Internet of Things (IoT)-Based Distributed Denial of Service (DDoS) Attack Using COOJA Network Simulator

2022 ◽  
pp. 671-677
Author(s):  
Harshil Joshi ◽  
Dushyantsinh Rathod
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
Network Simulator ◽  
Distributed Denial Of Service ◽  
Ddos Attack

scholarly journals Shield Advanced Mitigation System of Distributed Denial of Service Attack in Integration of Internet of Things and Cloud Computing Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 1300-1306
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Denial Of Service ◽  
Cloud Services ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Service Attack ◽  
Verification Process

Cloud services among public and business companies have become popular in recent years. For production activities, many companies rely on cloud technology. Distributed Denial of Services (DDoS) attack is an extremely damaging general and critical type of cloud attacks. Several efforts have been made in recent years to identify numerous types of DDoS attacks. This paper discusses the different types of DDoS attacks and their cloud computing consequences. Distributed Denial of Service attack (DDoS) is a malicious attempt to disrupt the normal movement of a targeted server, service or network through influx of internet traffic overwhelming the target or its infrastructure. The use of multiple affected computer systems as a source of attacks makes DDoS attacks effective. Computers and other networked tools, including IoT phones, may be included on exploited machines. A DDoS attack from a high level resembles a traffic jam that is caused by roads that prevents normal travel at their desired destination. So DDoS Attack is a major challenging problem in integrated Cloud and IoT. Hence, this paper proposes Shield Advanced Mitigation System of Distributed Denial of Service Attack in the integration of Internet of Things and Cloud Computing Environment. This secure architecture use two verification process to identify whether user is legitimate or malicious. Dynamic Captcha Testing with Equal Probability test for first verification process, moreover Zigsaw Image Puzzle Test is used for second verification process, and Intrusion Detection Prevention System is used to identify and prevent malicious user, moreover reverse proxy is used to hide server location. These functional components and flow could strengthen security in Client side network to provide cloud services furthermore to overcome distributed denial of service attack in the integration of Internet of Things and Cloud Environment.

scholarly journals USING GRAPHIC NETWORK SIMULATOR 3 FOR DDOS ATTACKS SIMULATION

2017 ◽  
pp. 219-225
Author(s):  
Anatoliy Balyk ◽  
Mikolaj Karpinski ◽  
Artur Naglik ◽  
Gulmira Shangytbayeva ◽  
Ihor Romanets
Keyword(s):  
Denial Of Service ◽  
Discrete Event ◽  
Network Simulator ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Easy Task ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Network Simulators ◽  
Attack Mitigation

Distributed Denial of Service (DDoS) attacks are still one of the major cybersecurity threats and the focus of much research on developing DDoS attack mitigation and detection techniques. Being able to model DDoS attacks can help researchers develop effective countermeasures. Modeling DDoS attacks, however, is not an easy task because modern DDoS attacks are huge and simulating them would be impossible in most cases. That’s why researchers use tools like network simulators for modeling DDoS attacks. Simulation is a widely used technique in networking research, but it has suffered a loss of credibility in recent years because of doubts about its reliability. In our previous works we used discrete event simulators to simulate DDoS attacks, but our results were often different from real results. In this paper, we apply our approach and use Graphical Network Simulator-3(GNS3) to simulate an HTTP server’s performance in a typical enterprise network under DDoS attack. Also, we provide references to related work.

Malware Threat in Internet of Things and Its Mitigation Analysis

2021 ◽  
pp. 371-387
Author(s):  
Shingo Yamaguchi ◽  
Brij Gupta
Keyword(s):  
Internet Of Things ◽  
Large Volume ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
New Type ◽  
Iot Devices ◽  
Mitigation Methods ◽  
The Internet Of Things

This chapter introduces malware's threat in the internet of things (IoT) and then analyzes the mitigation methods against the threat. In September 2016, Brian Krebs' web site “Krebs on Security” came under a massive distributed denial of service (DDoS) attack. It reached twice the size of the largest attack in history. This attack was caused by a new type of malware called Mirai. Mirai primarily targets IoT devices such as security cameras and wireless routers. IoT devices have some properties which make them malware attack's targets such as large volume, pervasiveness, and high vulnerability. As a result, a DDoS attack launched by infected IoT devices tends to become massive and disruptive. Thus, the threat of Mirai is an extremely important issue. Mirai has been attracting a great deal of attention since its birth. This resulted in a lot of information related to IoT malware. Most of them came from not academia but industry represented by antivirus software makers. This chapter summarizes such information.

