TCP SYN Flood Attack Research Articles

Detecting and Preventing of DDoS Attack in Cloud Computing Environment Based on Hybrid Technique (Cloudflare and WAF)

The adoption of cloud computing has revolutionized data storage and processing globally. Nevertheless, the need for a close watch on security is paramount. Hence, the aim of this paper, to develop a cloud security model that detects and prevents the risks of Distributed Denial of Service (DDoS) attacks in cloud computing systems which are gravely potent and, on the increase, today. This was done using two approaches: analyzing Transmission Control Protocol/Internet Protocol (TCP/IP) header features of incoming packets in cloud computing environment in order to detect and classify spoofed IP address during DDoS attack via a custom-made Web Application Firewall (WAF); and the integration of the cloud resources with Cloudflare. The result shows that a total of 1,625,192 packets were transmitted in a short period which were captured and analyzed via Wireshark. Several TCP errors were observed over a very short time interval which indicated successful DDoS attack effectively crashing the system. The result varied when the custom-made WAF was put in place, and the attacking lab machine launched a TCP syn flood attack against the web server on port http port 80. A total of 2,353,585 packets were transmitted in a short period which were captured and analyzed using Wireshark and contained less TCP errors indicating successful mitigation of DDoS attacks. When the resources were hosted online and integrated with Cloudflare, integrity checks were successful before the resources were loaded, indicating complete mitigation of attacks. Keywords: Bandwidth, Botnet, Cloudflare, Wireshark and Zombie

Early detection and mitigation of TCP SYN flood attacks in SDN using chi-square test

Early detection and mitigation of TCP SYN flood attacks in SDN using chi-square test

The impact of DoS (Denial of Service) cyberattacks on a Local Area Network (LAN)

In this paper I will highlight a modus operandi of hackers launching Denial of Service (DoS) cyberattacks. I will theoretically show how CAM Overflow and TCP SYN Flood attacks can be performed, using Kali Linux, a Linux distribution used by cyber criminals to launch MitM (Man-in-the-Middle) attacks, DoS attacks, observing traffic in a computer network, etc. Hackers can affect the functioning of devices on an organization’s local network (server, router, switch, etc.) by sending thousands of packets per second to the target device. CAM Overflow is an attack where a hacker aims to overcrowd the CAM table of a switch with MAC addresses, and TCP SYN Flood is an attack that can be launched against a server in the computer network.

As 5G Mobile Private Security Analysis Based on Cloud Computing

As time passes by, the increasing demands from 4G technology services to 5G technology are getting bigger. In line with the development of 5G, there are several open sources such as Free5gc, Open5gs which already provide core 5G network services so that they can help create and simulate private 5G networks. By using free5gc open source, it still needs more attention from the security side, especially from common attacks such as DDoS. Then the attack scenario is carried out from the inner network side, namely from the UE with the TCP SYN Flood and Ping of Death attack methods. The results of the attack contained comparisons for CPU and memory usage parameters. Then the TCP SYN Flood attack had an impact on the core network function components, namely Access and Mobility Management (AMF), which in 5G services experienced errors or crashes after receiving large packets. Thus, users cannot use the service because if an error occurs in the core network function (AMF), then the connection to gNB and the UE will also be lost

A DDoS attack detection and countermeasure scheme based on DWT and auto-encoder neural network for SDN

Software Defined Networking provides new functionalities to easily manage, configure, and optimize network resources by introducing a clear separation between the control entity and the forwarding devices. Such functionalities also help network operators detect and mitigate the security attacks to the network and provide better security level when compared to the traditional networks. However, some security threats, particularly distributed denial of service (DDoS) attacks, keep their effectiveness in degrading the availability of the networks even if the networking paradigm have changed. Existing DDoS attack detection approaches for SDN are mainly based on statistical (threshold-based) and Machine Learning-based (ML) approaches. Considering the dynamic characteristics of the network traffic, finding a dynamic threshold is somehow problematic. On the other hand, finding an appropriate feature that can discriminate DDoS attack from normal traffic is challenging for ML-based approaches. Therefore, to address the aforementioned issues, in this work, we propose a DDoS attack detection and countermeasure scheme based on discrete wavelet transform (DWT) and auto-encoder neural network for SDN. The proposed scheme extracts statistical features from the wavelet transform to be processed by an auto-encoder neural network to detect samples of DDoS attack traffic. Later, to reduce the computational burden imposed by the neural network model, the average hit rate in the flow table of the switches is used to activate the DDoS detection of the scheme. We also provide a detailed performance analysis by considering the computational cost complexity of the algorithms proposed in scheme and the evaluation of the successful detection rate with simulations. Our experimental results show that the proposed scheme achieves high detection rate against DNS amplification, Network Time Protocol and TCP SYN flood attacks with a remarkably low false alarm rate.

