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1. TL-NID: Deep Neural Network with Transfer Learning for Network Intrusion Detection

   https://doi.org/10.23919/ICITST51030.2020.9351317  

   Masum, Mohammad; Shahriar, Hossain (December 2020, 15th International Conference for Internet Technology and Secured Transactions (ICITST))

   null (Ed.)

   Network intrusion detection systems (NIDSs) play an essential role in the defense of computer networks by identifying a computer networks' unauthorized access and investigating potential security breaches. Traditional NIDSs encounters difficulties to combat newly created sophisticated and unpredictable security attacks. Hence, there is an increasing need for automatic intrusion detection solution that can detect malicious activities more accurately and prevent high false alarm rates (FPR). In this paper, we propose a novel network intrusion detection framework using a deep neural network based on the pretrained VGG-16 architecture. The framework, TL-NID (Transfer Learning for Network Intrusion Detection), is a two-step process where features are extracted in the first step, using VGG-16 pre-trained on ImageNet dataset and in the 2 nd step a deep neural network is applied to the extracted features for classification. We applied TL-NID on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the performance of the proposed framework. The experimental results show that our proposed method can effectively learn from the NSL-KDD dataset with producing a realistic performance in terms of accuracy, precision, recall, and false alarm. This study also aims to motivate security researchers to exploit different state-of-the-art pre-trained models for network intrusion detection problems through valuable knowledge transfer. 

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2. Reconstructing Classification to Enhance Machine-Learning Based Network Intrusion Detection by Embracing Ambiguity

   Song, Chungsik; Fan, Wenjun; Chang, Sang-Yoon; Park, Younghee (December 2020, Silicon Valley Cybersecurity Conference)

   null (Ed.)

   Network intrusion detection systems (IDS) has efficiently identified the profiles of normal network activities, extracted intrusion patterns, and constructed generalized models to evaluate (un)known attacks using a wide range of machine learning approaches. In spite of the effectiveness of machine learning-based IDS, it has been still challenging to reduce high false alarms due to data misclassification. In this paper, by using multiple decision mechanisms, we propose a new classification method to identify misclassified data and then to classify them into three different classes, called a malicious, benign, and ambiguous dataset. In other words, the ambiguous dataset contains a majority of the misclassified dataset and is thus the most informative for improving the model and anomaly detection because of the lack of confidence for the data classification in the model. We evaluate our approach with the recent real-world network traffic data, Kyoto2006+ datasets, and show that the ambiguous dataset contains 77.2% of the previously misclassified data. Re-evaluating the ambiguous dataset effectively reduces the false prediction rate with minimal overhead and improves accuracy by 15%. 

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3. Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

   https://doi.org/10.1109/BigData52589.2021.9671576  

   Masum, Mohammad; Shahriar, Hossain; Haddad, Hisham; Faruk, Md Jobair; Valero, Maria; Khan, Md Abdullah; Rahman, Mohammad A.; Adnan, Muhaiminul I.; Cuzzocrea, Alfredo; Wu, Fan (December 2021, 2021 IEEE International Conference on Big Data (Big Data))

   Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework’s effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. 

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4. Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

   https://doi.org/10.1186/s40537-023-00815-3  

   Rostami, Masoud A.; Balmaki, Behnaz; Dyer, Lee A.; Allen, Julie M.; Sallam, Mohamed F.; Frontalini, Fabrizio (December 2023, Journal of Big Data)

   Abstract Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field. 

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5. Shining New Light on Useful Features for Network Intrusion Detection Algorithms

   https://doi.org/10.1109/CCNC49033.2022.9700654  

   Lawrence, Heather; Ezeobi, Uchenna; Bloom, Gedare; Zhuang, Yanyan (January 2022, 2022 IEEE 19th Annual Consumer Communications Networking Conference (CCNC))

   Network intrusion detection systems (NIDS) today must quickly provide visibility into anomalous behavior on a growing amount of data. Meanwhile different data models have evolved over time, each providing a different set of features to classify attacks. Defenders have limited time to retrain classifiers, while the scale of data and feature mismatch between data models can affect the ability to periodically retrain. Much work has focused on classification accuracy yet feature selection is a key part of machine learning that, when optimized, reduces the training time and can increase accuracy by removing poorly performing features that introduce noise. With a larger feature space, the pursuit of more features is not as valuable as selecting better features. In this paper, we use an ensemble approach of filter methods to rank features followed by a voting technique to select a subset of features. We evaluate our approach using three datasets to show that, across datasets and network topologies, similar features have a trivial effect on classifier accuracy after removal. Our approach identifies poorly performing features to remove in a classifier-agnostic manner that can significantly save time for periodic retraining of production NIDS. 

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