The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.
Network security is of primary concerned now days for large organizations. The intrusion detection systems (IDS) are becoming indispensable for effective protection against attacks that are constantly changing in magnitude and complexity. With data integrity, confidentiality and availability, they must be reliable, easy to manage and with low maintenance cost. Various modifications are being applied to IDS regularly to detect new attacks and handle them. This paper proposes a fuzzy genetic algorithm (FGA) for intrusion detection. The FGA system is a fuzzy classifier, whose knowledge base is modelled as a fuzzy rule such as "if-then" and improved by a genetic algorithm. The reasons for introducing fuzzy logic is twofold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus fuzzy association rules can be mined to find the abstract correlation among different security features. The method is tested on the benchmark KDD'99 intrusion dataset and compared with other existing techniques available in the literature. The results are encouraging and demonstrate the benefits of the proposed approach.
As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviours, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. This paper compares the performance of Intrusion Detection System (IDS) Classifiers using various feature reduction techniques. To enhance the learning capabilities and reduce the computational intensity of competitive learning comparing the performance of the algorithms is performed respectively, different dimension reduction techniques have been proposed. These include: classifying and clustering Algorithms Naïve Bayes, Simple k mean, Decision tree and J48, Linear Discriminate Analysis, and Independent Component Analysis. Many Intrusion Detection Systems are based on neural networks. However, they are computationally very demanding. This paper provides a review on current trends in intrusion detection together with a study on technologies implemented by some researchers in this research area. We try to build as system which create clusters from its input data by labelling clusters as normal or anomalous data instances and finally used these cluster to classify unseen network data instances as either normal or anomalous 1 . Both training and testing was done using different subset of KDD Cup 99 2 data which is very popular and widely used intrusion attack dataset.
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