The current world scenario has given us a life perspective which requires much contribution from both the health sector as well as the technology. Wireless Body Area Network is an extension of Wireless Sensor Network that combines healthcare and technology in lieu of collecting and communicating health data minimizing human error in medical diagnosis. Wireless body area network require number of minute sensor nodes to collect physiological data from the human body and transmit it to the health server. Nowadays, real time monitoring of health data is prominent by virtue of wireless body area networks that are capable of continuously monitoring physiological parameters of the people so as to provide them with real time feedback from the medical fraternity. This helps in behaviour analysis, elderly care, detection of activities of daily life and abnormalities, unforeseen situations and symptoms, therefore, helping in improving quality of life. The sensing devices continuously sense and transmit data to the eHealth server for the further analysis of data using a machine learning technique. During this process, the sensor nodes consume most of the energy during communication rather than sensing. Therefore, various clustering and aggregation techniques have been proposed by the the researchers for efficient data transmission from the sensors to the sink node. The proposed data aggregation technique in this chapter, dynamically selects the cluster heads that transmits data from the source to the aggregator node. Secondly, performs aggregation on the basis of variance and priority of the data. The proposed technique outperforms other state-of-art techniques as values are significantly reduced for energy consumption, average delay, packet delivery ratio and network lifetime as shown by the analysis of results. 30% of reduction in packet transmissions and up to 65% of energy saving is achieved to enhance wireless body area networks.

Ransomware attacks are not only limited to Personal Computers but are increasing rapidly to target smart-phones as well. The attackers target smart-phone devices to steal users' personal information for monetary purposes. However, Android is the most widely used mobile operating system with the largest market share in the world that makes it a primary target for cyber-criminals to attack. The existing research towards the detection of Android ransomware lacks significant features and works with supervised machine learning techniques. But there are several restrictions in supervised machine learning techniques such as these techniques heavily rely on anti-virus vendors to provide explicit labels and the given sample can be wrongly classified if the training set does not include related examples and/or if the labels are incorrect. Moreover, it may not detect unknown ransomware samples in real-time situations due to the absence of historical targets in the real world. In this work, an attempt is made for an in-depth investigation of Android ransomware with reverse engineering and forensic analysis to extract static features. Furthermore, a novel RansomDroid framework on clustering based unsupervised machine learning techniques is proposed to address the issues such as mislabeling of historical targets and detecting unforeseen Android ransomware. To the best of our knowledge, performing unsupervised machine learning techniques for the detection of Android ransomware is still an open area of research that has not been explored by the researchers yet. The proposed RansomDroid framework employs a Gaussian Mixture Model that has a flexible and probabilistic approach to model the dataset. RansomDroid framework utilizes feature selection and dimensionality reduction to further improve the performance of the model. The experimental results show that the proposed RansomDroid framework detects Android ransomware with an accuracy of 98.08% in 44 ms. (c) 2021 Elsevier Ltd. All rights reserved.

Smart‐phones have become a necessity for users due to their abundance of services such as global positioning system, Wi‐Fi, voice/video calls, SMS, camera, and so forth. It contains personal information of users including photos, documents, messages, and videos. Android‐based smart‐phones enriched with many applications (commonly known as apps) fascinates users to use this ubiquitous technology up to a full extent. With open architecture and 73% of market share, Android is the most popular mobile operating system (OS) among developers. At the same time, the increasing popularity of Android OS woos attackers or cyber‐criminals to exploit its vulnerabilities. The attackers write malicious code to harm the device and grab users' sensitive information. For example, ransomware (a form of malware) demands ransom from victims to liberate the ceased material for illegal financial gain. The existing survey papers cover the analysis and detection of generic Android malware. The focus of this survey paper is to present an in‐depth threat scenario of Android ransomware. This article not only provides a comprehensive survey on analysis and detection methods for Android ransomware since its beginning (2015) till date (2020); but also presents observations and suggestions for researchers and practitioners to carry out further research.

With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased. The Android-based smartphones occupy the largest market share among other mobile operating systems. The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes. Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks. Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model. After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively

The mobile device has become an essential utility tool for more effective computation, storage, and power, making it suitable for mobile cloud computing. The cloudlet is used as a connectivity establishment link between the mobile device and the cloud. The objective of this paper is to focus on mobile cloud computing facilitated with cloudlet-based computation. The latter possesses inter-cloudlet communication, which had been proposed within the mobile cloudletbased computing environment framework. The same had been further enhanced to scalable critical parameter yield of resources framework. Nevertheless, this was not taken to the criteria, which would impact the yield factor, in terms of availability. The present research endeavor aims to improve the algorithm by considering some more criterion and provides a new mobile cloud computing framework for data execution as a service using cloudlet. The outcome shows a positive result in the cloud- cloudlet based computation.

