Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

The Predicting and Prevention of Malware from Cyber Hacking Breaches in Online Social Network

Analyzing cyber incident information units is an essential approach for deepening our information of the evolution of the risk situation. This is a notably new studies topic, and plenty of research continue to be to be done. In this paper, we record a statistical evaluation of a breach incident information set similar to 12 years (2005–2017) of cyber hacking sports that encompass malware attacks. We display that, in evaluation to the findings suggested withinside the literature, each hacking breach incident inter-arrival instances and breach sizes need to be modeled through stochastic processes, instead of through distributions due to the fact they show off autocorrelations. Then, we recommend specific stochastic method fashions to, respectively, match the inter-arrival instances and the breach sizes. In this paper we be aware that, through reading their actions, we are able to classify malware right into a small quantity of Behavioral classes, every of which plays a restrained set of misbehaviors that signify them. These misbehaviors may be described through tracking capabilities belonging to exclusive platforms. In this paper we gift a singular host-primarily based totally malware detection machine in OSN which concurrently analyzes and correlates capabilities at 4 levels: kernel, application, person and package, to come across and prevent malicious behaviors. It has been designed to do not forget the ones behaviors traits of virtually each actual malware which may be observed withinside the wild. This prototype detects and efficaciously blocks greater than 96% of malicious apps, which come from 3 massive datasets with approximately 2,800 apps, through exploiting the cooperation of parallel classifiers and a behavioral signature-primarily based totally detector. Keywords: Cyber security, Malware, Emerging technology trends, Emerging cyber threats, Cyber attacks and countermeasures

Significant Permission Identification for Machine Learning Based Android Malware Detection

The dreadful rate of growth of malicious apps has become a significant issue that sets back the prosperous mobile scheme. A recent report indicates that a brand new malicious app for golem is introduced each ten seconds. To combat this serious malware campaign, we'd like a scalable malware detection approach that may effectively and expeditiously determine malware apps. varied malware detection tools are developed, together with system-level and network-level approaches. However, scaling the detection for an outsized bundle of apps remains a difficult task. during this paper, we tend to introduce SIGPID, a malware detection system supported permission usage analysis to address the speedy increase within the range of golem malware. rather than extracting and analyzing all golem permissions, we tend to develop 3-levels of pruning by mining the permission information to spot the foremost important permissions that may be effective in identifying between benign and malicious apps. SIGPID then utilizes machine-learning based mostly classification ways to classify totally different families of malware and benign apps. Our analysis finds that solely twenty two permissions square measure important. we tend to then compare the performance of our approach, victimisation solely twenty two permissions, against a baseline approach that analyzes all permissions. The results indicate that once Support Vector Machine (SVM) is employed because the classifier, we are able to bring home the bacon over ninetieth of preciseness, recall, accuracy, and F-measure, that square measure concerning constant as those created by the baseline approach whereas acquisition the analysis times that square measure four to thirty two times but those of victimisation all permissions. Compared against alternative progressive approaches, SIGPID is more practical by sleuthing ninety three.62% of malware within the information set, and 91.4% unknown/new malware samples. Keywords: SIGPID (Significant Permission Identification), SVM(Support Vector Machine), Android, Malware, Benign, Data pruning

Less is More: A privacy-respecting Android malware classifier using federated learning

In this paper we present LiM (‘Less is More’), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users’ devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in > 100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with Ma-MaDroid’s dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.

Blockchain Based Detection of Android Malware using Ranked Permissions

Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

SFDroid: Android Malware Detection using Ranked Static Features

Over the past few years, malware attacks have risen in huge numbers on the Android platform. Significant threats are posed by these attacks which may cause financial loss, information leakage, and damage to the system. Around 25 million smartphones were infected with malware within the first half of 2019 that depicts the seriousness of these attacks. Taking into account the danger posed by the Android malware to the users' community, we aim to develop a static Android malware detector named SFDroid that analyzes manifest file components for malware detection. In this work, first, the proposed model ranks the manifest features according to their frequency in normal and malicious apps. This helps us to identify the significant features present in normal and malware datasets. Additionally, we apply support thresholds to remove the unnecessary and redundant features from the rankings. Further, we propose a novel algorithm that uses the ranked features, and several machine learning classifiers to detect Android malware. The experimental results demonstrate that by using the Random Forest classifier at 10% support threshold, the proposed model gives a detection accuracy of 95.90% with 36 manifest components.

