scholarly journals Implementation of a PSO-Based Security Defense Mechanism for Tracing the Sources of DDoS Attacks

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 88
Author(s):  
Hsiao-Chung Lin ◽  
Ping Wang ◽  
Wen-Hui Lin
Keyword(s):  
Defense Mechanism ◽  
Detection System ◽  
Denial Of Service ◽  
Packet Routing ◽  
Distributed Network ◽  
Ddos Attacks ◽  
Ip Traceback ◽  
Modified Particle Swarm Optimization ◽  
Network Intrusion ◽  
Management Techniques

Most existing approaches for solving the distributed denial-of-service (DDoS) problem focus on specific security mechanisms, for example, network intrusion detection system (NIDS) detection and firewall configuration, rather than on the packet routing approaches to defend DDoS threats by new flow management techniques. To defend against DDoS attacks, the present study proposes a modified particle swarm optimization (PSO) scheme based on an IP traceback (IPTBK) technique, designated as PSO-IPTBK, to solve the IP traceback problem. Specifically, this work focuses on analyzing the detection of DDoS attacks to predict the possible attack routes in a distributed network. In the proposed approach, the PSO-IPTBK identifies the source of DDoS attacks by reconstructing the probable attack routes from collected network packets. The performance of the PSO-IPTBK algorithm in reconstructing the attack route was investigated through a series of simulations using OMNeT++ 5.5.1 and the INET 4 Framework. The results show that the proposed scheme can determine the most possible route between the attackers and the victim to defend DDoS attacks.

scholarly journals Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ivandro Ortet Lopes ◽  
Deqing Zou ◽  
Francis A Ruambo ◽  
Saeed Akbar ◽  
Bin Yuan
Keyword(s):  
Deep Learning ◽  
Intrusion Detection System ◽  
State Of The Art ◽  
Detection System ◽  
Denial Of Service ◽  
Model Performance ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Validation Metrics ◽  
High Processing

Distributed Denial of Service (DDoS) is a predominant threat to the availability of online services due to their size and frequency. However, developing an effective security mechanism to protect a network from this threat is a big challenge because DDoS uses various attack approaches coupled with several possible combinations. Furthermore, most of the existing deep learning- (DL-) based models pose a high processing overhead or may not perform well to detect the recently reported DDoS attacks as these models use outdated datasets for training and evaluation. To address the issues mentioned earlier, we propose CyDDoS, an integrated intrusion detection system (IDS) framework, which combines an ensemble of feature engineering algorithms with the deep neural network. The ensemble feature selection is based on five machine learning classifiers used to identify and extract the most relevant features used by the predictive model. This approach improves the model performance by processing only a subset of relevant features while reducing the computation requirement. We evaluate the model performance based on CICDDoS2019, a modern and realistic dataset consisting of normal and DDoS attack traffic. The evaluation considers different validation metrics such as accuracy, precision, F1-Score, and recall to argue the effectiveness of the proposed framework against state-of-the-art IDSs.

Multi-Aspect DDOS Detection System for Securing Cloud Network

2019 ◽  
pp. 1952-1983
Author(s):  
Pourya Shamsolmoali ◽  
Masoumeh Zareapoor ◽  
M.Afshar Alam
Keyword(s):  
Cloud Computing ◽  
Detection System ◽  
Denial Of Service ◽  
Complex Form ◽  
Internet Security ◽  
Detection Methods ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Ddos Detection ◽  
Cloud Network

Distributed Denial of Service (DDoS) attacks have become a serious attack for internet security and Cloud Computing environment. This kind of attacks is the most complex form of DoS (Denial of Service) attacks. This type of attack can simply duplicate its source address, such as spoofing attack, which defending methods do not able to disguises the real location of the attack. Therefore, DDoS attack is the most significant challenge for network. In this chapter we present different aspect of security in Cloud Computing, mostly we concentrated on DDOS Attacks. The Authors illustrated all types of Dos Attacks and discussed the most effective detection methods.

scholarly journals Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

2021 ◽  
Author(s):  
Kathiroli Raja ◽  
Krithika Karthikeyan ◽  
Abilash B ◽  
Kapal Dev ◽  
Gunasekaran Raja
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Production Management ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Sensitive Information ◽  
Smart Meters ◽  
Network Intrusion

