Data Mining Practical Machine Learning Tools and Techniques

Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

A survey on security challenges in cloud computing: issues, threats, and solutions

Mobile users are increasingly becoming targets of malware infections and scams. Some platforms, such as Android, are more open than others and are therefore easier to exploit than other platforms. In order to curb such attacks it is important to know how these attacks originate. We take a previously unexplored step in this direction and look for the answer at the interface between mobile apps and the Web. Numerous inapp advertisements work at this interface: when the user taps on an advertisement, she is led to a web page which may further redirect until the user reaches the final destination. Similarly, applications also embed web links that again lead to the outside Web. Even though the original application may not be malicious, the Web destinations that the user visits could play an important role in propagating attacks. In order to study such attacks we develop a systematic methodology consisting of three components related to triggering web links and advertisements, detecting malware and scam campaigns, and determining the provenance of such campaigns reaching the user. We have realized this methodology through various techniques and contributions and have developed a robust, integrated system capable of running continuously without human intervention. We deployed this system for a two-month period and analyzed over 600,000 applications in the United States and in China while triggering a total of about 1.5 million links in applications to the Web. We gain a general understanding of attacks through the app-web interface as well as make several interesting findings, including a rogue antivirus scam, free iPad and iPhone scams, and advertisements propagating SMS trojans disguised as fake movie players. In broader terms, our system enables locating attacks and identifying the parties (such as specific ad networks, websites, and applications) that intentionally or unintentionally let them reach the end users and, thus, increasing accountability from these parties.

Detecting Hidden Attacks through the Mobile App-Web Interfaces

Assigning family labels to malicious apps is a common practice for grouping together malware with identical behavior. However, recent studies show that apps labeled as belonging to the same family do not necessarily behave similarly: one app may lack or have extra capabilities compared to others in the same family, and, conversely, two apps labeled as belonging to different families may exhibit close behavior. To reveal these inconsistencies, this paper presents AndrEnsemble, a characterization system for Android malware families based on ensembles of sensitive API calls extracted from aggregated call graphs of different families. Our method has several advantages over similar characterization approaches, including a greater reduction ratio with respect to original call graphs, robustness against transformation attacks, and flexibility to be applied at different granularity levels. We experimentally validate our approach and discuss three specific use cases: mobile ransomware, SMS Trojans and banking Trojans. This left us with some interesting findings. First of all, malicious operations in these types of malware are not necessarily exercised by using several sensitive API calls all together. Second, SMS Trojans have larger ensembles of API calls compared to the other types. Last but not least, we identified several samples with identical ensembles though being labeled as part of different families.

AndrEnsemble: Leveraging API Ensembles to Characterize Android Malware Families

Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine Learning (ML)/Deep Learning (DL). Meanwhile, critical reviews have emerged that suggest that ML/DL should be used cautiously. To improve the applicability and generalizability of ML/DL-related SE studies, we conducted a 12-year Systematic Literature Review (SLR) on 1,428 ML/DL-related SE papers published between 2009 and 2020. Our trend analysis demonstrated the impacts that ML/DL brought to SE. We examined the complexity of applying ML/DL solutions to SE problems and how such complexity led to issues concerning the reproducibility and replicability of ML/DL studies in SE. Specifically, we investigated how ML and DL differ in data preprocessing, model training, and evaluation when applied to SE tasks, and what details need to be provided to ensure that a study can be reproduced or replicated. By categorizing the rationales behind the selection of ML/DL techniques into five themes, we analyzed how model performance, robustness, interpretability, complexity, and data simplicity affected the choices of ML/DL models. 

