Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).&nbsp

Identification and Classification of Pulmonary Nodule in Lung Modality Using Digital Computer

This paper proposes an intelligent approach for the development of a new support system to improve the performance of Computer Aided Diagnosis for automated pulmonary nodule identification on Computed Tomography images which is Digital Imaging and Communications in Medicine format. The first step in diagnosis of any abnormality in lung region, is to acquire a Computer Tomography image, a non-invasive procedure. The digital format of the image is highly portable, hence the extraction and sharing of valuable knowledge. The large number of related images pose a challenge in coherence and consequently arriving at conclusion. The CAD system has been designed and developed to segment the lung tumour region and extract the features which is of region of interest. The Detection process consists of two steps, namely Lung segmentation and Feature extraction. In segmentation of lung region K-means, Watershed and Histogram based algorithms is implemented. The extracted features and the label of the corresponding ROI were used to train a neural network . Finally , these properties are used to classify lung tumour as benign or malignant. The main objective of this method is to reduce false positive rate and to improve the access time and reduce inter-observer variability