Biblio

The emergence of smart cars has revolutionized the automotive industry. Today's vehicles are equipped with different types of electronic control units (ECUs) that enable autonomous functionalities like self-driving, self-parking, lane keeping, and collision avoidance. The ECUs are connected to each other through an in-vehicle network, named Controller Area Network. In this talk, we will present the different cyber attacks that target autonomous vehicles and explain how an intrusion detection system (IDS) using machine learning can play a role in securing the Controller Area Network. We will also discuss the main research contributions for the security of autonomous vehicles. Specifically, we will describe our IDS, named Histogram-based Intrusion Detection and Filtering framework. Next, we will talk about the machine learning explainability issue that limits the acceptability of machine learning in autonomous vehicles, and how it can be addressed using our novel intrusion detection system based on rule extraction methods from Deep Neural Networks.

A huge amount of stored and transferred data is expanding rapidly. Therefore, managing and securing the big volume of diverse applications should have a high priority. However, Structured Query Language Injection Attack (SQLIA) is one of the most common dangerous threats in the world. Therefore, a large number of approaches and models have been presented to mitigate, detect or prevent SQL injection attack but it is still alive. Most of old and current models are created based on static, dynamic, hybrid or machine learning techniques. However, SQL injection attack still represents the highest risk in the trend of web application security risks based on several recent studies in 2021. In this paper, we present a review of the latest research dealing with SQL injection attack and its types, and demonstrating several types of most recent and current techniques, models and approaches which are used in mitigating, detecting or preventing this type of dangerous attack. Then, we explain the weaknesses and highlight the critical points missing in these techniques. As a result, we still need more efforts to make a real, novel and comprehensive solution to be able to cover all kinds of malicious SQL commands. At the end, we provide significant guidelines to follow in order to mitigate such kind of attack, and we strongly believe that these tips will help developers, decision makers, researchers and even governments to innovate solutions in the future research to stop SQLIA.

The widespread adoption of eCommerce, iBanking, and eGovernment institutions has resulted in an exponential rise in the use of web applications. Due to a large number of users, web applications have become a prime target of cybercriminals who want to steal Personally Identifiable Information (PII) and disrupt business activities. Hence, there is a dire need to audit the websites and ensure information security. In this regard, several web vulnerability scanners are employed for vulnerability assessment of web applications but attacks are still increasing day by day. Therefore, a considerable amount of research has been carried out to measure the effectiveness and limitations of the publicly available web scanners. It is identified that most of the publicly available scanners possess weaknesses and do not generate desired results. In this paper, the evaluation of publicly available web vulnerability scanners is performed against the top ten OWASP11OWASP® The Open Web Application Security Project (OWASP) is an online community that produces comprehensive articles, documentation, methodologies, and tools in the arena of web and mobile security. vulnerabilities and their performance is measured on the precision of their results. Based on these results, we proposed an Integrated Multi-Agent Blackbox Security Assessment Tool (SAT) for the security assessment of web applications. Research has proved that the vulnerabilities assessment results of the SAT are more extensive and accurate.

The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-wide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection.

A recently emerged cellular network based One-Tap Authentication (OTAuth) scheme allows app users to quickly sign up or log in to their accounts conveniently: Mobile Network Operator (MNO) provided tokens instead of user passwords are used as identity credentials. After conducting a first in-depth security analysis, however, we have revealed several fundamental design flaws among popular OTAuth services, which allow an adversary to easily (1) perform unauthorized login and register new accounts as the victim, (2) illegally obtain identities of victims, and (3) interfere OTAuth services of legitimate apps. To further evaluate the impact of our identified issues, we propose a pipeline that integrates both static and dynamic analysis. We examined 1,025/894 Android/iOS apps, each app holding more than 100 million installations. We confirmed 396/398 Android/iOS apps are affected. Our research systematically reveals the threats against OTAuth services. Finally, we provide suggestions on how to mitigate these threats accordingly.

Third party mobile applications are dominating the business strategies of organisations and have become an integral part of personal life of individuals. These applications are used for financial transactions, sharing of sensitive data etc. The recent breaches in Android clearly indicate that use of third party applications have become a serious security threat. By design, Android framework keeps all these applications in untrusted domain. Due to this a common policy of resource control exists for all such applications. Further, user discretion in granting permissions to specific applications is not effective because users are not always aware of deep functionalities, mala fide intentions (in case of spywares) and bugs/flaws in these third-party applications. In this regard, we propose a security scheme to mitigate unauthorised access of resources by third party applications. Our proposed scheme is based on SEAndroid policies and achieves fine grained confinement with respect to access control for the third party applications. To the best of our knowledge, the proposed scheme is unique and first of its kind. The proposed scheme is integrated with Android Oreo 8.1.0 for performance and security analysis. It is compatible with any Android device with AOSP support.

