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Objectives: Cloud is becoming a very assertive computing platform now a days due to the availability of resources in a customized manner. But DDoS attack is a very dangerous as it directly affects the availability of resources. So the objective of the paper is to mitigate DDoS attack in cloud network using threshold based technique. Methods/Statist...
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The growth of web Applications have increased rapidly due to the huge development of technology with very short turnaround time and with this development the protection from vulnerabilities became very difficult. There is a continuous demand for developing new methods that is able to prevent the fast growth of attacking methods and vulnerabilities....
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... Technically, it is achieved by the transformation of information into common and uncorrelated information and calculating the simple Euclidean length for the result. This shows that Mahalanobis distance is likely to unit variable z score that gives a path to calculated length [22]. ...
... Intrusion detection systems are classified based on their implementation; either on the host or a network [4]. If an IDS is implemented on a Host then it is considered as a Host Based Intrusion detection system, while an IDS that is implemented on a network is considered as a Network-based Intrusion Detection System [5]. ...
... 34 The matrix Z i = 1,i 2,i ··· n,i contains the parameters of the ith membership function for each of the n inputs. The firing strength of each of the L fuzzy rules can be normalized by (14) Us ...
... This approach is utilizes signature and anomaly techniques to determine both known and unknown network attacks. However, the approach is ineffective as it offers very limited visibility in the host machines and cannot be used to detect intrusion for encrypted network traffic.Reference[19] proposed a network-based Intrusion Detection System by conducting a turning test for all the IP addresses in the network. It identifies faulty IPs and labels them as blacklist addresses. ...
A Distributed Denial of Service Attack (DDoS) is an attack in which multiple systems compromised by a Trojan are maliciously used to target a single system. The attack leads to the denial of a certain service on the target system. In a DDoS attack, both the target system and the systems used to perform the attack are all victims of the attack. The compromised systems are also called Botnets. These attacks occur on networked systems, among them the cloud computing facet. Scholars have tried coming up with separate mechanisms for detecting and preventing such attacks long before they occur. However, as technology progresses in advancement so do the attack mechanisms. In cloud computing, security issues affect various stakeholders who plan on cloud adoption. DDoS attacks are such serious concerns that require mitigation in the cloud. This paper presents a survey of the various mechanisms, both traditional and modern, that are applied in detecting cloud-based DDoS attacks.
... The server starts to come up short on processor and memory administrations. Assume the numerous TCP SYN assault demand is extensive and they are procedure to after some time the casualty framework will come up short on asset to react to any genuine user [8]. ...
Now-a-days the technology advancement has made significant improvement in different sectors of the society. IoT is always having an important role dealing with all these smart devices and their security. One such security issue is DOS attacks which is very frequent in IOT devices. This paper provides a solution to the experiment on a NodeMCU12e based smart switch to prevent and mitigate the attacks executed on wireless embedded devices. The major idea for developing this model is to build a suitable security mechanism for constrained devices which are part of various IOT and CPS applications. The results presented claims that when attacks are executed on the device, further communications would be halted with unidentified IP addresses using Request Capturing Security Mechanism (RCSM).
Cloud computing has gained more importance in the IT service model that offers cost-effective and scalable processing. It provides virtualized and on-demand services to the user over the internet using several networking protocols with exceptional flexibility. However, with the existing technologies and the vulnerabilities, it leads to the occurrence of several attacks in the cloud environment. Distributed Denial of Service (DDoS) is most dangerous among all the attacks which limit the cloud users to access service and resources. Therefore, the detection of DDoS in the network and the identification of attacked VMs is the most dominating task in the cloud environment. In this work, a novel DDoS attack detection mechanism is presented. The research is carried out as follows: (i) Initially DDoS attack is detected by identifying the maximum number of connections to the network, (ii) then the attacked virtual machine and non-attacked virtual machines will be clustered using Self-Organized Mapping (SOM) based Neural Network (NN). The experimental results exhibit that the presented system can efficiently detect DDoS attacks and cluster attack and non-attack VMs in an attacked cloud network. Moreover, these results demonstrate that the proposed DDoS attack prediction accuracy of 97.63% and precision of 95.4% and it is better than the existing technique.
