A novel intrusion detection method based on threshold modification using receiver operating characteristic curve

Jun, Luo; Chai, Senchun; Zhang, Baihai; Xia, Yuanqing; Gao, Jianlei; Zeng, Guoqiang

doi:10.1002/cpe.5690

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…For this purpose, AUC and F-score indexes will be used, which are good choices for problems dealing with imbalanced data (Sokolova, Japkowicz & Szpakowicz, 2006). AUC indicates the area below the diagram in the ROC curve, and the ROC curve is a method for judging the performance of a two-class classifier (Luo et al, 2020). In the ROC curve, the vertical axis is the TPR (represents the true positive rate), Also, the horizontal axis is FPR (represents the false positive rate).…”

Section: Discussionmentioning

confidence: 99%

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Shumaly

Yazdinejad

Guo

2021

PeerJ Computer Science

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Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

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Section: Discussionmentioning

confidence: 99%

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Shumaly

Yazdinejad

Guo

2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…For this purpose, AUC and F-score indexes will be used, which are good choices for problems dealing with imbalanced data (Sokolova, Japkowicz, and Szpakowicz 2006). AUC indicates the area below the diagram in the ROC curve, and the ROC curve is a method for judging the performance of a two-class classifier (Luo et al 2020). In the ROC curve, the vertical axis is the TPR (represents the true positive rate), Also, the horizontal axis is FPR (represents the false positive rate).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings (v0.1)"

2021

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Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers' opinions about products assists to maintain their competitive conditions. We intend to analyze the users' opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews have been gathered in Persian from the Digikala website using web-mining techniques and have been used the fastText method to create a word embedding. It has been assumed it would dramatically cut down the need for the text pre-processing through the skip-gram method considering the position of the words in the sentence and the words' relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance.In addition, the results of the CNN, BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC, and 0.956 F-score using fastText and CNN. In this article, not only it has been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is also better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

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“…The data distribution after sampling is shown in the Table 7 below. Figure 14 shows the detection rate of the proposed intrusion detection model on Normal and 9 types of attack data, compared with CMLW [47], TSDL [48] and Thresholdoptimized CNNs [49]. Among them, PSO-LightGBM has an excellent detection rate for most of the data's type, especially in the detection of Backdoor, Shellcode and Worms, the three low frequency attacks, reached 51.28%, 64.47% and 77.78%, respectively, which is much higher than the other three IDSs listed, showing the excellent anomaly detection ability of this model.…”

Section: Experimental Evaluation a Evaluation Metricsmentioning

confidence: 99%

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

et al. 2021

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With the advent of the "Internet plus" era, the Internet of Things (IoT) is gradually penetrating into various fields, and the scale of its equipment is also showing an explosive growth trend. The age of the "Internet of Everything" is coming. The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT. Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient descent (PSO-LightGBM) for the intrusion detection. In this method, PSO-LightGBM is used to extract the features of the data and inputs it into one-class SVM (OCSVM) to discover and identify malicious data. The UNSW-NB15 dataset is applied to verify the intrusion detection model. The experimental results show that the model we propose is very robust in detecting either normal or various malicious data, especially small sample data such as Backdoor, Shellcode and Worms. INDEX TERMS Intrusion detection, Internet of Things (IoT), particle swarm optimization (PSO), oneclass SVM(OCSVM)

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A novel intrusion detection method based on threshold modification using receiver operating characteristic curve

Cited by 12 publications

References 27 publications

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Peer Review #2 of "Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings (v0.1)"

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

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