An Efficient Approach to Develop an Intrusion Detection System Based on Multi Layer Backpropagation Neural Network Algorithm

Sen, Rinku; Chattopadhyay, Manojit; Sen, Nilanjan

doi:10.1145/2751957.2751979

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…Also, the genetic approaches for IDS are very much successful as reported by S. Malhotra et al [4]. Nonetheless, the works by R. Sen et al [5] using artificial neural network, M. Tabatabaefar et al [6] using artificial immune systems and the work by T. Mehmood et al [7] using ant colony optimization have demonstrated higher accuracy but at the cost of higher time complexity and at the compromise of newer attack detection. Elaborating the same, although SVM based IDS can enhance IDS execution regarding identification rate and learning speed contrasted and conventional calculations, opportunity to get better still exists.…”

Section: Outcome Of the Parallel Research Workmentioning

confidence: 97%

Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Sudhakar*,

Narsimha,

Narsimaha

2019

IJEAT

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With the rapid growth in the data processing and data sharing, the application owners and the consumers of the applications are more influenced to use the remote storage on cloud-based data centre and the application generated data is also growing ups and bounds. Nevertheless, the adaptation of the data sharing, and data processing applications were not easy for the consumers. The application owners and the service providers have struggled with the sensitive data of the consumers and the consumers were also faced trust issues with the complete framework. The standard legacy applications were designed for the traditional centralized scenarios, where the intrusion detection can be performed only using the network status analysis and the application characteristics analysis. Moreover, most of the parallel calculations initially enhance the hybrid likelihood and change likelihood of GA as indicated by the populace advancement variable-based math and wellness esteem. Nevertheless, the population of data and the attacks on the data is high and the correct population size is highly difficult to determine. Regardless to mention, that the use of fitness functions will restrict the attack detection to certain types and these algorithms are bound to fail in case of a newer attack. However, with the migration of application to the data processing framework, the consumers have started demanding more security against the intrusions. A good number of research attempts were made to map the traditional security algorithms into the data processing space, nonetheless, the attempts were highly criticized due to the lack of proper analysis of security attacks on data processing applications. Hence, this work proposes a novel framework to detect the intrusions on data processing framework with justifying attack characteristics. This work proposes a novel algorithm to reduce the features of attack characteristics to justify the gaps on data processing frameworks with significant reduction in time for processing and further, proposes an algorithm to derive a strong rule engine to analyse the attack characteristics for detecting newer attacks. The complete proposed framework demonstrates nearly 93% and higher accuracy, which is much higher than the existing parallel research outcomes with least time complexity.

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Section: Outcome Of the Parallel Research Workmentioning

confidence: 97%

Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Sudhakar*,

Narsimha,

Narsimaha

2019

IJEAT

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“…Rinku Sen et al [20] have developed a flexible back propagation neural network (BPNN) architecture to identify the intrusion based on anomaly detection approach. To look for the best architecture, they have tested different combinations of hidden layers and various neurons in each hidden layers with three distinct percentage split of the KDD dataset, namely 6-40 percent split, 70-30 percent split and 80-20 percent split.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These nodes operate independently. BPNN is a special type of neural network [20]. It is also named error back propagation neural network, and it is a feed forward neural network which uses Multilayer Perception (MLP) as network architecture and Back Propagation Learning Algorithm as training or learning algorithm.…”

Section: Back Propagation Neural Network (Bpnn)mentioning

confidence: 99%

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

Chiba

Abghour

Moussaid

et al. 2022

Int. j. commun. netw. inf. secur.

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With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

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“…Intrusion detection of the network has become the most important part of information security defense network infrastructure. A selection of algorithms is used to identify and distinguish irregularity or assault in NIDS traffic networks, such as a Decision Tree [10], K-nearest neighbor (K-NN) [11], the naive Bayes Network [12], SOM [13], and SVM Network (ANN).…”

Section: Existing Workmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder

Moraboena¹,

Ketepalli²,

Ragam³

2020

RIA

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The security of computer networks is critical for network intrusion detection systems (NIDS). However, concerns exist about the suitability and sustainable development of current approaches in light of modern networks. Such concerns are particularly related to increasing levels of human interaction required and decreased detection accuracy. These concerns are also highlighted. This post presents a modern intrusion prevention deep learning methodology. For unattended function instruction, we clarify our proposed Symmetric Deep Autoencoder (SDAE). Also, we are proposing our latest deep research classification model developed with stacked SDAEs. The classification proposed by the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Canadian Institute for Cybersecurity -Intrusion Detection System (CICIDS 2017) data sets was implemented in Tensor Flow, a Graphics Procedure Unit (GPU) enabled and evaluated. We implemented and tested our experiment with different batch sizes using Adam optimizer. Promising findings from our model have been achieved so far, which demonstrates improvements over current solutions and the subsequent improvement for use in advanced NIDS.

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An Efficient Approach to Develop an Intrusion Detection System Based on Multi Layer Backpropagation Neural Network Algorithm

Cited by 15 publications

References 5 publications

Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder

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