Machine/Deep Learning for Software Engineering: A Systematic Literature Review

As Android platform evolves in a fast pace, API-related compatibility issues become a significant challenge for developers. To handle an incompatible API invocation, developers mainly have two choices: merely performing sufficient checks to avoid invoking incompatible APIs on platforms that do not support them, or gracefully providing replacement implementations on those incompatible platforms. As providing more consistent app behaviors, the latter one is more recommended and more challenging to adopt. However, it is still unknown how these issues are handled in the real world, do developers meet difficulties and what can we do to help them. In light of this, this paper performs the first large-scale study on the current practice of handling evolution-induced API compatibility issues in about 300,000 Android market apps, and more importantly, their solutions (if exist). Actually, it is in general very challenging to determine if developers have put in counter-measure for a compatibility issue, as different APIs have diverse behaviors, rendering various repair. To facilitate a large-scale study, this paper proposes RAPID, an automated tool to determine whether a compatibility issue has been addressed or not, by incorporating both static analysis and machine learning techniques. Results show that our trained classifier is quite effective by achieving a F1-score of 95.21% and 91.96% in the training stage and the validation stage respectively. With the help of RAPID, our study yields many interesting findings, e.g. developers are not willing to provide alternative implementations when handling incompatible API invocations (only 38.4%); for those incompatible APIs that Google gives replacement recommendations, the ratio of providing alternative implementations is significantly higher than those without recommendations; developers find more ways to repair compatibility issues than Google's recommendations and the knowledge acquired from these experienced developers would be extremely useful to novice developers and may significantly improve the current status of compatibility issue handling.

How Android developers handle evolution-induced API compatibility issues: a large-scale study

Mobile apps have become the main channel for accessing Web services. Both Android and iOS feature inapp Web browsers that support convenient Web service integration through a set of Web resource manipulation APIs. Previous work have revealed the attack surfaces of Web resource manipulation APIs and proposed several defense mechanisms. However, none of them provides evidence that such attacks indeed happen in the real world, measures their impacts, and evaluates the proposed defensive techniques against real attacks. This paper seeks to bridge this gap with a large-scale empirical study on Web resource manipulation behaviors in real-world Android apps. To this end, we first define the problem as cross-principal manipulation (XPM) of Web resources, and then design an automated tool named XPMChecker to detect XPM behaviors in apps. Through a study on 80,694 apps from Google Play, we find that 49.2% of manipulation cases are XPM, 4.8% of the apps have XPM behaviors, and more than 70% XPM behaviors aim at top Web sites. More alarmingly, we discover 21 apps with obvious malicious intents, such as stealing and abusing cookies, collecting user credentials and impersonating legitimate parties. For the first time, we show the presence of XPM threats in real-world apps. We also confirm the existence of such threats in iOS apps. Our experiments show that popular Web service providers are largely unaware of such threats. Our measurement results contribute to better understanding of such threats and the development of more effective and usable countermeasures.

/pdf/an-empirical-study-of-web-resource-manipulation-in-real-3svvruj5xb.pdf

An Empirical Study of Web Resource Manipulation in Real-world Mobile Applications

The invention belongs to the technical field of Android system compatibility testing, and particularly relates to a method for automatically evaluating the repairing effect of Android compatibility defects. The compatibility defect repair schemes comprise two schemes, namely simple compatibility defect inspection and replaceable compatibility defect inspection; according to the method, static analysis and machine learning are utilized to evaluate the two compatibility defect repair schemes; the method comprises the specific steps of code compatibility defect repair static analysis, compatibility defect repair feature extraction and compatibility defect repair effect evaluation, namely training and learning extracted feature vectors to obtain a final model; the model can evaluate the code compatibility check of an unknown application and output a corresponding compatibility repair scheme. According to the method, the repairing mode of API compatibility defects in the application programcan be automatically identified, and the repairing effect is classified, so that beneficial help is provided for Android platform maintainers and developers.

Automatic evaluation method for Android compatibility defect repair effect

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hao Xia

Author Tools

Chat about Author

Papers

How Android developers handle evolution-induced API compatibility issues: a large-scale study

An Empirical Study of Web Resource Manipulation in Real-world Mobile Applications

Automatic evaluation method for Android compatibility defect repair effect