Abstract The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified,
1
stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

scholarly journals Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

2021 ◽  
Author(s):  
◽  
Abigail Koay
Keyword(s):  
Information Sharing ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Low Intensity ◽  
Ddos Attack ◽  
Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

scholarly journals Defense mechanisms against Distributed Denial of Service attacks: Comparative Review

2021 ◽  
Vol 4 (1) ◽  
pp. 81-94
Author(s):  
Fahad Alatawi
Keyword(s):  
Defense Mechanisms ◽  
Large Scale ◽  
Defense Mechanism ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Response Mechanism ◽  
Packet Filtering ◽  
Comparative Review

Distributed Denial of Service (DDoS) remains a big concern in Cybersecurity. DDoS attacks are implemented to prevent legitimate users from getting access to services. The attackers make use of multiple hosts that have been compromised (i.e., Botnets) to organize a large-scale attack on targets. Developing an effective defensive mechanism against existing and potential DDoS attacks remains a strong desire in the cybersecurity research community. However, development of effective mechanisms or solutions require adequate evaluation of existing defense mechanism and a critical analysis of how these methods have been implemented in preventing, detecting, and responding to DDoS attacks. This paper adopted a systematic review method to critically analyze the existing mechanisms. The review of existing literature helped classify the defense mechanism into four categories: source-based, core-router, victim-based, and distributed systems. A qualitative analysis was used to exhaustively evaluate these defense mechanisms and determine their respective effectiveness. The effectiveness of the defense mechanisms was evaluated on six key parameters: coverage, implementation, deployment, detection accuracy, response mechanism, and robustness. The comparative analysis reviewed the shortcomings and benefits of each mechanism. The evaluation determined that victim-based defense mechanisms have a high detection accuracy but is associated with massive collateral as the detection happens when it is too late to protect the system. On the other hand, whereas stopping an attack from the source-end is ideal, detection accuracy at this point is too low as it is hard to differentiate legitimate and malicious traffic. The effectiveness of the core-based defense systems is not ideal because the routers do not have enough CPU cycles and memory to profile the traffic. Distributed defense mechanisms are effective as components can be spread out across the three locations in a way that takes advantage of each location. The paper also established that the rate-limiting response mechanism is more effective than packet filtering method because it does not restrict legitimate traffic. The analysis revealed that there is no single defense mechanism that offers complete protection against DDoS attacks but concludes that the best defense mechanism is the use of distributed defense because it ensures that defense components are placed on all locations.

scholarly journals Classification of Intrusion using Artificial Neural Network with GWO

2020 ◽  
Vol 9 (4) ◽  
pp. 599-606
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Data Set ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Artificial Neural

In the present milieu of connected world, where security is the major concern, Intrusion Detection System is the prominent area of research to deal with various types of attacks in network. Intrusion detection systems (IDS) finds the dynamic and malicious traffic of network, in accordance to the aspect of network. Various form of IDS has been developed working on distinctive approaches. One popular approach is machine learning in which various algorithms like ANN, SVM etc. have been used. But the most prominent method used is ANN. The performance of the ANN can significantly be improved by combining it with different metaheuristic algorithms. In present work, GWO is used to optimize ANN. For this KDD-99 data-set is used to classify various types of attacks i.e. denial of service (DOS), normal and other form of attack. The present paper provides detailed analysis of the performance of Artificial Neural Network and optimized Artificial Neural Network with GA, PSO and GWO. The research shows that ANN with GWO outperform as compared to others (ANN, ANN with PSO and ANN with GA).

Against Spoofing Attacks in Network Layer

2016 ◽  
pp. 41-56
Author(s):  
Kavisankar L. ◽  
Chellappan C. ◽  
Poovammal E.
Keyword(s):  
Network Security ◽  
Defense Mechanism ◽  
Open Systems ◽  
Denial Of Service ◽  
Internet Protocol ◽  
Network Protocol ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Network Layer ◽  
Open Systems Interconnection

In the context of network security, a spoofing attack is a condition in which one person or a program successfully masquerades as another. This is done by providing counterfeit data with the malicious intention of gaining an illegitimate advantage. Spoofing attack which may be generated in various layer of Open Systems Interconnection model (OSI model) is discussed in this chapter. The chapter ends with discussing about the possible spoofing attacks in network layer and the relevant defense mechanism of the same. The detailed analysis and discussion is made on the spoofing attack over the Network layer because, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks more devastating while using network protocol like Internet Protocol (IP) which have become more of a threat than ever for the past few years.

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