Mobile antivirus technologies incorporate static analysis which involves the analysis of programs without its execution. This process relies on pattern matching against a signature repository to identify malware, which can be easily tricked by transformation techniques such as obfuscation. Obfuscation as an evasion technique renders character strings disguised and incomprehensive, to prevent tampering and reengineering, which poses to be a valuable technique malware developers adopt to evade detection. This paper attempts to study the detection efficiency of static analyzers against obfuscated Android malware. This study is the first step in a larger project attempting to improve the efficiency of malware detectors.

The Android application market will conduct various security analysis on each application to predict its potential harm before put it online. Since almost all the static analysis tools can only detect malicious behaviors in the Java layer, more and more malwares try to avoid static analysis by taking the malicious codes to the Native layer. To provide a solution for the above situation, there's a new research aspect proposed in this paper and defined as Inter-language Static Analysis. As all the involved technologies are introduced, the current research results of them will be captured in this paper, such as static analysis in Java layer, binary analysis in Native layer, Java-Native penetration technology, etc.

Mobile security remains a concern for multiple stakeholders. Safe user behavior is crucial key to avoid and mitigate mobile threats. The research used a survey design to capture key constructs of mobile user threat avoidance behavior. Analysis revealed that there is no significant difference between the two key drivers of secure behavior, threat appraisal and coping appraisal, for Android and iOS users. However, statistically significant differences in avoidance motivation and avoidance behavior of users of the two operating systems were displayed. This indicates that existing threat avoidance models may be insufficient to comprehensively deal with factors that affect mobile user behavior. A newly introduced variable, perceived security, shows a difference in the perceptions of their level of protection among the users of the two operating systems, providing a new direction for research into mobile security.

Along with technological developments in the mobile environment, mobile devices are used in many areas like banking, social media and communication. The common characteristic of applications in these fields is that they contain personal or financial information of users. These types of applications are developed for Android or IOS operating systems and have become the target of attackers. To detect weakness, security analysts, perform mobile penetration tests using security analysis tools. These analysis tools have advantages and disadvantages to each other. Some tools can prioritize static or dynamic analysis, others not including these types of tests. Within the scope of the current model, we are aim to gather security analysis tools under the penetration testing framework, also contributing analysis results by data fusion algorithm. With the suggested model, security analysts will be able to use these types of analysis tools in addition to using the advantage of fusion algorithms fed by analysis tools outputs.

The competency to use mobile devices for work-related tasks gives advantages to the company productiveness and expedites business processes. Thus Bring Your Own Device (BYOD) setting emerge to enable work flexibility and technological compatibility. For management, employees’ productivity is important, but they could not jeopardise the security of information and data stored in the corporate network. Securing data and network becomes more complex tasks as it deals with foreign devices, i.e., devices that do not belong to the organisation. With much research focused on pre-implementation and the technical aspects of mobile device usage, post-implementation advancement is receiving less attention. IS audit as one of the post-implementation mechanisms provides performance evaluation of existing IS assets, business operations and process implementation, thus helping management formulating the best strategies in optimising IS practices. This paper discusses the feasibility of IS audit in assessing mobile device security by exploring the risks and vulnerabilities of mobile devices for organisational IS security as well as the perception of Information system management in mobile device security. By analysing related literature, authors pointed out how the references used in the current IS audit research address the mobile device security. This work serves a significant foundation in the future development in mobile device audit.

Attacks on mobile devices have gained a significant amount of attention lately. This is because more and more individuals are switching to smartphones from traditional non-smartphones. Therefore, attackers or cybercriminals are now getting on the bandwagon to have an opportunity at obtaining information stored on smartphones. In this paper, we present an Android mobile application that will aid to minimize data exfiltration from attacks, such as, Acoustic Side-Channel Attack, Clipboard Jacking, Permission Misuse and Malicious Apps. This paper will commence its inception with an introduction explaining the current issues in general and how attacks such as side-channel attacks and clipboard jacking paved the way for data exfiltration. We will also discuss a few already existing solutions that try to mitigate these problems. Moving on to the methodology we will emphasize how we came about the solution and what methods we followed to achieve the end goal of securing the smartphone. In the final section, we will discuss the outcomes of the project and conclude what needs to be done in the future to enhance this project so that this mobile application will continue to keep the user's data safe from the criminals' grasps.

