A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Abstract

SummaryIn cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment

“…Next, taking the detection rate and false detection rate as the evaluation criteria, the proposed ALR-PSO-BP detection algorithm is compared and analyzed with the algorithms in reference [16][17][18] in the case of various types of samples, and 10 tests are carried out, respectively, to obtain the average value. Te fnal calculation results of the average detection rate and false detection rate of diferent algorithms are shown in Tables 3 and 4.…”

“…However, the detection efciency of this algorithm is low and the speed is slow. Aiming at the problem that the traditional intrusion detection system in the cloud is vulnerable to attack and can not maintain a balance between sensitivity and accuracy, reference [17] proposed a network intrusion detection system in the cloud computing environment. However, this method is difcult to adapt to a network with a large amount of data.…”

Intelligent monitoring of malicious intrusion behavior for power communication network channel

“…Combined with Table 1, before intelligent monitoring of malicious intrusion behaviors in power communication network channels, this paper first collects and extracts relevant network performance parameters (network channel throughput, packet loss rate, etc. ), and normalizes them [17][18]. It should be noted that under the attack of malicious intrusion of different power communication network channels, the impact on the collected parameters is also different.…”

An Efficient Support Vector Machine Algorithm Based Network Outlier Detection System

“…3) Network Features Selection The network connection is described as a vector of network features representing the connection behaviour. The information contribution of these features concerning the connection behaviour label is varied [39]. Many features hold less information about the connection behaviour denoted by irrelevant features, while others contain redundant information denoted by redundant features.…”