A Comprehensive Survey on DDoS Attacks and Recent Defense Mechanisms

2020 ◽  
pp. 186-218
Author(s):  
Brij B. Gupta ◽  
Amrita Dahiya ◽  
Chivesh Upneja ◽  
Aditi Garg ◽  
Ruby Choudhary
Keyword(s):  
Cyber Security ◽  
Defense Mechanisms ◽  
The Internet ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Comprehensive View ◽  
Distribution Of Resources ◽  
Comprehensive Survey ◽  
Compromised Systems ◽  
Home Users

DDoS attack always takes advantage of structure of Internet and imbalance of resources between defender and attacker. DDoS attacks are driven by factors like interdependency of Internet's security, limited resources, fewer incentives for home users and local ISPs, flexibility of handlers to control multiple compromised systems at the same time, untraceable nature of malicious packets and unfair distribution of resources all over the Internet. This survey chapter gives a comprehensive view on DDoS attacks and its defense mechanisms. Defense mechanisms are categorized according to the deployment position and nature of defense. Comprehensive study of DDoS attacks will definitely help researchers to understand the important issues related to cyber security.

Security in IoT Devices

2021 ◽  
pp. 312-327
Author(s):  
N. JEYANTHI ◽  
Shreyansh Banthia ◽  
Akhil Sharma
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Ddos Attacks ◽  
General Survey ◽  
Internet Infrastructure ◽  
Ddos Attack ◽  
Comprehensive View ◽  
Iot Devices

An attempt to do a comparison between the various DDoS attack types that exist by analysing them in various categories that can be formed, to provide a more comprehensive view of the problem that DDoS poses to the internet infrastructure today. Then DDoS and its relevance with respect to IoT (Internet of Things) devices are analysed where attack types have been explained and possible solutions available are analysed. This chapter does not propose any new solutions to mitigating the effects of DDoS attacks but just provides a general survey of the prevailing attack types along with analysis of the underlying structures that make these attacks possible, which would help researchers in understanding the DDoS problem better.

Security in IoT Devices

2017 ◽  
pp. 96-116
Author(s):  
N. Jeyanthi ◽  
Shreyansh Banthia ◽  
Akhil Sharma
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Ddos Attacks ◽  
General Survey ◽  
Internet Infrastructure ◽  
Ddos Attack ◽  
Comprehensive View ◽  
Iot Devices

An attempt to do a comparison between the various DDoS attack types that exist by analysing them in various categories that can be formed, to provide a more comprehensive view of the problem that DDoS poses to the internet infrastructure today. Then DDoS and its relevance with respect to IoT (Internet of Things) devices are analysed where attack types have been explained and possible solutions available are analysed. This chapter does not propose any new solutions to mitigating the effects of DDoS attacks but just provides a general survey of the prevailing attack types along with analysis of the underlying structures that make these attacks possible, which would help researchers in understanding the DDoS problem better.

Taxonomy of Distributed Denial of Service (DDoS) Attacks and Defense Mechanisms in Present Era of Smartphone Devices

2018 ◽  
Vol 10 (2) ◽  
pp. 58-74 ◽  
Author(s):  
Kavita Sharma ◽  
B. B. Gupta
Keyword(s):  
Defense Mechanisms ◽  
Computer Network ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Network Resources ◽  
Common People ◽  
Ddos Attack ◽  
Legitimate User

This article describes how in the summer of 1999, the Computer Incident Advisory Capability first reported about Distributed Denial of Service (DDoS) attack incidents and the nature of Denial of Service (DoS) attacks in a distributed environment that eliminates the availability of resources or data on a computer network. DDoS attack exhausts the network resources and disturbs the legitimate user. This article provides an explanation on DDoS attacks and nature of these attacks against Smartphones and Wi-Fi Technology and presents a taxonomy of various defense mechanisms. The smartphone is chosen for this study, as they have now become a necessity rather than a luxury item for the common people.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

2021 ◽  
pp. 248-264
Author(s):  
Rochak Swami ◽  
Mayank Dave ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Generation ◽  
Networking Technology

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.

Use-Case of Blockchain in Cybercrime and Cyberattack

2021 ◽  
pp. 145-163
Author(s):  
Karthika Veeramani ◽  
Suresh Jaganathan
Keyword(s):  
Cyber Security ◽  
Computer Network ◽  
Computer Systems ◽  
The Internet ◽  
Use Case ◽  
Ddos Attacks ◽  
Sql Injection ◽  
The Individual

Cybercrime involves unlawful activities done by the individual in cyberspace using the internet. It is cyberbullying, financial theft, code-hack, cryptojacking, hacking, etc. The main difference between cybercrime and cyberattack is that cybercrime victims are humans. The crime associated with the latter is that of a computer network, hardware or software. Cyberattack activities include ransomware, viruses, worms, SQL injection, DDoS attacks, and government and corporate are potential targets. Cyber security provides a specialised approach to the protection of computer systems from cybercrimes and cyberattacks. As of now, no cyber defence is 100% safe. What is considered safe today may not be secure tomorrow. Blockchain enables a new way of recording transactions or any other digital interaction within the network with security, transparency, integrity, confidentiality, availability, and traceability. This chapter explains in detail about cyber risks and how blockchain can be used to avoid risks in financial and insurance frauds.

