scholarly journals Anomaly Detection in Distributed Denial of Service Attack using Map Reduce Improvised counter-based algorithm in Hadoop

2018 ◽  
Vol 7 (4.36) ◽  
pp. 390
Author(s):  
Y. S.Kalai vani ◽  
Dr. P.Ranjana
Keyword(s):  
Intrusion Detection ◽  
Large Volume ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Algorithm ◽  
Map Reduce ◽  
Distributed Denial Of Service ◽  
Huge Amount ◽  
Denial Of Service Attack ◽  
Flooding Attacks

A Distributed Denial of Service (DDOS) is one of the major threats in the cyber network and it causes the computers flooded with the Users Datagram Packet (UDP).This type of attack crashes the victim with large volume of traffic and the victim is not capable of performing normal communication and crashes it completely. To handle this DDOS attack the normal Intrusion Detection System is not suitable to hold and find the amount of the data in the network. Hadoop is a frame work that allows huge amount of data and it is used to processes the huge amount of data. A Map reduce program comprises of a Map task that performs filtering and sorting and a Reduce task that performs summary operation. The propose work  focuses on the detection algorithm based on Map Reduce platform which uses the Improvised counter based (MRICB)  algorithm to detect the DDOS flooding attacks. The MRICB algorithm is implemented with Map Reduce functionalities at the stage of verifying the Network IPS. This proposed algorithm also focuses  on the UDP flooding attack using anomaly based intrusion detection technique that identifies the kind of packets and the flow of packet in the node is more that the set threshold and also identifies  the source code causing UDP Flood attack . Thus it ensures the normal communication with large volume of traffic.   

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.

A SURVEY ON CLOUD-DENIAL OF SERVICE

2020 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Bibek Naha ◽  
Siddhartha Banerjee ◽  
Sayanti Mondal
Keyword(s):  
Cloud Computing ◽  
Comparative Study ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Cloud Environment ◽  
Dos Attack ◽  
Huge Amount ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Insight Into

Cloud Computing is one of the most nurtured as well as debated topic in today’s world. Billions of data of various fields ranging from personal users to large business enterprises reside in Cloud. Therefore, availability of this huge amount of data and services is of immense importance. The DOS (Denial of Service) attack is a well-known threat to the availability of data in a smaller premise. Whenever, it’s a Cloud environment this simple DOS attack takes the form of DDOS (Distributed Denial of Service) attack. This paper provides a generic insight into the various kinds of DOS as well as DDOS attacks. Moreover, a handful of countermeasures have also been depicted here. In a nutshell, it aims at raising an awareness by outlining a clear picture of the Cloud availability issues.Our paper gives a comparative study of different techniques of detecting DOS.

scholarly journals Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

2021 ◽  
Vol 1 (2) ◽  
pp. 67-74
Author(s):  
Dalia Nashat ◽  
Fatma A. Hussain ◽  
Xiaohong Jiang
Keyword(s):  
Detection Probability ◽  
Odds Ratio ◽  
Detection Method ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Internet Routing ◽  
Denial Of Service Attack ◽  
Flooding Attack ◽  
Flooding Attacks ◽  
Service Attack

Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a new detection method for the DDoS flooding attacks. By exploring the odds ratio to determine the risk factor of any incoming traffic to the server, the legitimate and attack traffic packets can be easily differentiated. Experimental results demonstrate the efficiency of the presented detection method in terms of its detection probability and detection time.

scholarly journals Multiple intrusion detection in RPL based networks

2020 ◽  
Vol 10 (1) ◽  
pp. 467 ◽  
Author(s):  
Manjula C. Belavagi ◽  
Balachandra Muniyal
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Network Size ◽  
Sensor Nodes ◽  
Machine Learning Techniques ◽  
Lossy Networks ◽  
Malicious Nodes ◽  
Denial Of Service Attack

<span lang="EN-US">Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.</span>

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