scholarly journals A Survey of Intrusion Detection System for Denial of Service Attack in Cloud

2015 ◽  
Vol 120 (19) ◽  
pp. 1-4
Author(s):  
Shalki Sharma ◽  
Anshul Gupta ◽  
Sanjay Agrawal
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Denial Of Service Attack ◽  
Service Attack

Multilevel Intrusion Detection System to Mitigate Denial-of-Service Attack in Wireless Sensor Networks

2020 ◽  
Vol 17 (6) ◽  
pp. 2451-2458
Author(s):  
Shilpy Ghai ◽  
Vijay Kumar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Data Encryption ◽  
Denial Of Service Attack ◽  
Malicious Behavior ◽  
Result Show ◽  
Service Attack ◽  
Open Issue

Malicious activities over WSN is quite hard to detect as sensors operate in an open network environment. Researchers have offered several solutions but still intrusion detection/prevention is an open issue. In this paper, a scheme is introduced that can analyze the malicious behavior of the nodes over multiple layers. It uses AES algorithm for data encryption and its integrity is insured using SHA512 method. Simulation results show that it outperforms as compared to traditional WatchDog method under QoS constraints. Simulation result show that it outperforms as compared to traditional watchdog scheme.

Machine Learning Based Intrusion Detection System for Denial of Service Attack

2021 ◽  
pp. 29-47
Author(s):  
Ashish Pandey ◽  
Neelendra Badal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dos Attack ◽  
Denial Of Service Attack ◽  
Service Attack

Machine learning-based intrusion detection system (IDS) is a research field of network security which depends on the effective and accurate training of models. The models of IDS must be trained with new attacks periodically; therefore, it can detect any security violations in the network. One of most frequent security violations that occurs in the network is denial of service (DoS) attack. Therefore, training of IDS models with latest DoS attack instances is required. The training of IDS models can be more effective when it is performed with the help of machine learning algorithms because the processing capabilities of machine learning algorithms are very fast. Therefore, the work presented in this chapter focuses on building a model of machine learning-based intrusion detection system for denial of service attack. Building a model of IDS requires sample dataset and tools. The sample dataset which is used in this research is NSL-KDD, while WEKA is used as a tool to perform all the experiments.

scholarly journals Anomaly Detection in Distributed Denial of Service Attack using Map Reduce Improvised Counter Based Algorithm in Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 4668-4671
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Map Reduce ◽  
Distributed Denial Of Service ◽  
Unusual Pattern ◽  
Denial Of Service Attack

A Distributed denial of Service attacks(DDoS) is one of the major threats in the cyber network and it attacks the computers flooded with the Users Data Gram packet. These types of attacks causes major problem in the network in the form of crashing the system with large volume of traffic to attack the victim and make the victim idle in which not responding the requests. To detect this DDOS attack traditional intrusion detection system is not suitable to handle huge volume of data. Hadoop is a frame work which handles huge volume of data and is used to process the data to find any malicious activity in the data. In this research paper anomaly detection technique is implemented in Map Reduce Algorithm which detects the unusual pattern of data in the network traffic. To design a proposed model, Map Reduce platform is used to hold the improvised algorithm which detects the (DDoS) attacks by filtering and sorting the network traffic and detects the unusual pattern from the network. Improvised Map reduce algorithm is implemented with Map Reduce functionalities at the stage of verifying the network IPS. This Proposed algorithm focuses on the UDP flooding attack using Anomaly based Intrusion detection system technique which detects kind of pattern and flow of packets in the node is more than the threshold and also identifies the source code causing UDP Flood Attack.

scholarly journals Exploring Defense of SQL Injection Attack in Penetration Testing

2017 ◽  
Vol 9 (4) ◽  
pp. 62-71
Author(s):  
Alex Zhu ◽  
Wei Qi Yan
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Network Resources ◽  
Sql Injection ◽  
Qualitative And Quantitative ◽  
Denial Of Service Attack ◽  
Confidential Data ◽  
Service Attack ◽  
Long Time

SQLIA is adopted to attack websites with and without confidential information. Hackers utilized the compromised website as intermediate proxy to attack others for avoiding being committed of cyber-criminal and also enlarging the scale of Distributed Denial of Service Attack (DDoS). The DDoS is that hackers maliciously turn down a website and make network resources unavailable to web users. It is extremely difficult to effectively detect and prevent SQLIA because hackers adopt various evading SQLIA Intrusion Detection System techniques. Victims may not be even aware of that their confidential data has been compromised for a long time. In this paper, our contribution is that we evaluate several most popular open source SQLIA tools and SQLIA prevention tools with both qualitative and quantitative assessments.

scholarly journals A Comparative Analysis of Decision Tree and Bayesian Model for Network Intrusion Detection System

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Bosede A Ayogu ◽  
Adebayo O Adetunmbi ◽  
Ikechukwu I Ayogu
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
Bayesian Network ◽  
Classification System ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Attribute Selection ◽  
Denial Of Service Attack ◽  
Network Intrusion

Denial of Service Attacks (DoS) is a major threat to computer networks. This paper presents two approaches (Decision tree and Bayesian network) to the building of classifiers for DoS attack. Important attributes selection increases the classification accuracy of intrusion detection systems; as decision tree which has the advantage of generating explainable rules was used for the selection of relevant attributes in this research. A C4.5 decision tree dimensional reduction algorithm was used in reducing the 41 attributes of the KDD´99 dataset to 29. Thereafter, a rule based classification system (decision tree) was built as well as Bayesian network classification system for denial of service attack (DoS) based on the selected attributes. The classifiers were evaluated and compared using performance on the test dataset. Experimental results show that Decision Tree is robust and gives the highest percentage of successful classification than Bayesian Network which was found to be sensitive to the discritization techniques. It has been successfully tested that significant attribute selection is important in designing a real world intrusion detection system (IDS). Keywords— Intrusion Detection System, Machine Learning, Decision Tree, and Bayesian Network.

scholarly journals Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1257
Author(s):  
Mohamed Amine Ferrag ◽  
Lei Shu ◽  
Hamouda Djallel ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Future Generation ◽  
Cyber Attacks ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Service Attack

Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.

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