Performance Evaluation of Different Raspberry Pi Models as MQTT Servers and Clients

Performance analysis for devices in Internet of Things (IoT) environments is an important consideration, especially with their increasing integration in technological solutions, worldwide. The Single Board Computers (SBCs) of the Raspberry Pi Foundation have been widely accepted by the community, and hence, they have been incorporated in numerous IoT projects. To ease their integration, it is essential to assess their network performance. In this paper, we made an empirical performance evaluation of one of the most popular network protocols for IoT environments, named the Message Queuing Telemetry Transport (MQTT) protocol, on Raspberry Pi. To do so, we set up two different testbeds scenarios and assessed the performance with benchmarks. At the software level, we focused on Mosquitto, a popular open-source MQTT broker implementation and client library. Our principal metric is the transmission time, but we also investigated the throughput. In our experiments, we varied several parameters, such as the size of the payload of the published messages, the WiFi bandwidth, the QoS level, the security level (MQTT vs. MQTT with TLS), and the hardware for the clients and broker. We focus mainly on packet sizes ranging from 100 to 25,000 bytes. We also investigate how these low-cost devices handle a TCP SYN flood attack. In the research work presented within this paper, we aim to guide developers, researchers, network administrators, and hobbyists who plan to use these low-cost devices in an MQTT or IoT network by showing the performance that they should expect according to different Raspberry Pi options.

An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

The success of Internet of Things (IoT) deployment has emerged important smart applications. These applications are running independently on different platforms, almost everywhere in the world. Internet of Medical Things (IoMT), also referred as the healthcare Internet of Things, is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems. Indeed, with the impact of the COVID-19 pandemic, a large number of interconnected devices designed to create smart networks. These networks monitor patients from remote locations as well as tracking medication orders. However, IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks. In this paper, we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes. For this purpose, we develop a new algorithm for Intrusion Detection System (IDS) to detect malicious activities in the Internet of Medical Things. The proposed scheme minimizes as possible the number of attacks to ensure data security, and preserve confidentiality of gathered data. In order to check the viability of our approach, we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.

Comparative Study on TCP SYN Flood DDoS Attack Detection: A Machine Learning Algorithm Based Approach

A most common attack on the internet network is a Distributed Denial of Service (DDoS) attack, which involves occupying computational resources and bandwidth to suppress services to potential clients. The attack scenario is to massively flood the packets. The attack is called a denial of service (DoS) if the attack originates from a single server, and a distributed denial of service (DDoS) if the attack originates from multiple servers. Control and mitigation of DDoS attacks have been a research goal for many scholars for over a decade, and they have achieved in delivering a few major DDoS detection and protection techniques. In the current state of internet use, how quickly and early a DDoS attack can be detected in broadcasting network transactions remains a key research goal. After the development of a machine learning algorithm, many potential methods of DDoS attack detection have been developed. The work presents the results of various experiments carried out using data mining and machine learning algorithms as well as a combination of these algorithms on the commonly available dataset named CAIDA for TCP SYN flood attack detection. Also, this work analysis the various performance metrics such as false positive rate, precision, recall, F-measure and receiver operating characteristic (ROC) using various machine learning algorithm. One-R(OR) with an ideal FPR value of 0.05 and recall value of 0.95,decision stump(DS) with an ideal precision value of o.93,PART with an excellent F-measure value of 0.91 are some of the performance metric values while performing TCP SYN flood attack detection.

DDoS Attack Detection on Internet o Things using Unsupervised Algorithms

The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.

