Biblio

Intelligent gadgets for example smartphones, tablet phones, and personal digital assistants play an increasingly important part in our lives and have become indispensable in our everyday routines. As a result, the market for mobile apps tends to grow at a rapid rate, and mobile app utilization has long eclipsed that of desktop software. The applications based on these smartphones are becoming vulnerable due to the use of open-source operating systems in these smart devices. These applications are vulnerable to smartphones because of memory leaks; they can steal personal data, hack our smartphones, and monitor our private activity, giving anyone significant financial loss. Because of these issues, smartphone security plays a vital role in our daily lives. The Play Store contains unrated applications which any unprofessional developer can develop, and these applications do not pass through the rigorous process of testing and analysis of code leaks. The existing developed system does not include a stringent procedure to examine and investigate source code to detect such vulnerabilities among mobile applications. This paper presented a dynamic analysis-based robust system for Source Code Analysis of Mobile Applications for Privacy Leaks using a machine learning algorithm. Furthermore, our framework is called Source Code Analysis of Mobile Applications (SCA-MA), which combines DynaLog and our machine learning-based classifier for Source Code Analysis of Mobile Applications. Our dataset will contain around 20000 applications to test and analyze vulnerabilities. We will perform dynamic analysis and separate the classification of vulnerable applications and safe applications. Our results show that we can detect vulnerabilities through our proposed system while reviewing code and provide better results than other existing frameworks. We have evaluated our large dataset with the pervasive way so we can detect even small privacy leak which can harm our app. Finally, we have compared results with existing methods, and framework performance is better than other methods.

The current paper is proposing a three-factor authentication (3FA) scheme based on three components. In the first component a token and a password will be generated (this module represents the kernel of the three-factor authentication scheme - 3FA). In the second component a pass-code will be generated, using to the token resulted in the first phase. We will use RSA for encryption and decryption of the generated values (token and pass-code). For the token ID and passcode the user will use his smartphone. The third component uses a searchable encryption scheme, whose purpose is to retrieve the documents of the user from the cloud server, based on a keyword and his/her fingerprint. The documents are stored encrypted on a mistrust server (cloud environment) and searchable encryption will help us to search specific information and to access those documents in an encrypted content. We will introduce also a software simulation developed in C\# 8.0 for our scheme and a source code analysis for the main algorithms.

This paper presents an overview of the H2020 project VESSEDIA [9] aimed at verifying the security and safety of modern connected systems also called IoT. The originality relies in using Formal Methods inherited from high-criticality applications domains to analyze the source code at different levels of intensity, to gather possible faults and weaknesses. The analysis methods are mostly exhaustive an guarantee that, after analysis, the source code of the application is error-free. This paper is structured as follows: after an introductory section 1 giving some factual data, section 2 presents the aims and the problems addressed; section 3 describes the project's use-cases and section 4 describes the proposed approach for solving these problems and the results achieved until now; finally, section 5 discusses some remaining future work.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

A program exhibits trigger-based behavior if it performs undocumented, often malicious, functions when the environmental conditions and/or specific input values match some pre-specified criteria. Checking whether such hidden functions exist in the program is important for increasing trustworthiness of software. In this paper, we propose a framework to effectively detect trigger-based behavior at the source code level. Our approach is semi-automated: We use automated source code instrumentation and mixed concrete and symbolic execution to generate potentially suspicious test cases that may trigger hidden, potentially malicious functions. The test cases must be investigated by a human analyst manually to decide which of them are real triggers. While our approach is not fully automated, it greatly reduces manual work by allowing analysts to focus on a few test cases found by our automated tools.

Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries.

In this ubiquitous IoT (Internet of Things) era, web services have become a vital part of today's critical national and public sector infrastructure. With the industry wide adaptation of service-oriented architecture (SOA), web services have become an integral component of enterprise software eco-system, resulting in new security challenges. Web services are strategic components used by wide variety of organizations for information exchange on the internet scale. The public deployments of mission critical APIs opens up possibility of software bugs to be maliciously exploited. Therefore, vulnerability identification in web services through static as well as dynamic analysis is a thriving and interesting area of research in academia, national security and industry. Using OWASP (Open Web Application Security Project) web services guidelines, this paper discusses the challenges of existing standards, and reviews new techniques and tools to improve services security by detecting vulnerabilities. Recent vulnerabilities like Shellshock and Heartbleed has shifted the focus of risk assessment to the application layer, which for majority of organization means public facing web services and web/mobile applications. RESTFul services have now become the new service development paradigm normal; therefore SOAP centric standards such as XML Encryption, XML Signature, WS-Security, and WS-SecureConversation are nearly not as relevant. In this paper we provide an overview of the OWASP top 10 vulnerabilities for web services, and discuss the potential static code analysis techniques to discover these vulnerabilities. The paper reviews the security issues targeting web services, software/program verification and security development lifecycle.

There has been an ongoing trend toward collaborative software development using open and shared source code published in large software repositories on the Internet. While traditional source code analysis techniques perform well in single project contexts, new types of source code analysis techniques are ermerging, which focus on global source code analysis challenges. In this article, we discuss how the Semantic Web, can become an enabling technology to provide a standardized, formal, and semantic rich representations for modeling and analyzing large global source code corpora. Furthermore, inference services and other services provided by Semantic Web technologies can be used to support a variety of core source code analysis techniques, such as semantic code search, call graph construction, and clone detection. In this paper, we introduce SeCold, the first publicly available online linked data source code dataset for software engineering researchers and practitioners. Along with its dataset, SeCold also provides some Semantic Web enabled core services to support the analysis of Internet-scale source code repositories. We illustrated through several examples how this linked data combined with Semantic Web technologies can be harvested for different source code analysis tasks to support software trustworthiness. For the case studies, we combine both our linked-data set and Semantic Web enabled source code analysis services with knowledge extracted from StackOverflow, a crowdsourcing website. These case studies, we demonstrate that our approach is not only capable of crawling, processing, and scaling to traditional types of structured data (e.g., source code), but also supports emerging non-structured data sources, such as crowdsourced information (e.g., StackOverflow.com) to support a global source code analysis context.