Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection

2021 ◽  
Author(s):  
Shubhra Dwivedi ◽  
Manu Vardhan ◽  
Sarsij Tripathi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Optimization Algorithm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Binary Grasshopper Optimization Based Feature Selection For Intrusion Detection System Using Feed Forward Neural Network Classifier

2020 ◽  
Vol 13 ◽  
Author(s):  
M. Jeyakarthic ◽  
A. Thirumalairaj
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Superior Performance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Background: Due to the advanced improvement in internet and network technologies, significant number of intrusions and attacks takes place. An intrusion detection system (IDS) is employed to prevent distinct attacks. Several machine learning approaches has been presented for the classification of IDS. But, IDS suffer from the curse of dimensionality that results to increased complexity and decreased resource exploitation. Consequently, it becomes necessary that significant features of data must be investigated by the use of IDS for reducing the dimensionality. Aim: In this article, a new feature selection (FS) based classification system is presented which carries out the FS and classification processes. Methods: Here, the binary variants of the Grasshopper Optimization Algorithm called BGOA is applied as a FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to the feed forward neural network (FFNN) model to train and test the KDD99 dataset. Results: The validation of the presented model takes place using a benchmark KDD Cup 1999 dataset. By the inclusion of FS process, the classifier results gets increased by attaining FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, Fscore of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome ensured the superior performance of the presented model compared to diverse models under several aspects and is found to be an appropriate tool for detecting intrusions.

scholarly journals An Intelligent Feature Selection with Optimal Neural Network Based Network Intrusion Detection System for Cloud Environment

2020 ◽  
Vol 9 (3) ◽  
pp. 3560-3569
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cloud Environment ◽  
Sensitive Data ◽  
High Detection Rate ◽  
Security Issues ◽  
Network Intrusion ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

At present times, Cloud Computing (CC) becomes more familiar in several domains such as education, media, industries, government, and so on. On the other hand, uploading sensitive data to public cloud storage services involves diverse security issues, specifically integrity, availability and confidentiality to organizations/companies. Besides, the open and distributed (decentralized) structure of the cloud is highly prone to cyber attackers and intruders. Therefore, it is needed to design an intrusion detection system (IDS) for cloud environment to achieve high detection rate with low false alarm rate. The proposed model involves a binary grasshopper optimization algorithm with mutation (BGOA-M) as a feature selector to choose the optimal features. For classification, improved particle swarm optimization (IPSO) based NN model, called IPSO-NN has been derived. The significance of the IPSO-NN model is assessed using a set of two benchmark IDS dataset. The experimental results stated that the IPSO-NN model has achieved maximum accuracy values of 99.36% and 97.80% on the applied NSL-KDD 2015 and CICIDS 2017 dataset. The obtained experimental outcome clearly pointed out the extraordinary detection performance of the IPSO-NN model over the compared methods.

Hybrid Intrusion Detection Framework for Ad hoc networks

2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Security Framework ◽  
Detection Techniques ◽  
Wide Range ◽  
Hoc Networks

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

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.

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