Improved BP Neural Network for Intrusion Detection Based on AFSA

Wei, Lihao; Ai, Jieqing

doi:10.2991/isci-15.2015.51

Cited by 5 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [14], the authors proposed a method to train a Back Propagation Neural Network (BPNN) model as a network threats detector, based on Artificial Fish Swarming Algorithm (AFSA), they address the problem of falling into local minima, the results in this work showed that the proposed method had higher accuracy and faster convergence speed with the comparison to BPNN, Back Propagation Neural Network by Particle Swarm Optimization (PSO-BPNN), and Back Propagation Neural Network with Genetic Algorithm (GA-BPNN). In [15], an ANN is used to create a learning-based filter to detect spam E-mails, and the authors proposed an optimizing algorithm to set the weights and biases of the Feedforward Neural Network (FFNN) model to the best possible value using a new modified bat algorithm.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Worm Detection System Based on Neural Network

Menassel,

Gattal,

Ahmed

2023

INDJCSE

View full text Add to dashboard Cite

Among the numerous methods found in the literature that researchers used to develop intrusion detection systems, Artificial Neural Networks (ANN) were the most used machine learning techniques, which is why they were chosen as the main focus in this study, which we relied on to develop a new model that can detect network anomalies. The training stage of ANN is considered the main step of building a predictive model for computer worms to attack, there are different algorithms in the literature to get that done, and including deterministic methods and stochastic ones, each has its pros and cons. to train our proposed model, we relied, within this study, on the improved 'tree-seed algorithm' which is a nature-inspired algorithm. The proposed model was trained with an improved dataset from its predecessor that was extracted from a simulation of the US Air Force network, and it was evaluated based on the test part, which contains types of attacks that the model has not previously trained on, the results obtained indicate good learning capabilities of the proposed model comparing to the results of two other models based on two stochastic algorithms, namely the 'genetic algorithm' and 'particle swarm optimization. The experimental results obtained on, NSL-KDD database show that the proposed scheme achieves interesting performances.

show abstract

Section: Related Workmentioning

confidence: 99%

A New Approach for Worm Detection System Based on Neural Network

Menassel,

Gattal,

Ahmed

2023

INDJCSE

View full text Add to dashboard Cite

show abstract

“…Several ANN approaches have been used in enhanced IDS environments. In article [39], Tian, Lihao, and Jieqing, they developed a detection model based on training BP neural network using Artificial Fish Swarming Algorithm (AFSA). The algorithm optimizes the weights of BP neural network, shortens the sample training time and improves the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons

et al. 2020

View full text Add to dashboard Cite

One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods. INDEX TERMS Intrusion Detection System (IDS); Multilayer Perceptron (MLP); Metaheuristic Algorithm (MA); Artificial Bee Colony Algorithm (ABC); Dragonfly algorithm (DA)

show abstract

“…(1) Threshold type: (2) (2) A piecewise linear model (3) (3) S-type function: (4) (4) Competitive function: (5) Among them, the competitive decision-making function has been used in probabilistic neural network output as the output of Bayes decision. Model layer: The total number of nodes is Summation Layer: According This layer nodes is M,…”

Section: Figure 2 Multiple Input Neuronsmentioning

confidence: 99%

Quantitative Investment Based on Artificial Neural Network Algorithm

Zhang¹

2015

IJUNESST

View full text Add to dashboard Cite

Improved BP Neural Network for Intrusion Detection Based on AFSA

Cited by 5 publications

References 11 publications

A New Approach for Worm Detection System Based on Neural Network

A New Approach for Worm Detection System Based on Neural Network

An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons

Quantitative Investment Based on Artificial Neural Network Algorithm

Contact Info

Product

Resources

About