Security vetting of Android applications is one of the crucial aspects of the Android ecosystem. Regarding the state of the art tools for this goal, most of them doesn't consider analyzing native codes and only analyze the Java code. However, Android concedes its developers to implement a part or all of their applications using C or C++ code. Thus, applying conservative manners for analyzing Android applications while ignoring native codes would lead to less precision in results. Few works have tried to analyze Android native codes, but only JN-SAF has applied taint analysis using static techniques such as symbolic execution. However, symbolic execution has some problems when is used in large programs. One of these problems is the exponential growth of program paths that would raise the path explosion issue. In this work, we have tried to alleviate this issue by introducing our new tool named CTAN. CTAN applies new symbolic execution methods to angr in a particular way that it can make JN-SAF more efficient and faster. We have introduced compositional taint analysis in CTAN by combining satisfiability modulo theories with symbolic execution. Our experiments show that CTAN is 26 percent faster than its previous work JN-SAF and it also leads to more precision by detecting more data-leakage in large Android native codes.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

The reality of today's computing landscape already suffers from a shortage of cybersecurity professionals, and this gap only expected to grow. We need to generate interest in this STEM topic early in our student's careers and provide teachers the resources they need to succeed in addressing this gap. To address this shortfall we present Practical LAbs in Security for Mobile Applications (PLASMA), a public set of educational security labs to enable instruction in creation of secure Android apps. These labs include example vulnerable applications, information about each vulnerability, steps for how to repair the vulnerabilities, and information about how to confirm that the vulnerability has been properly repaired. Our goal is for instructors to use these activities in their mobile, security, and general computing courses ranging from secondary school to university settings. Another goal of this project is to foster interest in security and computing through demonstrating its importance. Initial feedback demonstrates the labs' positive effects in enhancing student interest in cybersecurity and acclaim from instructors. All project activities may be found on the project website: http://www.TeachingMobileSecurity.com

Location sharing applications are becoming increasingly common. These applications allow users to share their own locations and view contacts’ current locations on a map. Location applications are commonly used by friends and family members to view Global Positioning System (GPS) location of an individual, but valuable forensic evidence may exist in this data when stored locally on smartphones. This paper aims to discover forensic artifacts from two popular third-party location sharing applications on iOS and Android devices. Industry standard mobile forensic suites are utilized to discover if any locally stored data could be used to assist investigations reliant on knowing the past location of a suspect. Security issues raised regarding the artifacts found during our analysis is also discussed.

In this paper, we propose the first traffic monitoring and labeling system for iOS devices, named iMonitor, which not just captures mobile network traffic in .pcap files, but also provides comprehensive APP-related and user-related information of captured packets. Through further analysis, one can obtain the exact APP or device where each packet comes from. The labeled traffic can be used in many research areas for mobile security, such as privacy leakage detection and user profiling. Given the implementation methodology of NetworkExtension framework of iOS 9+, APP labels of iMonitor are reliable enough so that labeled traffic can be regarded as training data for any traffic classification methods. Evaluations on real iPhones demonstrate that iMonitor has no notable impact upon user experience even with slight packet latency. Also, the experiment result supports our motivation that mobile traffic monitoring for iOS is absolutely necessary, as traffic generated by different OSes like Android and iOS are different and unreplaceable in researches.

With the rapid growth of mobile devices and mobile apps, mobile has surpassed desktop and now has the largest worldwide market share [1]. While such growth brings in more opportunities, it also poses new challenges in security. Among the challenges, user privacy protection has drawn tremendous attention in recent years, especially after the Facebook-Cambridge Analytica data scandal in April 2018 [2].

Nowadays mobile devices have become the majority terminals used by people for social activities so that carrying business data and private information in them have become normal. Accordingly, the risk of data related cyber attacks has become one of the most critical security concerns. The main purpose of this work is to mitigate the risk of data breaches and damages caused by malware and the lost of mobile devices. In this paper, we propose an encrypted QR code based optical challenge-response authentication by mobile devices for mounting concealed file systems. The concealed file system is basically invisible to the users unless being successfully mounted. The proposed authentication scheme practically applies cryptography and QR code technologies to challenge-response scheme in order to secure the concealed file system. The key contribution of this work is to clarify a possibility of a mounting authentication scheme involving two mobile devices using a special optical communication way (QR code exchanges) which can be realizable without involving any network accesses. We implemented a prototype system and based on the preliminary feature evaluations results we confirmed that encrypted QR code based optical challenge-response is possible between a laptop and a smart phone and it can be applied to authentication for mounting concealed file systems.