scholarly journals Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms

2021 ◽  
Vol 26 (5) ◽  
pp. 461-468
Author(s):  
Kishore Babu Dasari ◽  
Nagaraju Devarakonda
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Good Accuracy ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Classification Algorithms ◽  
Negative Consequences ◽  
Ddos Attacks ◽  
Machine Learning Classification ◽  
Ddos Attack

Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while detecting DDoS attacks by using classification algorithms. But researchers, use individual classification algorithms on generalized DDoS attacks. This study used six machine learning classification algorithms to detect eleven different DDoS attacks on different DDoS attack datasets. We used the CICDDoS2019 dataset which is collected from the Canadian Institute of Cyber security in this study. It contains eleven different DDoS attack datasets in CSV file format. On each DDoS attack, we evaluated the effectiveness of the classification methods Logistic regression, Decision tree, Random Forest, Ada boost, KNN, and Naive Bayes, and determined the best classification algorithms for detection.

Towards Utilizing Blockchain for Countering Distributed Denial-of-Service (DDoS)

2021 ◽  
pp. 35-58
Author(s):  
Rochak Swami ◽  
Mayank Dave ◽  
Virender Ranga ◽  
Nikhil Tripathi ◽  
Abhijith Kalayil Shaji ◽  
...  
Keyword(s):  
Cyber Security ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Future Research ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Research Directions ◽  
Network Bandwidth ◽  
Future Research Directions ◽  
Network Administrators

Distributed denial of service (DDoS) attacks have been a matter of serious concern for network administrators in the last two decades. These attacks target the resources such as memory, CPU cycles, and network bandwidth in order to make them unavailable for the benign users, thereby violating availability, one of the components of cyber security. With the existence of DDoS-as-a-service on internet, DDoS attacks have now become more lucrative for the adversaries to target a potential victim. In this work, the authors focus on countering DDoS attacks using one of the latest technologies called blockchain. In inception phase, utilizing blockchain for countering DDoS attacks has proved to be quite promising. The authors also compare existing blockchain-based defense mechanisms to counter DDoS attacks and analyze them. Towards the end of the work, they also discuss possible future research directions in this domain.

Cyber Security Crime and Punishment

2019 ◽  
pp. 1267-1281
Author(s):  
Evon Abu-Taieh ◽  
Auhood Alfaries ◽  
Shaha Al-Otaibi ◽  
Ghadah Aldehim
Keyword(s):  
Saudi Arabia ◽  
Cyber Security ◽  
The Internet ◽  
Crime And Punishment ◽  
Comprehensive View ◽  
Different Types ◽  
Global Understanding ◽  
Definition Of

Cyberspace and the existence of the internet allows different types of crimes to appear. Hence, there is a need for new laws to be set with a collective, comprehensive, view of crime and a global understanding. This article studies 5 different countries' laws pertaining to cybercrimes namely: Jordan, Oman, Kuwait, Qatar, and Saudi Arabia. These different countries issued different laws at different times, some in 2007 others are as new as 2015. The article looks at the laws from an academic definition of different crimes, and also describes the laws from a perspective of each country.

scholarly journals Application Layer DDoS Attack Defense Methods and A new Defense Mechanism against Flooding

2020 ◽  
Vol 8 (6) ◽  
pp. 208-212
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Defense Mechanism ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Network Environment ◽  
Ddos Attacks ◽  
Application Layer ◽  
New Approach ◽  
Ddos Attack

In a network environment, Distributed Denial of Service (DDoS) attacks eemploys a network or server is unavailable to its normal users. Application-layer Distributed Denial of Service (App-DDoS) attacks are serious issues for the webserver itself. The multitude and variety of such attacks and defense approaches are overwhelming. This paper here follows, we analyze the different defense mechanisms for application-layer DDoS attacks and proposes a new approach to defend using machine learning.

scholarly journals Cyber security Network Prevention from DDOS Attack in Healthcare System

2020 ◽  
Vol 9 (11) ◽  
pp. 329-333
Keyword(s):  
Healthcare System ◽  
Cyber Security ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Learning Technology ◽  
Ddos Attacks ◽  
Healthcare Applications ◽  
Ddos Attack

In today’s world of network security, wireless communication attacks such as Distributed Denial of Services (DDoS) attacks are one of the most severe cybercriminal attacks. For the information technology and computer systems, a cyber security rule is required to compel different group as well as businesses to secure their systems and information from cyber-attacks. The occurrence of attacks in the healthcare system is responsible for affecting financial as well as prestige losses the patient. To cyber defense networks from this type of attack, it is essential to design an autonomous detection system by considering some essential countermeasures. Our aim is to detect Distributed Denial of Service (DDoS) attack, which is one of the most commonly present cyber-attacks. This research presented an automatic cybersecurity system against DDoS attacks in healthcare applications. This paper focused on deep learning technology along with the concept of a nature-inspired optimization algorithm to detect the affected node. The designed network is simulated in MATLAB tool and provides better results in terms of Packet Delivery Rate, delay and detection rate with Cuckoo Search (CS) and Artificial Neural Network (ANN) as prevention algorithm. In this paper, author has discussed the importance of the information of the patient data in the healthcare. The detail architecture of the health care information system has also been demonstrated and various security requirement are also been discussed. To analyse the performance of this proposed work, the computed metrices are Throughput %, PDR, Detection Rate and Delay.

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