The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It is observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.

Contemporary technologies have ensured the availability of high‐quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever‐evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty‐seven research trends. The study also suggests the potential future research directions.

Opinion these days is considered vital to attract probable customers; hence e-commerce websites are facilitating the existing customers to share their experiences about the products they have used. The dawn of e-commerce business has resulted into some firms, which post reviews that are carefully designed to influence the opinion of prospective customers. Review sites provide an open platform for everyone to share their experiences but authenticity of the reviews is not assured thereby affecting customers' buying decisions. Reviews which are not genuine are generally termed as spam. For many years, email spam and web spam were the two socially discussed opinion issues. Nowadays, due to popularity of online shopping and customer's dependence on the online reviews, it became a major target for review spammers to mislead customers by writing untruthful reviews for target products. Opinion spammers may write reprehensible positive opinions in order to promote some target objects or by giving beguiling negative opinions to some other objects in order to damage their reputation. Writing fake review is an unethical activity, also known as shilling. To the best of our knowledge, not much study is reported to evaluate trustworthiness of online reviews. In the past few years, variety of techniques has been recommended by researchers to accord with this trouble. This paper intends to introduce parameterized approach for identifying suspicions, review spammers and their group considering geographical statistics and networking parameters.

Today, Online audits give profitable data about items and administrations to buyers. On the other hand, spammers are joining the group attempting to deceive pursuers by composing fake surveys. Past endeavors for spammer identification utilized reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies have the capacity recognize certain sorts of spammers, e.g., The individuals who post numerous comparable surveys about one target substance. Then again, in actuality, there are different sorts of spammers who can control their practices to act much the same as a genuine commentators, and in this way can't be identified by the accessible systems. From various years, email spam and web spam were the two essential highlighted social issues. In the meantime nowadays, in light of reputation of customers' interest to web shopping and their dependence on the online reviews, it transformed into a true center for review spammers to betray customers by making sham studies for target things. To the best of our understanding, not much study is represented concerning this issue of online surveys. Regardless paper was disseminated in 2007 by NITIN JINDAL & BING LIU with respect to review spam detection. In the recent years, various techniques have been prescribed via specialists to accord with this problem. To the best of our insight, identification of reviews spam using networking parameters and geographical statistics has not yet been reported. This paper expects to present parameterized methodology for distinguishing suspicions by making use of metadata about reviews. Metadata about reviews is private data like MAC address IP Address, location, date-time and browser id.