Distributed Denial-of-Service Attack Detection and Mitigation for the Internet of Things

2020 ◽  
Vol 11 (2) ◽  
pp. 18-32
Author(s):  
Opeyemi Peter Ojajuni ◽  
Yasser Ismail ◽  
Albertha Lawson
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
Application Programming Interface ◽  
Attack Detection ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Financial Loss ◽  
Ddos Attack ◽  
The Internet Of Things ◽  
Sdn Controller

The Internet of Things (IoT) allows different devices with internet protocol (IP) address to be connected together via the internet to collect, provide, store, and exchange data amongst themselves. The distributed denial of service (DDoS) attack is one of the inevitable challenges which should be addressed in the development of the IoT. A DDoS attack has the potential to render a victim's services unavailable, which can then lead to additional challenges such as website outage, financial loss, reputational damage and loss of confidential information. In this article, a framework of the SDN controller via an application programming interface (API) is compared to an existing framework. SDN provides a new architecture that can detect and mitigate a DDoS attack so that it makes the networking functionalities programmable via the API and also it centralizes the control management of the IoT devices. Experimental results show the capability of the SDN framework to analyze a real-time traffic of the SDN controller via the API by setting a control bandwidth usage threshold using the API.

scholarly journals Securing an Internet of Things from Distributed Denial of Service and Mirai Botnet Attacks Using a Novel Hybrid Detection and Mitigation Mechanism

2021 ◽  
Vol 14 (1) ◽  
pp. 113-123
Author(s):  
M Karthik ◽  
◽  
M Krishnan ◽  
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
False Negative ◽  
False Negative Rate ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
African Buffalo ◽  
Attack Model ◽  
Ddos Attack ◽  
Hybrid Detection

Internet of Things (IoT) has become more familiar in all applications and industrial fields such as medical, military, transportation, etc. It has some limitations because of the attack model in the transmission or communication channel. Moreover, one of the deadliest attacks is known as a Distributed Denial of Service Attack (DDoS). The Presence of DDoS in network layer cause huge damage in data transmission channel that ends in data loss or collapse. To address this issue the current research focused on an innovative detection and mitigation of Mirai and DDoS attack in IoT environment. Initially, number of IoT devices is arranged with the help of a novel Hybrid Strawberry and African Buffalo Optimization (HSBABO). Consequently, the types of DDoS attacks are launched in the developed IoT network. Moreover, the presence of strawberry and African Buffalo fitness is utilized to detect and specify the attack types. Subsequently a novel MCELIECE encryption with Cloud Shield scheme is developed to prevent the low and high rate DDoS attack in the Internet of Things. Finally, the proposed model attained 94% of attack detection accuracy, 3% of false negative rate and 5.5% of false positive rate.

Malware Threat in Internet of Things and Its Mitigation Analysis

2020 ◽  
pp. 363-379
Author(s):  
Shingo Yamaguchi ◽  
Brij Gupta
Keyword(s):  
Internet Of Things ◽  
Large Volume ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
New Type ◽  
Iot Devices ◽  
Mitigation Methods ◽  
The Internet Of Things

This chapter introduces malware's threat in the internet of things (IoT) and then analyzes the mitigation methods against the threat. In September 2016, Brian Krebs' web site “Krebs on Security” came under a massive distributed denial of service (DDoS) attack. It reached twice the size of the largest attack in history. This attack was caused by a new type of malware called Mirai. Mirai primarily targets IoT devices such as security cameras and wireless routers. IoT devices have some properties which make them malware attack's targets such as large volume, pervasiveness, and high vulnerability. As a result, a DDoS attack launched by infected IoT devices tends to become massive and disruptive. Thus, the threat of Mirai is an extremely important issue. Mirai has been attracting a great deal of attention since its birth. This resulted in a lot of information related to IoT malware. Most of them came from not academia but industry represented by antivirus software makers. This chapter summarizes such information.

