Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods

Towards a Standard Feature Set of NIDS Datasets

Network Intrusion Detection Systems (NIDSs) datasets are essential tools used by researchers for the training and evaluation of Machine Learning (ML)-based NIDS models. There are currently five datasets, known as NF-UNSW-NB15, NF-BoT-IoT, NF-ToN-IoT, NF-CSE-CIC-IDS2018 and NF-UQ-NIDS, which are made up of a common feature set. However, their performances in classifying network traffic, mainly using the multi-classification method, is often unreliable. Therefore, this paper proposes a standard NetFlow feature set, to be used in future NIDS datasets due to the tremendous benefits of having a common feature set. NetFlow has been widely utilised in the networking industry for its practical scaling properties. The evaluation is done by extracting and labeling the proposed features from four well-known datasets. The newly generated datasets are known as NF- UNSW-NB15-v2, NF-BoT-IoT-v2, NF-ToN-IoT-v2, NF-CSE-CIC-IDS2018-v2 and NF-UQ-NIDS-v2. Their performances have been compared to their respective original datasets using an Extra Trees classifier, showing a great improvement in the attack detection accuracy. They have been made publicly available to use for research purposes.Comment: 13 pages, 4 figures, 13 tables. arXiv admin note: substantial text overlap with arXiv:2011.0914

Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Network Intrusion Detection System:A systematic study of Machine Learning and Deep Learning approaches

The rapid advances in the internet and communication fields have resulted in ahuge increase in the network size and the corresponding data. As a result, manynovel attacks are being generated and have posed challenges for network secu-rity to accurately detect intrusions. Furthermore, the presence of the intruderswiththeaimtolaunchvariousattackswithinthenetworkcannotbeignored.Anintrusion detection system (IDS) is one such tool that prevents the network frompossible intrusions by inspecting the network traffic, to ensure its confidential-ity, integrity, and availability. Despite enormous efforts by the researchers, IDSstillfaceschallengesinimprovingdetectionaccuracywhilereducingfalsealarmrates and in detecting novel intrusions. Recently, machine learning (ML) anddeep learning (DL)-based IDS systems are being deployed as potential solutionsto detect intrusions across the network in an efficient manner. This article firstclarifiestheconceptofIDSandthenprovidesthetaxonomybasedonthenotableML and DL techniques adopted in designing network-based IDS (NIDS) sys-tems. A comprehensive review of the recent NIDS-based articles is provided bydiscussing the strengths and limitations of the proposed solutions. Then, recenttrends and advancements of ML and DL-based NIDS are provided in terms ofthe proposed methodology, evaluation metrics, and dataset selection. Using theshortcomings of the proposed methods, we highlighted various research chal-lenges and provided the future scope for the research in improving ML andDL-based NIDS

Studying machine learning techniques for intrusion detection systems

Intrusion detection systems (IDSs) have been studied widely in the computer security community for a long time. The recent development of machine learning techniques has boosted the performance of the intrusion detection systems significantly. However, most modern machine learning and deep learning algorithms are exhaustive of labeled data that requires a lot of time and effort to collect. Furthermore, it might be late until all the data is collected to train the model. In this study, we first perform a comprehensive survey of existing studies on using machine learning for IDSs. Hence we present two approaches to detect the network attacks. We present that by using a tree-based ensemble learning with feature engineering we can outperform state-of-the-art results in the field. We also present a new approach in selecting training data for IDSs hence by using a small subset of training data combined with some weak classification algorithms we can improve the performance of the detector while maintaining the low running cost