R-MFDroid: Android Malware Detection using Ranked Manifest File Components

With the increasing fame of Android OS over the past few years, the quantity of malware assaults on Android has additionally expanded. In the year 2018, around 28 million malicious applications were found on the Android platform and these malicious apps were capable of causing huge financial losses and information leakage. Such threats, caused due to these malicious apps, call for a proper detection system for Android malware. There exist some research works that aim to study static manifest components for malware detection. However, to the best of our knowledge, none of the previous research works have aimed to find the best set amongst different manifest file components for malware detection. In this work, we focus on identifying the best feature set from manifest file components (Permissions, Intents, Hardware Components, Activities, Services, Broadcast Receivers, and Content Providers) that could give better detection accuracy. We apply Information Gain to rank the manifest file components intending to find the best set of components that can better classify between malware applications and benign applications. We put forward a novel algorithm to find the best feature set by using various machine learning classifiers like SVM, XGBoost, and Random Forest along with deep learning techniques like classification using Neural networks. The experimental results highlight that the best set obtained from the proposed algorithm consisted of 25 features, i.e., 5 Permissions, 2 Intents, 9 Activities, 3 Content Providers, 4 Hardware Components, 1 Service, and 1 Broadcast Receiver. The SVM classifier gave the highest classification accuracy of 96.93% and an F1-Score of 0.97 with this best set of 25 features.

Game Theory based Cyber-Insurance to Cover Potential Loss from Mobile Malware Exploitation

Potential for huge loss from malicious exploitation of software calls for development of principles of cyber-insurance. Estimating what to insure and for how much and what might be the premiums poses challenges because of the uncertainties, such as the timings of emergence and lethality of malicious apps, human propensity to favor ease by giving more privilege to downloaded apps over inconvenience of delay or functionality, the chance of infection determined by the lifestyle of the mobile device user, and the monetary value of the compromise of software, and so on. We provide a theoretical framework for cyber-insurance backed by game-theoretic formulation to calculate monetary value of risk and the insurance premiums associated with software compromise. By establishing the conditions for Nash equilibrium between strategies of an adversary and software we derive probabilities for risk, potential loss, gain to adversary from app categories, such as lifestyles, entertainment, education, and so on, and their prevalence ratios. Using simulations over a range of possibilities, and using publicly available malware statistics, we provide insights about the strategies that can be taken by the software and the adversary. We show the application of our framework on the most recent mobile malware data (2018 ISTR report—data for the year 2017) that consists of the top five Android malware apps: Malapp, Fakeinst, Premiumtext, Maldownloader , and Simplelocker and the resulting leaked phone number, location information, and installed app information. Uniqueness of our work stems from developing mathematical framework and providing insights of estimating cyber-insurance parameters through game-theoretic choice of strategies and by showing its efficacy on a recent real malicious app data . These insights will be of tremendous help to researchers and practitioners in the security community.

A Review and Case Study on Android Malware: Threat Model, Attacks, Techniques and Tools

As android devices have increased in number in the past few years, the android operating system has started dominating the smartphone market. The vast spread of android across all the devices has made security an important issue as the android users continue to grow exponentially. The security of android platform has become the need of the hour in view of increase in the number of malicious apps and thus several studies have emerged to present the detection approaches. In this paper, we review the android components to propose a threat model that illustrates the possible threats that are present in the android. We also present the attack taxonomy to illustrate the possible attacks at various layers of the android architecture. Experiments demonstrating the feature extraction and classification using machine earning algorithms have also been performed.