scholarly journals Function-Oriented Mobile Malware Analysis as First Aid

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jae-wook Jang ◽  
Huy Kang Kim
Keyword(s):  
Mobile Devices ◽  
First Aid ◽  
High Accuracy ◽  
Detection Methods ◽  
Sensitive Information ◽  
Malware Analysis ◽  
Quick Response ◽  
Malicious Behavior ◽  
Mobile Malware ◽  
Novel Method

Recently, highly well-crafted mobile malware has arisen as mobile devices manage highly valuable and sensitive information. Currently, it is impossible to detect and prevent all malware because the amount of new malware continues to increase exponentially; malware detection methods need to improve in order to respond quickly and effectively to malware. For the quick response, revealing the main purpose or functions of captured malware is important; however, only few recent works have attempted to find malware’s main purpose. Our approach is designed to help with efficient and effective incident responses or countermeasure development by analyzing the main functions of malicious behavior. In this paper, we propose a novel method for function-oriented malware analysis approach based on analysis of suspicious API call patterns. Instead of extracting API call patterns for malware in each family, we focus on extracting such patterns for certain malicious functionalities. Our proposed method dumps memory sections where an application is allocated and extracts suspicious API sequences from bytecode by comparing with predefined suspicious API lists. By matching API call patterns with our functionality database, our method determines whether they are malicious. The experiment results demonstrate that our method performs well in detecting malware with high accuracy.

scholarly journals Survey on Mobile Malware Analysis and Detection

2018 ◽  
Vol 7 (2.32) ◽  
pp. 279 ◽  
Author(s):  
K Swetha ◽  
K V.D.Kiran
Keyword(s):  
Mobile Phones ◽  
Malware Detection ◽  
Detection Methods ◽  
Classification Algorithms ◽  
Malware Analysis ◽  
Consistent System ◽  
Malware Classification ◽  
Mobile Malware

The amazing advances of mobile phones enable their wide utilize. Since mobiles are joined with pariah applications, bundles of security and insurance issues are incited. But, current mobile malware analysis and detection advances are as yet flawed, incapable, and incomprehensive. On account of particular qualities of mobiles such as constrained assets, user action and neighborhood correspondence ability, consistent system network, versatile malware detection faces new difficulties, particularly on remarkable runtime malware area. This paper provides overview on  malware classification, methodologies of assessment, analysis and on and off device detection methods on android. The work mainly focuses on different classification algorithms which are used as a part of dynamic malware detection on android.  

scholarly journals XSS MBot: Mobile Botnet for Stealthy DDoS Attacks

2019 ◽  
Vol 8 (9) ◽  
pp. 2832-2836
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Sensitive Information ◽  
Ddos Attacks ◽  
Mobile Botnet ◽  
Mobile Malware ◽  
Cross Site

Mobile devices are overgrowing; nowadays people are using mobile devices for different activities. Over the years malware attacks on mobile devices are increasing, the primary intention of the attacker is to steal sensitive information and turn the infected mobile device into a member of the botnet. We studied differences between traditional botnets and mobile botnets, also analyzed different mobile botnet attacks. Mobile malware applications spread through Cross-site Scripting vulnerabilities in trusted websites. Developed a mobile malware which can perform Denial-of-service attacks and used this malware to test and review mobile botnet attacks. We also studied solutions to prevent these mobile botnet attacks.

Techniques for Analysis of Mobile Malware

2019 ◽  
pp. 193-210
Author(s):  
Gopinath Palaniappan ◽  
Balaji Rajendran ◽  
S. Sangeetha ◽  
NeelaNarayanan V
Keyword(s):  
Mobile Devices ◽  
Mobile Application ◽  
Mobile Security ◽  
Malware Analysis ◽  
Rapid Rise ◽  
Mobile Malware ◽  
Attack Surface ◽  
Mobile Malware Detection ◽  
Fundamental Shift

The rapid rise in the number of mobile devices has resulted in an alarming increase in mobile software and applications. The mobile application markets/stores too have created a fundamental shift in the way mobile applications are delivered to users, with apps being added and updated in thousands every day. Even though research progresses have been achieved towards detection and mitigation of mobile security, open challenges still remain and also keep evolving in this area. Several studies reveal that mobile application markets/stores do harbor applications that are either vulnerable or malicious in nature, leading to compromises of millions of devices. This chapter (1) captures the attack surface of mobile devices, (2) lists the various mobile malware analysis techniques, and (3) lays the ground for research on mobile malware by providing mobile malware dataset resources, tools for malware analysis, patent landscaping for mobile malware detection, and a few open challenges in malware analysis.

scholarly journals Detection of Malware attacks in smart phones using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 4396-4400
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Research Work ◽  
Smart Phones ◽  
Smart Device ◽  
Sensitive Information ◽  
Security Vulnerabilities ◽  
Malicious Behavior ◽  
Iot Devices ◽  
New Applications

In recent years, security has become progressively vital in mobile devices. The biggest security problems in android devices are malware attack which has been exposed to different threats. The volume of new applications by the production of mobile devices and their related app-stores is too big to manually examine the each and every application for malicious behavior. Installing applications which may leads to security vulnerabilities on the smart phones request access to sensitive information. There are various malwares can attack android device namely virus, worms, Botnet, Trojans, Backdoor and Root kits due to these attacks the users is compromised by privacy. Root kits and viruses in mobile phone and IoT devices improve along with smart device versions are very difficult to detect or to the least costly. There are 3 places where the trace of these root kits / virus is visible namely CPU, Baseband and Memory. In the new approach we will use machine learning to detect “anomaly” usage pattern and a remote (master server) will analyze and verify the presence of such threats. This research work aims to develop a pipeline to investigate if any application present in a smart device is a malware or not. This pipeline uses HMM algorithm to read anomaly in application behavior, deep learning with Deep Belief Networks (DBN) to classify application events, and bootstrapping algorithm using random forest to categorize the application itself after malware or benign

scholarly journals On-Device Detection of Repackaged Android Malware via Traffic Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Gaofeng He ◽  
Bingfeng Xu ◽  
Lu Zhang ◽  
Haiting Zhu
Keyword(s):  
Mobile Devices ◽  
Detection Methods ◽  
Massive Data ◽  
Cloud Platform ◽  
Android Malware ◽  
Minimal Impact ◽  
Android Malware Detection ◽  
Resource Limited ◽  
Integrated Architecture ◽  
Novel Method