A Novel Approach for distributed denial of service defense using continuous wavelet transform and convolutional neural network for software-Defined network

Software-Defined Networks (SDNs) have emerged particularly for medium and large scale networks since they introduce agility and flexibility with the help of centralized network management by abstracting control plane from data forwarding plane. Although this centralized architecture provides various advantages in terms of effortlessly managing and optimizing network resources, this makes SDN vulnerable to conventional distributed denial of service (DDoS) attacks by making the network inoperable due to the overloaded network traffic. To address the vulnerability, in this work, we propose a detection and countermeasure scheme based on continuous wavelet transform (CWT) and convolutional neural network (CNN). The scheme uses features obtained from CWT as the input for the CNN classifier to differentiate attack samples from the normal ones. Our experimental results show that the proposed scheme achieves high detection rate against DNS amplification, NTP and TCP-SYN flood attacks with a remarkably low false alarm rate.

SDNDefender: A Comprehensive DDoS Defense Mechanism Using Hybrid Approaches over Software Defined Networking

In traditional networks, DDoS attacks are often launched in the network layer or the transport layer. Researchers had explored this problem in depth and put forward plenty of solutions. However, these solutions are only suitable for scenarios such as a single link or victim side network and could not analyse traffic distribution from the angle of the global network. Also, the TCP/IP network architecture lacks abilities to quickly conduct resource deployment and traffic scheduling. When DDoS attacks occur, victims usually could not respond in time. With the superiorities of centralized control mode and global topological view, Software-Defined Networking (SDN) provides a new way to get over the above issues. In this paper, we adopt a combination of diverse technologies to design SDNDefender, a SDN-based DDoS detection and defense mechanism, which is composed of two core components aiming to counter the most popular DDoS attacks including IP spoofing attack and TCP SYN flood attack. We carry out quantitative simulation experiments for evaluating SDNDefender from many metrics. The experimental results show that in contrast to other DDoS defense algorithms, SDNDefender not only efficiently validates spoofed packets and withstands well-known attacks but also defends unknown attacks according to the target’s available resources. Besides, SDNDefender could significantly reduce TCP half-open connections and improve detection accuracy, alleviating attack influences that exhaust the server’s resources and network bandwidth.

FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks

Fog computing is a paradigm to overcome the cloud computing limitations which provides low latency to the users’ applications for the Internet of Things (IoT). Software-defined networking (SDN) is a practical networking infrastructure that provides a great capability in managing network flows. SDN switches are powerful devices, which can be used as fog devices/fog gateways simultaneously. Hence, fog devices are more vulnerable to several attacks. TCP SYN flood attack is one of the most common denial of service attacks, in which a malicious node produces many half-open TCP connections on the targeted computational nodes so as to break them down. Motivated by this, in this paper, we apply SDN concepts to address TCP SYN flood attacks in IoT–fog networks . We propose FUPE, a security-aware task scheduler in IoT–fog networks. FUPE puts forward a fuzzy-based multi-objective particle swarm Optimization approach to aggregate optimal computing resources and providing a proper level of security protection into one synthetic objective to find a single proper answer. We perform extensive simulations on IoT-based scenario to show that the FUPE algorithm significantly outperforms state-of-the-art algorithms. The simulation results indicate that, by varying the attack rates, the number of fog devices, and the number of jobs, the average response time of FUPE improved by 11% and 17%, and the network utilization of FUPE improved by 10% and 22% in comparison with Genetic and Particle Swarm Optimization algorithms, respectively.

Tcp Syn Flood Attack Detection and Prevention System using Adaptive Thresholding Method

Transmission Control Protocol Synchronized (SYN) flooding contributes to a major part of the Denial of service attacks (Dos) because of the easy to exploit nature of the TCP three way handshake mechanism. Attackers use this weakness to overflow the TCP queue of the server and make its re-sources consumed resulting it to be unavailable for the requests of legitimate users. So we are in need of a quick and precise defence mechanism to detect the TCP-SYN Flood attack. The main objective of the paper is to propose a detection and prevention mechanism of the TCP-SYN flood attack using adaptive thresholding. Adaptive threshold algorithm (ATA) is used to calculate dynamic threshold .Thus this algorithm helps to overcome the limitations of static thresholding like high false positive ratio and also alert users after violation of the threshold calculated by adaptive thresholding algorithm. The result of the suggested mechanism is very effective in the detection and prevention of the TCP SYN flood attack using adaptive thresholding algorithm.

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

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

Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment

Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects the functionalities of the cloud services and affects its core capabilities of cloud such as availability and reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies security requirements and can identify the different DDoS security attacks. The proposed system provides promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target virtual instance and protects the cloud applications from DDoS attacks.

