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1. A Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection

   Mohammad Masum, Hossain Shahriar (April 2021, International journal of intellligent computing research)

   Ayahiko Niimi, Future University-Hakodate (Ed.)

   Traditional Network Intrusion Detection Systems (NIDS) encounter difficulties due to the exponential growth of network traffic data and modern attacks' requirements. This paper presents a novel network intrusion classification framework using transfer learning from the VGG-16 pre-trained model. The framework extracts feature leveraging pre-trained weights trained on the ImageNet dataset in the initial step, and finally, applies a deep neural network to the extracted features for intrusion classification. We applied the presented framework on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the proposed framework's performance. We also implemented other pre-trained models such as VGG19, MobileNet, ResNet-50, and Inception V3 to evaluate and compare performance. This paper also displays both binary classification (normal vs. attack) and multi-class classification (classifying types of attacks) for network intrusion detection. The experimental results show that feature extraction using VGG-16 outperforms other pre-trained models producing better accuracy, precision, recall, and false alarm rates. 

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2. Feature Vector Difference based Neural Network and Logistic Regression Models for Authorship Verification

   Weerasinghe, Janith; Greenstadt, Rachel (January 2020, CEUR workshop proceedings)

   Cappellato, Linda; Eickhoff, Carsten; Ferro, Nicola; Névéol, Aurélie (Ed.)

   This paper describes the approach we took to create a machine learning model for the PAN 2020 Authorship Verification Task. For each document pair, we extracted stylometric features from the documents and used the absolute difference between the feature vectors as input to our classifier. We created two models: a Logistic Regression Model trained on a small dataset, and a Neural Network based model trained on the large dataset. These models achieved AUCs of 0.939 and 0.953 on the small and large datasets, making them the second-best models on both datasets submitted to the shared task. 

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3. Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

   https://doi.org/10.3847/1538-4365/ad21e4  

   Alshammari, Khaznah; Hamdi, Shah Muhammad; Filali Boubrahimi, Soukaina (March 2024, The Astrophysical Journal Supplement Series)

   Abstract Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes. 

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4. Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data

   https://doi.org/10.1145/3307339.3343482  

   Eslami, Taban; Saeed, Fahad (January 2019, Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)

   Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS). 

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5. Protect privacy of deep classification networks by exploiting their generative power

   https://doi.org/10.1007/s10994-021-05951-6  

   Chen, Jiyu; Guo, Yiwen; Zheng, Qianjun; Chen, Hao (April 2021, Machine Learning)

   null (Ed.)

   Abstract Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a new framework to defend against this sort of attack. Our key insight is that if we retrain the original classifier with a new dataset that is independent of the original training set while their elements are sampled from the same distribution, the retrained classifier will leak no information that cannot be inferred from the distribution about the original training set. Our framework consists of three phases. First, we transferred the original classifier to a Joint Energy-based Model (JEM) to exploit the model’s implicit generative power. Then, we sampled from the JEM to create a new dataset. Finally, we used the new dataset to retrain or fine-tune the original classifier. We empirically studied different transfer learning schemes for the JEM and fine-tuning/retraining strategies for the classifier against shadow-model attacks. Our evaluation shows that our framework can suppress the attacker’s membership advantage to a negligible level while keeping the classifier’s accuracy acceptable. We compared it with other state-of-the-art defenses considering adaptive attackers and showed our defense is effective even under the worst-case scenario. Besides, we also found that combining other defenses with our framework often achieves better robustness. Our code will be made available at https://github.com/ChenJiyu/meminf-defense.git . 

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