scholarly journals Mathematical model on distributed denial of service attack through Internet of things in a network

2019 ◽  
Vol 8 (1) ◽  
pp. 486-495 ◽  
Author(s):  
Bimal Kumar Mishra ◽  
Ajit Kumar Keshri ◽  
Dheeresh Kumar Mallick ◽  
Binay Kumar Mishra
Keyword(s):  
Mathematical Model ◽  
Internet Of Things ◽  
Equilibrium Points ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Different Types ◽  
Service Attack ◽  
Iot Devices

Abstract Internet of Things (IoT) opens up the possibility of agglomerations of different types of devices, Internet and human elements to provide extreme interconnectivity among them towards achieving a completely connected world of things. The mainstream adaptation of IoT technology and its widespread use has also opened up a whole new platform for cyber perpetrators mostly used for distributed denial of service (DDoS) attacks. In this paper, under the influence of internal and external nodes, a two - fold epidemic model is developed where attack on IoT devices is first achieved and then IoT based distributed attack of malicious objects on targeted resources in a network has been established. This model is mainly based on Mirai botnet made of IoT devices which came into the limelight with three major DDoS attacks in 2016. The model is analyzed at equilibrium points to find the conditions for their local and global stability. Impact of external nodes on the over-all model is critically analyzed. Numerical simulations are performed to validate the vitality of the model developed.

scholarly journals Reducing distributed denial of service (DDoS) attacks using client puzzle mechanism

2017 ◽  
Vol 7 (1.1) ◽  
pp. 230
Author(s):  
C. Vasan Sai Krishna ◽  
Y. Bhuvana ◽  
P. Pavan Kumar ◽  
R. Murugan
Keyword(s):  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attack ◽  
Computing Power ◽  
Server Side ◽  
Ddos Attack ◽  
Client Machine ◽  
Client Side

In a typical DoS attack, the attacker tries to bring the server down. In this case, the attacker sends a lot of bogus queries to the server to consume its computing power and bandwidth. As the server’s bandwidth and computing power are always greater than attacker’s client machine, He seeks help from a group of connected computers. DDoS attack involves a lot of client machines which are hijacked by the attacker (together called as botnet). As the server handles all these requests sent by the attacker, all its resources get consumed and it cannot provide services. In this project, we are more concerned about reducing the computing power on the server side by giving the client a puzzle to solve. To prevent such attacks, we use client puzzle mechanism. In this mechanism, we introduce a client-side puzzle which demands the machine to perform tasks that require more resources (computation power). The client’s request is not directly sent to the server. Moreover, there will be an Intermediate Server to monitor all the requests that are being sent to the main server. Before the client’s request is sent to the server, it must solve a puzzle and send the answer. Intermediate Server is used to validate the answer and give access to the client or block the client from accessing the server.

Cluster-Based Countermeasures for DDoS Attacks

2016 ◽  
pp. 206-227
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Dileep Kumar G
Keyword(s):  
Unsupervised Learning ◽  
Network Traffic ◽  
Denial Of Service ◽  
Security Threats ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Real Progress ◽  
Bandwidth Capacity

Distributed Denial of Service (DDoS) attack is considered one of the major security threats in the current Internet. Although many solutions have been suggested for the DDoS defense, real progress in fighting those attacks is still missing. In this chapter, the authors analyze and experiment with cluster-based filtering for DDoS defense. In cluster-based filtering, unsupervised learning is used to create profile of the network traffic. Then the profiled traffic is passed through the filters of different capacity to the servers. After applying this mechanism, the legitimate traffic will get better bandwidth capacity than the malicious traffic. Thus the effect of bad or malicious traffic will be lesser in the network. Before describing the proposed solutions, a detail survey of the different DDoS countermeasures have been presented in the chapter.

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