Cloud computing is a framework for delivering services through a shared network. It should follow certain procedures or mechanisms to protect and make the cloud data more reliable. In secure cloud... Show More

Cloud computing is a framework for delivering services through a shared network. It should follow certain procedures or mechanisms to protect and make the cloud data more reliable. In secure cloud computing, the implementation and service delivery model are dealt with. Cloud security could be viewed as a provider and customer responsibility depending on their design and level. There are cases where multiple cloud users can use the same information to create service abnormalities, which is called ‘multitenancy.' In a multi-tenant model, the resources are isolated between different users and it is done by segmentation to achieve the goal of data security between many users. In the user access area, user access to common resources is also limited. The software is aware of the actions of the client and acts accordingly if unauthorized access is detected. The authors propose a clear idea of the data storage security of cloud information through honeypot in cloud monitoring applications and various cloud security scheme key models used and also identifies the audit time and invalid response time.

The incidence of skin cancer has been increasing in recent years and it can become dangerous if not detected early. Computer-aided diagnosis systems can help the dermatologists in assisting with... Show More

The incidence of skin cancer has been increasing in recent years and it can become dangerous if not detected early. Computer-aided diagnosis systems can help the dermatologists in assisting with skin cancer detection by examining the features more critically. In this article, a detailed review of pre-processing and segmentation methods is done on skin lesion images by investigating existing and prevalent segmentation methods for the diagnosis of skin cancer. The pre-processing stage is divided into two phases, in the first phase, a median filter is used to remove the artifact; and in the second phase, an improved K-means clustering with outlier removal (KMOR) algorithm is suggested. The proposed method was tested in a publicly available Danderm database. The improved cluster-based algorithm gives an accuracy of 92.8% with a sensitivity of 93% and specificity of 90% with an AUC value of 0.90435. From the experimental results, it is evident that the clustering algorithm has performed well in detecting the border of the lesion and is suitable for pre-processing dermoscopic images.

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. Existing studies have a lack of real data for analysis and focus on a very... Show More

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. Existing studies have a lack of real data for analysis and focus on a very partial version of the problem by covering only a specific actor, healthcare service, or disease. In this article, the proposed strategy identifies fraudulent behaviors in Medicare claims data using several predictors as model inputs. The methodology involves preprocessing and model development phases. At the initial phase, the feature mining is done by estimating their feature importance score. The labeling of instances by using the classification rules to the whole dataset. Thus, a transformed dataset is obtained by the model. In the development phase, the RF with SMOTE is applied against the training and testing data. Specifically, SMOTE adapted to balance data and sorts misclassified instances and finds the interesting instances. The results of the proposed model improvises the classifier performance RF with SMOTE when contrast with RF method.

The rapid development of various services that are provided by information technology has been widely accepted by the users who are making use of such services in their day-to-day life activities.... Show More

The rapid development of various services that are provided by information technology has been widely accepted by the users who are making use of such services in their day-to-day life activities. Securing such a system application from various intrusions still remains to be a one of the major issues in the current era. Detecting such anomalies from the regular events involves various steps such as data pre-processing, feature selection, and classification. Many of the computational models intend to accurately discriminate the samples of each group for better classification by identifying candidate features prior to the learning phase. This research studies the implementation of a combined feature selection technique such as the GRRF-FWSVM method which is applied to the benchmarked anomaly detection dataset KDD CUP 99. The results prove the novel proposed hybrid model is an effective method in identifying anomalies and it increases the detection rate of about 98.55% of the intrusion detection system with the two most common benchmark models.

Power consumption plays a crucial role in the design of portable wireless communication devices, as it has a direct influence on the battery weight and volume required for operation. This article... Show More

Power consumption plays a crucial role in the design of portable wireless communication devices, as it has a direct influence on the battery weight and volume required for operation. This article presents a novel design for a linear LMS equalizer for the optimization of filter order. The article describes the use of a variable length algorithm for dynamically updating the tap-length of the LMS adaptive filter to optimize the performance and for reducing the power in the adaptive filter core. An algorithm is applied to reduce and adjust the order of the filter in linear equalizer according to the channel conditions. The proposed design is implemented in the synopsis TSMC 65nm technology. The results from using the algorithm uses 28% less power when compared with the conventional 64-tap fixed length adaptive filter design. It has also been shown that the low-complexity of the additional circuitry needed for the variable length adaptive filter presents minimal overhead for this architecture.

In this article, an EFS-LSTM, a deep recurrent learning model, is proposed for network intrusion detection systems. The EFS-LSTM model uses ensemble-based feature selection (EFS) and LSTM (Long... Show More

In this article, an EFS-LSTM, a deep recurrent learning model, is proposed for network intrusion detection systems. The EFS-LSTM model uses ensemble-based feature selection (EFS) and LSTM (Long Short Term Memory) for the classification of network intrusions. The EFS combines five feature selection mechanisms namely, information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the benchmark NSL-KDD dataset and implemented using Tensor flow and python. The EFS-LSTM classifier is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection techniques and classified using LSTM. The performance study showed that the EFS-LSTM model outperforms better with 99.8% accuracy with a higher detection and less false alarm rates.