Symmetric Measure of Network Traffic using Packet Ratio and Packet Symmetry

Abstract Flood attacks on a network occur when attackers send a very high volume of traffic to a system. This leads to an exhaustion of resources of the targeted system. Such a system or infrastructure under attack won’t be able to cope up with the services it needs to provide due to capacity overload. These types of attacks are also called as Denial of Service (DoS) attacks. As it is essential for companies and organizations to provide high speed and high-quality services the network in which it operates needs to be secured against DoS attacks. As it is said prevention is better than cure, it is always better to detect an attack in the preliminary stages itself and deny an attacker any opportunity to continue the assault on the system. There are various kinds of DoS attacks, in this study the focus is on three kinds of attacks and its detection ICMP flood attack, UDP flood attack and TCP SYN flood attack. The detection mechanism employed is based on packet symmetry which can be used in to monitor the data flow in a network and detect any malicious anomalies in the network traffic at any instant. The experimental results, show that the packet symmetry is a reliable metric to detect attacks on the network by checking the data flow in the ICMP, UDP and TCP layers.

An empirical investigation of botnet as a service for cyberattacks

AbstractDuring the last years, cloud computing has emerged and imposed itself as a cost‐effective solution for providing high quality Information Technology services. However, beyond a legitimate usage, the benefits of cloud computing are being exploited by attackers in nefarious ways and botnets are among the greatest beneficiaries of this malicious use. This botnet trend is a major issue because it strongly increases the power of massive distributed attacks when leveraging the capabilities of cloud service providers that do not have appropriate detection approaches in place to detect botnets exploiting cloud resources (also referred to as botclouds). We developed a free experimental intra–Cloud Service Provider (CSP) botnet that exploits CSPs' trial versions. We used this intra‐CSP botnet to execute numerous TCP SYN flood and UDP flood attacks during one week. Our empirical results demonstrate that, contrary to what CSPs claim, all the CSPs (with the exception of one) we have tested still cannot detect or issued no warnings when malicious activities were launched from their cloud computing platforms. Besides, CSP's trial versions can easily be exploited to perpetrate large scale cyberattacks. We argue that efficient, cost‐effective, scalable detection approaches that can detect botclouds need to be developed in the future to address this challenge.

An efficient detection of TCP Syn flood attacks with spoofed IP addresses

An efficient detection of TCP Syn flood attacks with spoofed IP addresses

SDN-Based SYN Proxy—A Solution to Enhance Performance of Attack Mitigation Under TCP SYN Flood

Recently, TCP SYN flood has been the most common and serious type of Distributed Denial of Service attack that causes outages of server resource of Internet Service Providers. In another aspect, Software Defined Networking (SDN) has emerged as a new networking paradigm to increase network agility and programmability. SDN is also a promising architecture to deal with the network security issue where we can flexibly change security rules and control incoming flows. In this article, we design an Openflow/SDN network remedy to combat specifically TCP SYN flood. We show security threats for the SDN architecture and exploit SDN capabilities and features to design a SDN-based SYN Proxy (SSP) paradigm to mitigate such TCP SYN threats. Our SSP is proved to be a network-based solution to protect application servers in terms of decreasing number of Half-Open Connections at an application server and increasing probability of successful establishment for a TCP flow connection under TCP SYN Flood attack. Using SSP to support application servers is shown to outperform the case where the servers adopt only the protection scheme of Microsoft Windows server reference model without utilizing SSP. SSP also shows that it can reduce the time a flow entry occupies the switch resource by 94% in comparison with the Avant-Guard solution. In addition, SSP improves the successful connection rate and average connection retrieval time in comparison with the standard Openflow solution.

Realizacija TCP Syn Flood napada upotrebom Kali Linuksa

Denial-of-Service (DoS) is a type of attack that attempts to prevent legitimate users from accessing network services. This is accomplished by overloading network services or by excessive connectivity, causing a drop in a connection or a service. DoS tools are designed to send large numbers of requests to the targeted server (usually web, FTP, e-mail server), in order to overwhelm server resources and make it unusable. There are various ways in which attackers achieve this. One of the usual ways is simply overwhelming the server by sending too many requests. This will disable the normal functioning of the server (and the web pages will open more slowly), and in some cases it can lead to a situation that the server ceases to operate. This paper shows some effects of TCP Syn Flood Attacks (using Kali Linux) through the change of processor utilization and the unavailability of the target computer (executing ping command).