In today's interconnected era, intrusion detection system (IDS) has the potential to be the frontier of defense against cyberattacks and plays an essential role in achieving security of networking resources and infrastructures. The performance of IDS depends highly on data features. Selecting the most informative features eliminating the redundant and irrelevant features from network traffic data for IDS is still an open research issue. The key impetus of this paper is to identify and benchmark the potential set of features that can characterize network traffic for intrusion detection. In this correspondence, an ensemble approach is proposed. As a first step, the approach applies four different feature evaluation measures, such as correlation, consistency, information, and distance, to select the more crucial features for intrusion detection. Second, it applies the subset combination strategy to merge the output of the four measures and achieve the potential feature set. Along with this, a new framework that adopts the data analytic lifecycle practices is explored to employ the proposed ensemble for building an effective IDS. The effectiveness of the proposed approach is demonstrated by conducting several experiments on four intrusion detection evaluation datasets, namely KDDCup'99, NSL-KDD, UNSW-NB15, and CICIDS2017. The obtained results prove that the proposed approach contributes more potential features compared to the state-of-the-art approaches, leading to achieve a promising performance gain in the detection rate of 3.2%, the false alarm rate of 38%, and the detection time of 12%. Furthermore, ROC and statistical significance are analyzed for the identified feature subset to strongly conform its acceptability as a future benchmark for building an effective IDS.
The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.
COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.
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