Improved Feature Selection Algorithm for Intrusion Detection Using Data Mining Approach

With the rapid growth of Internet applications, there are more and more intrusions into network systems. In this case, it is necessary to provide security for the network through effective intrusion detection and prevention methods. This can mainly be achieved by creating effective interruption detection systems using efficient algorithms that can identify abnormal activities in network traffic and safeguard network resources from unlawful attack by interlopers. Although many interruption recognition frameworks have been proposed before, existing network intrusion detection has limitations in terms of accuracy and detection time. To overcome these shortcomings, In this paper, we propose a new intrusion detection system by developing a new intelligent feature selection algorithm based on conditional random fields (CRF) to optimize the number of features. Furthermore, algorithms based on existing hierarchical methods (LA) In this paper, we propose another interrupt recognition framework, fostering a book. Compared with the existing methods, the interruption identification framework provides high precision and achieves the efficiency of attack detection. The main advantages of this system are reduced detection time, improved classification accuracy and lower false positive rate

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach

Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease’s total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors’ class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions

Виявлення мережевих аномалій за допомогою систем штучного інтелекту

Магістерська дисертація: 123с., 4 ч., 35 табл., 16 рис., 1 дод., 48 джерел. Об’єктом дослідження є мережеві аномалії. Предметом дослідження є використання мережевих аномалій для виявлення вторгнень. Мета роботи – розробити систему виявлення мережевих аномалій на основі досліджених алгоритмів та методів машинного навчання. Методи дослідження – статистичні методи, класифікаційні метод, методи на базі кластеризації, методи на базі знань, комбіновані методи. Актуальність – виявлення вторгнень та миттєве сповіщення адміністраторів мережі про потенційну загрозу інфраструктурі. Система перешкоджає зловмисникам отримати несакціонований доступ до мережі за допомогою як відомих так і невідомих атак. Новизна – на відміну від ручного адміністрування, автоматизована система дозволяє зекономити ресурси та не допускає помилки через людський фактор. Результати дослідження – побудована модель для автоматичного виявлення мережевих аномалій для запобігання вторгнень у мережу або інфраструктуру.Masters’ thesis: 123p., 4 s., 35 tabl., 16 fig., 1 appendix., 48 references. The object of this research is network anomalies. The subject of the research is the use of network anomalies for intrusion detection. The purpose of the work is to develop a system for detecting network anomalies based on the studied algorithms and methods of machine learning. Methods of the study – statistical methods, classification, clustering, knowledge base, combination learning. The relevance of the study – Intrusion detection and immediate notification of network administrators about a potential threat to the infrastructure. The system prevents intruders from accessing the network through both known and unknown attacks. Novelty – In contrast to manual administration, an automated system saves resources and does not make mistakes due to human factor. The results of the study – A model was built to automatically detect network anomalies to prevent intrusion into the network or infrastructure

Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions

This research focuses mainly on the binary class imbalance problem in data mining. It investigates the use of combined approaches of data and algorithmic level solutions. Moreover, it examines the use of swarm intelligence and population-based techniques to combat the class imbalance problem at all levels, including at the data, algorithmic, and feature level. It also introduces various solutions to the class imbalance problem, in which swarm intelligence techniques like Stochastic Diffusion Search (SDS) and Dispersive Flies Optimisation (DFO) are used. The algorithms were evaluated using experiments on imbalanced datasets, in which the Support Vector Machine (SVM) was used as a classifier. SDS was used to perform informed undersampling of the majority class to balance the dataset. The results indicate that this algorithm improves the classifier performance and can be used on imbalanced datasets. Moreover, SDS was extended further to perform feature selection on high dimensional datasets. Experimental results show that SDS can be used to perform feature selection and improve the classifier performance on imbalanced datasets. Further experiments evaluated DFO as an algorithmic level solution to optimise the SVM kernel parameters when learning from imbalanced datasets. Based on the promising results of DFO in these experiments, the novel approach was extended further to provide a hybrid algorithm that simultaneously optimises the kernel parameters and performs feature selection