Training MLP neural network to reduce false alerts in IDS

Barapatre, P.; Tarapore, Noshir; Pukale, S. G.; Dhore, M. L.

doi:10.1109/icccnet.2008.4787714

Cited by 13 publications

(3 citation statements)

References 4 publications

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“…In conclusion, the model achieves the best performance when normal_log � 11, sequence_window � 13, num_layers � 2, and hidden_size � 64. e further comparative study showed that the proposed method has better performance than other deep learning algorithms and traditional machine learning algorithms. is paper selects MLP [41], RBM [42], SVM [43], and naive Bayes algorithms [44] for comparative analysis, uses comprehensive F1-score indicators for comparison, and uses training samples of different magnitudes. As shown in Figure 10, the algorithm proposed in this paper achieves a more accurate recognition rate than traditional machine learning algorithms and has better results than ordinary deep learning algorithms.…”

Section: Word2vecmentioning

confidence: 99%

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Zhong

Zhou

Chen

2021

Security and Communication Networks

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Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

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Section: Word2vecmentioning

confidence: 99%

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Zhong

Zhou

Chen

2021

Security and Communication Networks

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“…Barapatre et al [24] experimented with an input layer, a hidden layer, and an output layer in the neural network. The input node contained 41 features of the KDD Cup'99 dataset.…”

Section: A Supervised Learningmentioning

confidence: 99%

Detecting Stealth-based Attacks in Large Campus Networks

Al-Fawa’reh¹

2020

IJATCSE

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Detecting and classifying new malicious network traffic is a high priority concern for cybersecurity practitioners. New stealth or zero-day attack can make companies go out of businesses in the digital transformation era. Despite the plethora of studies that have explored different machine-learning (ML) techniques to address this issue, the most popular used approach remains traditional ML with legacy datasets and small campus network. The difficulty in data collection considers the biggest impediment of using ML. This paper examines the possibility of exposing zero-day malicious network traffic in large campus networks based on cloud environments by presenting a lightweight framework. An experiment was devised for the analysis. However, before that, the characteristics of the network were examined based on the flow level. The framework showed an outperformed accuracy rate of 100% for a specific type of attack and 97.97% as a comprehensive detection mechanism.

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“…Prachi Barapatre et al [4] used MLP with backpropagation algorithm to classify attacks. Dataset used was KDD99 dataset.…”

Section: Related Workmentioning

confidence: 99%

A novel approach to ensemble MLP and random forest for network security

Deore¹,

Kyatham²,

Narkhede³

2020

ITM Web Conf.

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The following paper provides a novel approach for Network Intrusion Detection System using Machine Learning and Deep Learning. This approach uses two MLP (Multi-Layer Perceptron) models one having 3 layers and other having 6 layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and also the testing time is reduced. Researchers have implemented various ways for the ensemble of multiple models but we are using contradiction management concept to ensemble machine learning models. Contradiction Management concept means if two machine learning models are contradicting in their decisions (in our case 3-layer MLP and Random Forest), then the third model’s (6-layer MLP) decision is considered whose accuracy is higher than the previous models. The third model is only used for testing when the previous two models contradict in their decision because the testing time of third model is higher than the two previous models as the third model has complex architecture. This approach increased the final accuracy as ensemble of multiple models is done and also testing time has reduced. The novelty of this paper is the choice and the combination of the models for the purpose of Network security.

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Training MLP neural network to reduce false alerts in IDS

Cited by 13 publications

References 4 publications

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Detecting Stealth-based Attacks in Large Campus Networks

A novel approach to ensemble MLP and random forest for network security

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