SummaryNowadays, the technological advancements of low power electronic and sensing devices, wearable systems, communication technologies, and cloud computing have encouraged the provision of ubiquitous health monitoring, medical diagnosis, and treatment consultations. The data collected from the patients are transmitted to mHealth server for real‐time and self‐reliant activity detection, behavior analysis, ambient assisted living, elderly care, activity of daily living, rehabilitations, entertainments, and surveillance in smart home environments. This helps in building sensor analyst systems that analyze the data on mHealth server for disseminating it to the respective end users. This is of utmost importance as it can provide real time feedback to patients, family members, caregivers, and so forth about the behavioral changes of elderly people and people with special needs. In this article, we have proposed knowledge based data dissemination system using machine learning techniques that analyses the data collected on mHealth server in three steps. First, the data are analyzed using support vector machine, k‐nearest neighbor, neural network, and logistic regression. Second, it finds hidden patterns from the data using hidden Markov model (HMM). Third, it defines fuzzy rules to disseminate the data to the end users. The system has been compared against conventional approaches. The uniqueness of the system lies in the fact that it uses temporal nature of data and provides with real‐time feedback as well as predicts outcomes and detects hidden patterns. The results have shown that the techniques NN, HMM, and fuzzy logic when used in conjunction with each other for disease prediction, hidden pattern detection, and data dissemination gives an accuracy of 98%. Thus, increasing effectiveness of sensor analyst system.

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features' similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software's capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets.

Ransomware is not a Personal Computer (PC) problem anymore, but nowadays smartphones are also vulnerable to it. Various types of ransomware such as Android/Simplocker and Android/ Lockerpin attack Android OS to steal users’ personal information. In this paper, we present the evolution of Android ransomware and coin a term—RansomAnalysis—to perform the investigation of samples to analyze the AndroidManifest.xml file for the extraction of permissions. We perform a comparison between permissions gathered by ransomware and benign apps. Besides this, we analyze the topmost permissions used by Android ransomware.

Cyber-criminals perform ransomware attacks to make money from victims by harming their devices. The attacks are rapidly increasing on Android-based smartphones due to its vast usage world-wide. In this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) employ machine learning models to classify ransomware and benign apps, and (3) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results show that the proposed framework detects Android ransomware by achieving an accuracy of 99.59% with Logistic Regression in 177 milliseconds and 235 milliseconds on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) respectively.

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

According to the World Health Organization, most of the world population is affected by chronic diseases, obesity, cardiovascular diseases and diabetes while another dominant problem is of aging population. Thus, it is desirable to have cost effective solutions for health monitoring, especially for countries that have minimum conventionally trained healthcare staff and infrastructure. Healthcare has shifted from hospital dominant services to patient dominant services which has thrived WBANs to provide ubiquitous health monitoring by virtue of wearable or implantable sensor nodes that commonly monitor biological signals. As the society becomes more health conscious, WBANs have the potential to revolutionize the way people integrate their health and information technology. Hence, WBANs are desired to strengthen conventional healthcare systems. Notwithstanding the current achievements, technological advances, proposed solutions and commercialized products; WBANs still experience many obstacles in their foolproof adoption. This paper surveys the plethora of WBAN applications and network architecture in detail used for data collection, data transmission and data analysis that form sensor analyst system in the realm of Internet of Things. Wireless communicational technologies are also discussed in this paper. Also, we have categorized the routing protocols and have provided with their critical qualitative analysis. Towards the end we discuss several projects in the field of WBANs and some open research areas. These findings on how the sensor nodes, newest routing protocols and data analysis techniques influence ubiquitous health monitoring sets this survey apart from the already existing surveys on WBANs.

