results show that this detction system can detect normal and abnormal user behavior precisely and effectively.

Ateş, Çağatay, Özdel, Süleyman, Yıldırım, Metehan, Anarım, Emin.  2019.  DDoS Attack Detection Using Greedy Algorithm and Frequency Modulation. 2019 27th Signal Processing and Communications Applications Conference (SIU). :1–4.
Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Boğaziçi University network.
Zekri, M., Kafhali, S. E., Aboutabit, N., Saadi, Y..  2017.  DDoS attack detection using machine learning techniques in cloud computing environments. 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). :1–7.

Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.

Kansal, V., Dave, M..  2017.  DDoS attack isolation using moving target defense. 2017 International Conference on Computing, Communication and Automation (ICCCA). :511–514.

Among the several threats to cyber services Distributed denial-of-service (DDoS) attack is most prevailing nowadays. DDoS involves making an online service unavailable by flooding the bandwidth or resources of a targeted system. It is easier for an insider having legitimate access to the system to circumvent any security controls thus resulting in insider attack. To mitigate insider assisted DDoS attacks, this paper proposes a moving target defense mechanism that involves isolation of insiders from innocent clients by using attack proxies. Further using the concept of load balancing an effective algorithm to detect and handle insider attack is developed with the aim of maximizing attack isolation while minimizing the total number of proxies used.

Ahmed, M. E., Kim, H..  2017.  DDoS Attack Mitigation in Internet of Things Using Software Defined Networking. 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService). :271–276.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

Saridou, Betty, Shiaeles, Stavros, Papadopoulos, Basil.  2019.  DDoS Attack Mitigation through Root-DNS Server: A Case Study. 2019 IEEE World Congress on Services (SERVICES). 2642-939X:60—65.

Load balancing and IP anycast are traffic routing algorithms used to speed up delivery of the Domain Name System. In case of a DDoS attack or an overload condition, the value of these protocols is critical, as they can provide intrinsic DDoS mitigation with the failover alternatives. In this paper, we present a methodology for predicting the next DNS response in the light of a potential redirection to less busy servers, in order to mitigate the size of the attack. Our experiments were conducted using data from the Nov. 2015 attack of the Root DNS servers and Logistic Regression, k-Nearest Neighbors, Support Vector Machines and Random Forest as our primary classifiers. The models were able to successfully predict up to 83% of responses for Root Letters that operated on a small number of sites and consequently suffered the most during the attacks. On the other hand, regarding DNS requests coming from more distributed Root servers, the models demonstrated lower accuracy. Our analysis showed a correlation between the True Positive Rate metric and the number of sites, as well as a clear need for intelligent management of traffic in load balancing practices.

Zeb, K., Baig, O., Asif, M. K..  2015.  DDoS attacks and countermeasures in cyberspace. 2015 2nd World Symposium on Web Applications and Networking (WSWAN). :1–6.

In cyberspace, availability of the resources is the key component of cyber security along with confidentiality and integrity. Distributed Denial of Service (DDoS) attack has become one of the major threats to the availability of resources in computer networks. It is a challenging problem in the Internet. In this paper, we present a detailed study of DDoS attacks on the Internet specifically the attacks due to protocols vulnerabilities in the TCP/IP model, their countermeasures and various DDoS attack mechanisms. We thoroughly review DDoS attacks defense and analyze the strengths and weaknesses of different proposed mechanisms.

Rahman, Obaid, Quraishi, Mohammad Ali Gauhar, Lung, Chung-Horng.  2019.  DDoS Attacks Detection and Mitigation in SDN Using Machine Learning. 2019 IEEE World Congress on Services (SERVICES). 2642-939X:184–189.

Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.

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%.
Mustapha, Hanan, Alghamdi, Ahmed M.  2018.  DDoS Attacks on the Internet of Things and Their Prevention Methods. Proceedings of the 2Nd International Conference on Future Networks and Distributed Systems. :4:1-4:5.

The Internet of Things (IoT) vulnerabilities provides an ideal target for botnets, making them a major contributor in the increased number of Distributed Denial of Service (DDoS) attacks. The increase in DDoS attacks has made it important to address the consequences it implies on the IoT industry being one of the major causes. The aim of this paper is to provide an analysis of the attempts to prevent DDoS attacks, mainly at a network level. The sensibility of these solutions is extracted from their impact in resolving IoT vulnerabilities. It is evident from this review that there is no perfect solution yet for IoT security, this field still has many opportunities for research and development.