With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. This article categorizes systems by methodology and date to evaluate progression and weaknesses. This article also discusses evaluations of industry solutions, malware statistics, and malware evasion techniques and concludes by supporting future research paths.

Mobile devices and their application marketplaces drive the entire economy of the today's mobile landscape. Android platforms alone have produced staggering revenues, exceeding five billion USD, which has attracted cybercriminals and increased malware in Android markets at an alarming rate. To better understand this slew of threats, we present CopperDroid, an automatic VMI-based dynamic analysis system to reconstruct the behaviors of Android malware. The novelty of CopperDroid lies in its agnostic approach to identify interesting OS-and high-level Android-specific behaviors. It reconstructs these behaviors by observing and dissecting system calls and, therefore, is resistant to the multitude of alterations the Android runtime is subjected to over its life-cycle. CopperDroid automatically and accurately reconstructs events of interest that describe, not only well-known process-OS interactions (e.g., file and process creation), but also complex intra-and inter-process communications (e.g., SMS reception), whose semantics are typically contextualized through complex Android objects. Because CopperDroid's reconstruction mechanisms are agnostic to the underlying action invocation methods, it is able to capture actions initiated both from Java and native code execution. CopperDroid's analysis generates detailed behavioral profiles that abstract a large stream of low-level-often uninteresting-events into concise, high-level semantics, which are well-suited to provide insightful behavioral traits and open the possibility to further research directions. We carried out an extensive evaluation to assess the capabilities and performance of CopperDroid on more than 2,900 Android malware samples. Our experiments show that CopperDroid faithfully reconstructs OSand Android-specific behaviors. Additionally, we demonstrate how CopperDroid can be leveraged to disclose additional behaviors through the use of a simple, yet effective, app stimulation technique. Using this technique, we successfully triggered and disclosed additional behaviors on more than 60% of the analyzed malware samples. This qualitatively demonstrates the versatility of CopperDroid's ability to improve dynamic-based code coverage. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.

In the current economy roughly 90% of all world trade is transported by the shipping industry, which is now accelerating its technological growth. While the demand on mariners, ship owners, and the encompassing maritime community for digital advances (particularly towards digitization and automation) has led to efficient shipping operations, maritime cybersecurity is a pertinent issue of equal importance. As hackers are becoming increasingly aware of cyber-vulnerabilities within the maritime sector, and as existing risk assessment tools do not adequately represent the unique nature of maritime cyber-threats, this article introduces a model-based risk assessment framework which considers a combination of cyber and maritime factors. Confronted with a range of ship functionalities, configurations, users, and environmental factors, this framework aims to comprehensively present maritime cyber-risks and better inform those in the maritime community when making cyber-security decisions. By providing the needed maritime-cyber risk profiles, it becomes possible to support a range of parties, such as operators, regulators, insurers, and mariners, in increasing overall global maritime cyber-security.

As a $183.3 Billion industry controlling 90% of all world trade, the shipping community is continuously looking for methods to increase profits while still considering human and environmental safety. As a result of developing technologies and policy that make autonomy a feasible solution, at least three separate organizations are aiming to produce and sail their first autonomous ships by 2020. Thus it is essential to begin assessing their cyber-risk profiles in order to rank and mitigate any vulnerabilities. As existing risk models for physical ship safety and autonomous cars do not adequately represent the unique nature of cyber-threats for autonomous vessels within the maritime sector, this article applies a model-based risk assessment framework named MaCRA which had previous only been used to model existing ships, not those of the near-future.