According to clinical trials, the treatment of patients with chronic hepatitis C (CHC) with second-generation direct acting antiviral agents (DAAs) is highly efficient and well tolerated. The goal of this study was to investigate the effectiveness and safety of various combinations of these drugs during their first 2 years of use in the real-world practice of French general hospitals.Data from patients treated with all-oral DAAs in 24 French non-academic hospital centers from March 1, 2014 to January 1, 2016, were prospectively recorded. The sustained virological response 12–24 weeks after treatment (SVR 12-24) was estimated and severe adverse events (SAE) were evaluated and their predictive factors were determined using logistic regression.Data from 1123 patients were analyzed. The population was 69% genotype (G) 1, 13% G3, 11.5% G4, 5% G2, 49% with cirrhosis and 55% treatment-experienced. The treatment regimens were sofosbuvir/ledipasvir (38%), sofosbuvir/daclatasvir (32%), sofosbuvir/simeprevir (17%), ombitasvir + paritaprevir + ritonavir (5%) (with dasabuvir 3.5%), and sofosbuvir/ribavirin (8%). Ribavirin was given to 24% of patients. The SVR 12-24 was 91.0% (95% CI: 89.2–92.5%). Sofosbuvir-ribavirin was less effective than other regimens. The independent predictors of SVR 12-24 by logistic regression were body weight, albumin, previous hepatocellular carcinoma and treatment regimen (sofosbuvir/ribavirin vs. others). Sixty-four severe adverse events (SAE) were observed in 59 [5.6%] patients, and were independently predicted by cirrhosis and baseline hemoglobin. Serum creatinine increased during treatment (mean 8.5%, [P < 10−5]), satisfying criteria for acute kidney injury in 62 patients (7.3%). Patient-reported overall tolerance was excellent, and patient-reported fatigue decreased during and after treatment.Second generation DAAs combinations are as effective and well tolerated in a « real-world » population as in clinical trials. Further studies are needed on renal tolerance.Les essais thérapeutiques des antiviraux d’action directe (AAD) de seconde génération ont montré leur efficacité et leur bonne tolérance. Nous avons voulu évaluer l’efficacité et la tolérance de diverses combinaisons de ces médicaments pendant leurs deux premières années d’utilisation dans la pratique quotidienne d’hôpitaux généraux français.Les données des malades traités avec des DAA sans interféron dans 24 centres hospitaliers non universitaires entre le 1er mars 2014 et le 1er janvier 2016 ont été enregistrées prospectivement. Nous avons mesuré la réponse virologique soutenue 12 à 24 semaines après la fin du traitement (RVS 12-24), analysé les évènements indésirables sévères (EIS), et déterminé leurs facteurs prédictifs par régression logistique.Les données de 1123 malades ont été analysées. Il y avait 69 % de génotype (G) 1, 13 % de G3, 11,5 % de G4, 5 % de G2, 49 % des malades avaient une cirrhose, 55 % avaient déjà été traités. Les principales combinaisons thérapeutiques étaient sofosbuvir/ledipasvir (38 %), sofosbuvir/daclatasvir (32 %), sofosbuvir/simeprevir (17 %), ombitasvir + paritaprevir + ritonavir (5 %) (avec dasabuvir 3,5 %), et sofosbuvir/ribavirine (8 %); 24 % des maladies prirent de la ribavirine. Le taux de RVS 12-24 était de 91,0 % (IC 95 %: 89,2–92,5 %). Sofosbuvir-ribavirine était moins efficace que les autres combinaisons. Les facteurs prédictifs indépendants de RVS 12-24 étaient le poids, l’albumine, un carcinome hépatocellulaire préalable et la combinaison thérapeutique (sofosbuvir/ribavirin vs autres). Soixante-quatre EIS furent observés chez 59 malades (5,6 %), et étaient indépendamment prédits par l’existence d’une cirrhose et l’hémoglobine initiale. La créatininémie augmenta pendant le traitement (8,5 % en moyenne [p < 10−5]), remplissant les critères d’une insuffisance rénale aiguë chez 62 patients (7,3 %). Les malades évaluèrent la tolérance comme excellente, et leur fatigue qui décrut pendant et après le traitement.Dans une population «de la vraie vie», les combinaisons d’AAD de seconde génération sont aussi remarquablement efficaces et bien tolérées que dans les essais thérapeutiques. Des études ultérieures concernant la tolérance rénale sont nécessaires.

The recent trends show that electronics companies are trying to add as much as features in their products to show the uniqueness. As a number of peripheral devices attached to an electronic device consume more power. So when the devices are in idle state the system automatically suspend the peripheral devices to the suspended state to save power. The system is put in to the suspended state instead of powered down and is resumed from the last suspended state instead of complete power boot up sequence. An operating system handles system suspends and resume transition during STR (Suspend to Ram) operation using power management module. This module is part of core feature of an operating system and manages all devices suspend and resume states. The devices resumed back in serial fashion depending upon system bus architecture which causes preferred devices later wakeup. According to the proposed idea of our research preferred devices are selected based on the requirement and assign higher priorities among devices for enabling depending upon user profile setting. Higher priority devices are resumed prior to low priority devices. This is don e by analyzing the timings of devices for enabling device for first time used, recently used and frequently used in user profile. The device usage logs are captured and parsed for getting devices usage timing.

The paper presents the Performance Analysis of Manhattan Street Network (MSN). Improvement of performance at the destination node by applying different buffering capacities at the routers is demonstrated. The results using NS2 simulator are produced. It can be concluded that the congestion and the packet drops can be reduced at the link node by appropriate size of buffer at the link node.