Detection Rate Of System Research Articles

ANALYSIS AND DETECTION OF INTRUSION DETECTION ON NETWORK SECURITY ENVIRONMENT USING ML

The Intrusion Detection System (IDS) is a critical cyber security instrument that monitors and detects intrusion threats. This research intends to examine contemporary IDS research utilizing a Machine Learning (ML) methodology, with a focus on datasets, ML algorithms, and metrics. The choice of datasets is critical for ensuring that the model is suitable for IDS use. Traditional methods, such as firewalls, which focus on data filtering, may not be adequate for detecting all forms of assaults in a timely manner. Machine learning algorithms-based on intrusion detection systems (IDS) are especially good in efficiently processing vast amounts of data in order to identify any malicious behavior for effective handling and prompt detection of these types of attacks. Machine learning-based intrusion detection systems (IDS) are used to monitor all network traffic for malicious activity. In order to improve the intrusion detection system's detection rate, the system's focus was on false negative and false positive performance measures. The results of this paper showed that the ML model XGBoost classifier had the best accuracy rate, while the Decision tree classifier had the lowest. Key Words: IDS, XGBoost Algorithm, Decision tree

Machine Learning Techniques for Malware Detection

Abstract—Malware is a term used to describe the types of malicious software that can be used to infect a single computer or the network of an entire company. Viruses and malware are among the most serious risks to online safety that exist right now. There is a serious threat to world security since the volume of malware is increasing at an alarming rate. In order to prevent detection by any antivirus software, all current malware applications tend to incorporate several polymorphic layers or side mechanisms that automatically update themselves at short intervals so that they can remain undetected for longer periods of time. For the identification of malware, we present a flexible framework that allows the use of various machine learning methods, such as decision trees, random forests, and so on. The system's detection rate is greatly improved by selecting the algorithm with the highest level of accuracy. False positive and false negative rates are calculated using the confusion matrix in order to evaluate the system' s overall performance. Keywords—Malware, Malware Detection, Machine Learning, Malware Analysis

Intrusion detection by initial classification-based on protocol type

Increased use of computer networks, internet and online transactions pose higher risk of intrusions and protecting the information from the hackers/intruders is a new area in computers and network security. The major factors which affect intrusion detection are the system's detection rate and time required to detect intrusions. Many researchers have focused in this area and have used data mining techniques for detecting the intrusions. This paper proposes to classify the dataset initially based on 'protocol type' feature and the performance improvements over traditional way of considering the full data without initial classification. This paper does not advocate any techniques or algorithms, but establishes the fact that by splitting the dataset on Protocol Type feature enhances performance with respect to detection rate and time to build model for intrusion detection. In this study, the well-known KDD Cup 99 intrusion dataset has been tested with the proposed approach. The computational study reveals that the initial classification based on protocol type' attribute increases the performance with respect to rate of detection and time to build model.

Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis.

Facial analysis systems are becoming available to healthcare providers to aid in the recognition of dysmorphic phenotypes associated with a multitude of genetic syndromes. These technologies automatically detect facial points and extract various measurements from images to recognize dysmorphic features and evaluate similarities to known facial patterns (gestalts). To evaluate such systems' usefulness for supporting the clinical practice of healthcare professionals, the recognition accuracy of the Cornelia de Lange syndrome (CdLS) phenotype was examined with FDNA's automated facial dysmorphology novel analysis (FDNA) technology. In the first experiment, 2D facial images of CdLS patients with either an NIPBL or SMC1A gene mutation as well as non-CdLS patients which were assessed by dysmorphologists in a previous study were evaluated by the FDNA technology; the average detection rate of experts was 77% while the system's detection rate was 87%. In the second study, when a new set of NIPBL, SMC1A and non-CdLS patient photos was evaluated, the detection rate increased to 94%. The results from both studies indicated that the system's detection rate was comparable to that of dysmorphology experts. Therefore, utilizing such technologies may be a useful tool in a clinical setting.

Health Insurance Claim Review Using Information Technologies

ObjectivesThe objective of this paper is to describe the Health Insurance Review and Assessment Service (HIRA)'s payment request (PARE) system that plays the role of the gateway for all health insurance claims submitted to HIRA, and the claim review support (CRS) system that supports the work of claim review experts in South Korea.MethodsThis study describes the two systems' information technology (IT) infrastructures, their roles, and quantitative analysis of their work performance. It also reports the impact of these systems on claims processing by analyzing the health insurance claim data submitted to HIRA from April 1 to June 30, 2011.ResultsThe PARE system returned to healthcare providers 2.7% of all inpatient claims (97,930) and 0.1% of all outpatient claims (317,007) as un-reviewable claims. The return rate was the highest for the hospital group as 0.49% and the lowest rate was found in clinic group. The CRS system's detection rate of the claims with multiple errors in inpatient and outpatient areas was 23.1% and 2.9%, respectively. The highest rate of error detection occurred at guideline check-up stages in both inpatient and outpatient groups.ConclusionsThe study found that HIRA's two IT systems had a critical role in reducing heavy administrative workloads through automatic data processing. Although the return rate of the problematic claims to providers and the error detection rate by two systems was low, the actual count of the returned claims was large. The role of IT will become increasingly important in reducing the workload of health insurance claims review.