Biometric authentication offers promise for mobile security, but its adoption can be controversial, both from a usability and security perspective. We describe a preliminary study, comparing recollections of biometric adoption by computer security experts and non-experts collected in semi-structured interviews. Initial decisions and thought processes around biometric adoption were recalled, as well as changes in those views over time. These findings should serve to better inform security education across differing levels of technical experience. Preliminary findings indicate that both user groups were influenced by similar sources of information; however, expert users differed in having more professional requirements affecting choices (e.g., BYOD). Furthermore, experts often added biometric authentication methods opportunistically during device updates, despite describing higher security concern and caution. Non-experts struggled with the setting up fingerprint biometrics, leading to poor adoption. Further interviews are still being conducted.

Modern operating systems, such as iOS, use multiple access control policies to define an overall protection system. However, the complexity of these policies and their interactions can hide policy flaws that compromise the security of the protection system. We propose iOracle, a framework that logically models the iOS protection system such that queries can be made to automatically detect policy flaws. iOracle models policies and runtime context extracted from iOS firmware images, developer resources, and jailbroken devices, and iOracle significantly reduces the complexity of queries by modeling policy semantics. We evaluate iOracle by using it to successfully triage executables likely to have policy flaws and comparing our results to the executables exploited in four recent jailbreaks. When applied to iOS 10, iOracle identifies previously unknown policy flaws that allow attackers to modify or bypass access control policies. For compromised system processes, consequences of these policy flaws include sandbox escapes (with respect to read/write file access) and changing the ownership of arbitrary files. By automating the evaluation of iOS access control policies, iOracle provides a practical approach to hardening iOS security by identifying policy flaws before they are exploited.

The rapid development of mobile networks has revolutionized the way of accessing the Internet. The exponential growth of mobile subscribers, devices and various applications frequently brings about excessive traffic in mobile networks. The demand for higher data rates, lower latency and seamless handover further drive the demand for the improved mobile network design. However, traditional methods can no longer offer cost-efficient solutions for better user quality of experience with fast time-to-market. Recent work adopts SDN in LTE core networks to meet the requirement. In these software defined LTE core networks, scalability and security become important design issues that must be considered seriously. In this paper, we propose a scalable channel security scheme for the software defined LTE core network. It applies the VxLAN for scalable tunnel establishment and MACsec for security enhancement. According to our evaluation, the proposed scheme not only enhances the security of the channel communication between different network components, but also improves the flexibility and scalability of the core network with little performance penalty. Moreover, it can also shed light on the design of the next generation cellular network.

What makes a security visualization effective? How do we measure visualization effectiveness in the context of investigating, analyzing, understanding and reporting cyber security incidents? Identifying and understanding cyber-attacks are critical for decision making - not just at the technical level, but also the management and policy-making levels. Our research studied both questions and extends our Security Visualization Effectiveness Measurement (SvEm) framework by providing a full-scale effectiveness approach for both theoretical and user-centric visualization techniques. Our framework facilitates effectiveness through interactive three-dimensional visualization to enhance both single and multi-user collaboration. We investigated effectiveness metrics including (1) visual clarity, (2) visibility, (3) distortion rates and (4) user response (viewing) times. The SvEm framework key components are: (1) mobile display dimension and resolution factor, (2) security incident entities, (3) user cognition activators and alerts, (4) threat scoring system, (5) working memory load and (6) color usage management. To evaluate our full-scale security visualization effectiveness framework, we developed VisualProgger - a real-time security visualization application (web and mobile) visualizing data provenance changes in SvEm use cases. Finally, the SvEm visualizations aims to gain the users' attention span by ensuring a consistency in the viewer's cognitive load, while increasing the viewer's working memory load. In return, users have high potential to gain security insights in security visualization. Our evaluation shows that viewers perform better with prior knowledge (working memory load) of security events and that circular visualization designs attract and maintain the viewer's attention span. These discoveries revealed research directions for future work relating to measurement of security visualization effectiveness.

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

We introduce MobiCeal, the first practical Plausibly Deniable Encryption (PDE) system for mobile devices that can defend against strong coercive multi-snapshot adversaries, who may examine the storage medium of a user's mobile device at different points of time and force the user to decrypt data. MobiCeal relies on "dummy write" to obfuscate the differences between multiple snapshots of storage medium due to existence of hidden data. By incorporating PDE in block layer, MobiCeal supports a broad deployment of any block-based file systems on mobile devices. More importantly, MobiCeal is secure against side channel attacks which pose a serious threat to existing PDE schemes. A proof of concept implementation of MobiCeal is provided on an LG Nexus 4 Android phone using Android 4.2.2. It is shown that the performance of MobiCeal is significantly better than prior PDE systems against multi-snapshot adversaries.