Malware has become a significant problem on the Android platform. To defend against Android malware, researchers have proposed several on-device detection methods. Typically, these on-device detection methods are composed of two steps: (i) extracting the apps’ behavior features from the mobile devices and (ii) sending the extracted features to remote servers (such as a cloud platform) for analysis. By monitoring the behaviors of the apps that are running on mobile devices, available methods can detect suspicious applications (simply, apps) accurately. However, mobile devices are typically resource limited. The feature extraction and massive data transmission might consume substantial power and CPU resources; thus, the performance of mobile devices will be degraded. To address this issue, we propose a novel method for detecting Android malware by clustering apps’ traffic at the edge computing nodes. First, a new integrated architecture of the cloud, edge, and mobile devices for Android malware detection is presented. Then, for repackaged Android malware, the network traffic content and statistics are extracted at the edge as detection features. Finally, in the cloud, similarities between apps are calculated, and the similarity values are automatically clustered to separate the original apps and the malware. The experimental results demonstrate that the proposed method can detect repackaged Android malware with high precision and with a minimal impact on the performance of mobile devices.

Techniques for Analysis of Mobile Malware

2021 ◽  
pp. 581-595
Author(s):  
Gopinath Palaniappan ◽  
Balaji Rajendran ◽  
S. Sangeetha ◽  
NeelaNarayanan V
Keyword(s):  
Mobile Devices ◽  
Mobile Application ◽  
Mobile Security ◽  
Malware Analysis ◽  
Rapid Rise ◽  
Mobile Malware ◽  
Attack Surface ◽  
Mobile Malware Detection ◽  
Fundamental Shift

The rapid rise in the number of mobile devices has resulted in an alarming increase in mobile software and applications. The mobile application markets/stores too have created a fundamental shift in the way mobile applications are delivered to users, with apps being added and updated in thousands every day. Even though research progresses have been achieved towards detection and mitigation of mobile security, open challenges still remain and also keep evolving in this area. Several studies reveal that mobile application markets/stores do harbor applications that are either vulnerable or malicious in nature, leading to compromises of millions of devices. This chapter (1) captures the attack surface of mobile devices, (2) lists the various mobile malware analysis techniques, and (3) lays the ground for research on mobile malware by providing mobile malware dataset resources, tools for malware analysis, patent landscaping for mobile malware detection, and a few open challenges in malware analysis.

scholarly journals ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes

2021 ◽  
Vol 17 (1) ◽  
pp. 1-23
Author(s):  
Mingliang Xu ◽  
Qingfeng Li ◽  
Jianwei Niu ◽  
Hao Su ◽  
Xiting Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Qr Code ◽  
Quick Response ◽  
Estimation Model ◽  
Qr Codes ◽  
Excellent Performance ◽  
Novel Method ◽  
Coarse To Fine

Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

2017 ◽  
Vol 88 (18) ◽  
pp. 2120-2131 ◽  
Author(s):  
Jue Hou ◽  
Bugao Xu ◽  
Hanchao Gao ◽  
RongWu Wang
Keyword(s):  
Curve Fitting ◽  
Fiber Orientation ◽  
Euclidean Distance ◽  
High Accuracy ◽  
Corner Detection ◽  
Bézier Curve ◽  
Corner Points ◽  
Novel Method ◽  
Web Images ◽  
Orientation Statistics

This paper describes a novel method for measuring fiber orientations in nonwoven web images by using Bézier fitting curves to detect corners of fiber edges and to separate crossing fiber edges. First, the Canny detector was adopted to extract fiber edges. Second, Bézier curve fitting was used to fit each fiber edge for calculating the curvature of every point on the edge. Third, corner points were detected by locating points where the curvatures were minimal on various edges and below the threshold to divide edges into segments for orientation calculations. Last, a formula calculating the fiber orientation statistics based on the Euclidean distance was established. The experiment results demonstrated that the proposed method is robust for analyzing different nonwoven web images, and has a high accuracy for corner detection and fiber orientation calculation.

scholarly journals Dynamic detection of mobile malware using smartphone data and machine learning

2021 ◽  
Author(s):  
Sebastian Panman de Wit ◽  
Doina Bucur ◽  
Jeroen van der Ham
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Real Life ◽  
Detection Methods ◽  
Nearest Neighbour ◽  
Dynamic Features ◽  
Machine Learning Classifiers ◽  
Cpu Usage ◽  
Mobile Malware ◽  
Mobile Malware Detection

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. Detection methods for mobile malware exist but are still limited. In this paper, we propose dynamic malware-detection methods that use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System (OS). We use a real-life sensor dataset containing device and malware data from 47 users for a year (2016) to create multiple mobile malware detection methods. We examine which features, i.e. aspects, of a device, are most important to monitor to detect (subtypes of) Mobile Trojans. The focus of this paper is on dynamic hardware features. Using these dynamic features we apply the following machine learning classifiers: Random Forest, K-Nearest Neighbour, and AdaBoost.

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