Biblio

Due to the advantages and limitations of the two kinds of vulnerability mining methods of static and dynamic analysis of android applications, the paper proposes a method of Android application vulnerability mining based on dynamic and static combination. Firstly, the static analysis method is used to obtain the basic vulnerability analysis results of the application, and then the input test case of dynamic analysis is constructed on this basis. The fuzzy input test is carried out in the real machine environment, and the application security vulnerability is verified with the taint analysis technology, and finally the application vulnerability report is obtained. Experimental results show that compared with static analysis results, the method can significantly improve the accuracy of vulnerability mining.

For a long time, there have been a number of malicious APP discovery and detection services in the Android security field. There are multiple and multiple sensitive actions in most malicious apps. This paper is based on the research of dynamic and static detection technology to analyze the sensitive behaviors in APP, combined with automated testing technology to achieve automated detection, which can improve the detection efficiency and accuracy of malicious APP.

This paper proposes a real time implementation of a smart key which is a Wi-Fi based device that helps to lock/unlock all kinds of doors. Internet access allows to control doors all over the world by a simple mobile application. The app developed can be used in two modes ADMIN and GUEST mode. The ADMIN mode is protected by pin/password and is encrypted by the Advanced Encryption Standard (AES) algorithm. The password can be stored in the Key store and it can be changed whenever required. The ADMIN mode has the privilege to authenticate the GUEST mode to access all doors. For GUEST mode authentication, guests have to request the admin by using the app. Firebase is used as a server where the device and the mobile app are connected to it. Firebase is fast and accurate and hence can be accessed quickly. The main advantage of this proposed method is that it is fully operated through Internet so it can locked/unlocked wherever from the world. Comparative analysis is taken for three algorithms i.e., AES, DES and 3-DES and AES algorithm has given the best results in terms of execution time and memory usage and is implemented in the hardware lock. The experimental results give the screen shots of the app in guest and admin mode, firebase data and hardware real time implementation of the smart lock on a door.

GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a clients daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy to the existing fingerprint-based approaches, they are limited to the characteristics of the homo-morphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.

Air-gapped computers are hermetically isolated from the Internet to eliminate any means of information leakage. In this paper we present HOTSPOT - a new type of airgap crossing technique. Signals can be sent secretly from air-gapped computers to nearby smartphones and then on to the Internet - in the form of thermal pings. The thermal signals are generated by the CPUs and GPUs and intercepted by a nearby smartphone. We examine this covert channel and discuss other work in the field of air-gap covert communication channels. We present technical background and describe thermal sensing in modern smartphones. We implement a transmitter on the computer side and a receiver Android App on the smartphone side, and discuss the implementation details. We evaluate the covert channel and tested it in a typical work place. Our results show that it possible to send covert signals from air-gapped PCs to the attacker on the Internet through the thermal pings. We also propose countermeasures for this type of covert channel which has thus far been overlooked.

With growing popularity of Android, it's attack surface has also increased. Prevalence of third party android marketplaces gives attackers an opportunity to plant their malicious apps in the mobile eco-system. To evade signature based detection, attackers often transform their malware, for instance, by introducing code level changes. In this paper we propose a lightweight static Permission Flow Graph (PFG) based approach to detect malware even when they have been transformed (obfuscated). A number of techniques based on behavioral analysis have also been proposed in the past; how-ever our interest lies in leveraging the permission framework alone to detect malware variants and transformations without considering behavioral aspects of a malware. Our proposed approach constructs Permission Flow Graph (PFG) for an Android App. Transformations performed at code level, often result in changing control flow, however, most of the time, the permission flow remains invariant. As a consequences, PFGs of transformed malware and non-transformed malware remain structurally similar as shown in this paper using state-of-the-art graph similarity algorithm. Furthermore, we propose graph based similarity metrics at both edge level and vertex level in order to bring forth the structural similarity of the two PFGs being compared. We validate our proposed methodology through machine learning algorithms. Results prove that our approach is successfully able to group together Android malware and its variants (transformations) together in the same cluster. Further, we demonstrate that our proposed approach is able to detect transformed malware with a detection accuracy of 98.26%, thereby ensuring that malicious Apps can be detected even after transformations.

Mobile apps are widely adopted in daily life, and contain increasing security flaws. Many regulatory agencies and organizations have announced security guidelines for app development. However, most security guidelines involving technicality and compliance with this requirement is not easily feasible. Thus, we propose Mobile Apps Assessment and Analysis System (MAS), an automatic security validation system to improve guideline compliance. MAS combines static and dynamic analysis techniques, which can be used to verify whether android apps meet the security guideline requirements. We implemented MAS in practice and verified 143 real-world apps produced by the Taiwan government. Besides, we also validated 15,000 popular apps collected from Google Play Store produced in three countries. We found that most apps contain at least three security issues. Finally, we summarize the results and list the most common security flaws for consideration in further app development.