TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

A Rare Example of Pitfall in Corporate Data Modeling Practices for information systems

Although Entity Relationship Model have turned out to be de facto corporate data modeling vehicle, current exercise for its application to real-world business processes is disclosed to surprisingly face a pressing peril of an alarming level in terms of data consistency and the degree of unnecessary data redundancy. In this paper, the matter-of-fact legacy practice, once thought to be a confidential or secret, obtained from some major broadcasting company is articulately reported and its demerits as well as its antagonistic side effect due to abnormality are discussed in depth. We were able to remold such an ill-manifested case to a legitimate ER model vindicated through an endeavor of a couple of months long. The two cases then are compared both qualitatively and quantitatively. The result of analysis has shown that illformed data models contain more than a ratio of 40 percent of unnecessary data redundancy, which leads us to have an implication that it is heavily contingent to seek a corrective measure for cutting down the ratio

Personalized Federated Learning for Statistical Heterogeneity

The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL

Corporate Data Obesity: 50 Percent Redundant

In this essay, we report what we have observed with regard to status quo of corporate information systems in real world from our experiences of twenty years of data management practices. It is considered to be serious in that data are too conveniently and frequently replicated to make information systems improperly behave in terms of their quality standards including response time. Average ratio of data replication in a site is astonishingly judged to be more than 50 percent of a whole corporate database. It is in reality about 65 percent in average to our knowledge. Presenting this paper to academia has been motivated by our strong belief and evidence that most of the redundancy can effectively and systemically be removed from the very start of information system development. We also noted that field workers including database administrators in corporate environment tend to think data part of IS and program part of IS mixed together from the start of IS design and popularity of this tendency eventually caused a lot of entanglement that could hardly be dealt with later by themselves. We therefore present a couple of mandates that must be respected in order not to get involved in such a perplexity

Gender inequality among champions and players’ reception of gender disproportion of utility support champions in league of legends

Designing female character in video game has been criticized as being sexually objectified and underrepresented in quantity (number of characters and their appearance rate in the game) and in quality (take only secondary role and inferior ability statistics given). In this paper, we analyze world leading multiple-user online battle arena game league of legends to see if previously criticized gender inequality of champions still stands and conduct a survey of 1,403 players of that game and asks how they feel about serious gender disproportion of utility support champions (all females). The result shows that league of legends still has serious gender disparity in performance parameters and there has been only a small change in 5-year span (2014-2019). The survey result tells us that game players also feel political incorrectness of such gender disproportion, but they accept such gender prototype because they have been taught as such as social role theory explains gender inequality issues

A Rare Example of Pitfall in Corporate Data Modeling Practices for information systems

Although Entity Relationship Model have turned out to be de facto corporate data modeling vehicle, current exercise for its application to real-world business processes is disclosed to surprisingly face a pressing peril of an alarming level in terms of data consistency and the degree of unnecessary data redundancy. In this paper, the matter-of-fact legacy practice, once thought to be a confidential or secret, obtained from some major broadcasting company is articulately reported and its demerits as well as its antagonistic side effect due to abnormality are discussed in depth. We were able to remold such an ill-manifested case to a legitimate ER model vindicated through an endeavor of a couple of months long. The two cases then are compared both qualitatively and quantitatively. The result of analysis has shown that illformed data models contain more than a ratio of 40 percent of unnecessary data redundancy, which leads us to have an implication that it is heavily contingent to seek a corrective measure for cutting down the ratio

Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity.

ObjectiveEmerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time.MethodsSecondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time.ResultsOne hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05).ConclusionsDistinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs