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This paper introduces a new hydraulic manipulator with hydraulic-cable driven actuation (HCA) modules for disaster response mobile-manipulation. The hydraulic actuation system has the potential to apply disaster-response application, because it has a higher power-to-weight ratio and robustness to external impacts than electric motor actuation. However, using a conventional hydraulic manipulators is inappropriate because the revolute joint uses conventional actuators, such as linear cylinders and vanes, which have some limitations: 1) linear cylinder: small range of motion, 2) vane: low torque-toweight ratio. To overcome these limitations, we propose new 3DOF manipulator which has a larger workspace than the conventional hydraulic manipulator and comparable payloadto-weight ratio. To this end, we use hydraulic-cable driven actuation modules from our previous research. Experimental results verify the basic performance of the actuator modules and manipulator and their capability to perform various disaster response tasks.

Perfluorobutyl (C4) substituted linear C4-C4 ether linked tri-sodium sulfonate derivatives are designed, synthesized and characterized as alternative substances to perfluoroctanesulfonic acid (PFOS, 1 ), a well known surfactant used in various industrial and consumer products. The tri-sodium sulfonate was synthesized from commercially available octafluoro-1,4-diiodo butane as starting material in 9 steps using radical addition, Upjohn dihydroxylation and nucleophilic substitution reactions. Simultaneously, we have also synthesized di-branched C4-C4 perfluorobutyl alkyl mono-sodium sulfonate and di-sodium sulfonate. We have illustrated the surface tension behavior and calculated critical micelle concentration values for all three substances synthesized.

Detecting repackaged apps is one of the important issues in the Android ecosystem. Many attackers usually reverse engineer a legitimate app, modify or embed malicious codes into the app, repackage and distribute it in the online markets. They also employ code obfuscation techniques to hide app cloning or repackaging. In this paper, we propose a new technique for detecting repackaged Android apps, which is robust to code obfuscation. The technique analyzes the similarity of Android apps based on the method call information of component classes that receive implicit intents. We developed a tool Calldroid that implemented the proposed technique, and evaluated it on apps transformed using well-known obfuscators. The evaluation results showed that the proposed technique can effectively detect repackaged apps.

Accurate malware detection is important to protect Android users against the growing number of sophisticated malwares. In this paper, we propose a simple but efficient malware detection methodology that identifies the subset of Android APIs as classification features. Since each app needs to use a set of Android APIs to fulfill its main objective, the list of APIs used in an app represents the app’s characteristic. Our methodology constructs two ranked lists of Android APIs, namely benign_API_list and malicious_API_list. The benign_API_list contains the most commonly invoked APIs among benign apps and the malicious_API_list contains the most commonly invoked APIs among malicious apps. Then, for a given suspicious app, we compute the sum of inverse values of the rankings of the used benign APIs and also the sum of inverse value s of the ranking of the used malicious APIs. We determine whether the app is benign or malicious by comparing the two sums. More specifically, if the sum of inverse values based on benign apps is larger than the one based on malicious apps, we determine that the given app is benign. Otherwise, we determine that the given app is malicious. Our experimental evaluation shows that the proposed methodology achieves an accuracy of 87.35%~89.93% for Android malware detection. The proposed method can be possibly utilized in features selections in machine learning-based malware detection algorithms.

Software filtering systems can be employed to detect and filter out pirated or counterfeit software on the Web sites and peer-to-peer networks. They determine whether a suspicious program is legal or not by comparing it with original programs in a database or in the market. To identify pirated or counterfeit software, software filtering systems need to measure software similarity when comparing a suspicious program with original ones. In this case, the comparison overhead might be very high because the suspicious program is compared with all programs in the database or market in the worst case. This paper proposes a software classification scheme for efficient software filtering systems. The scheme focuses specifically on the Windows portable executable files which have been prime targets for software pirates. The scheme extracts software characteristics from a suspicious program and classifies it into one of pre-defined categories quickly based on the characteristics. The suspicious program is compared only with the programs in the one of pre-defined categories in most cases; thus, the comparison overhead is reduced. We propose two classification methods. The first one extracts strings from GUI-related resources of a program and computes the relevance of the program to each category based on the pre-computed score of the strings. The second one extracts API call frequency from a program's execution codes and uses Random Forest technique to classify the program. Experimental results show that the proposed scheme can classify programs effectively and can reduce the comparison overhead significantly.

Most safety inspections of large piping structures are performed by humans. However, this approach requires considerable time and money and does not guarantee the safety of the inspectors. The proposed pipe-climbing robot is designed to move on the outside of large pipe structures and perform non-destructive inspection in place of human inspectors. Required to negotiate external obstacles such as fixtures, flanges, and valves, as well as pipe components such as elbow and T-branch joints, the proposed pipe-climbing robot comprises two driving modules and an actuated connecting arm. By working together, the two driving modules overcome the various obstacle components in a pipeline network. The wheel-driven locomotion mechanisms of the system also enable it to move quickly along the pipeline. A strain gauge is attached to the climbing arm to confirm the grasping state of the robot on the pipe. The robot is designed to attach stably to a pipe, which features a very low backlash, by means of worm drive and ball screw mechanisms. Experiments show the ability of the robot to grasp stably on a pipeline and climb the pipeline at a speed of 18 cm/s.

The paper proposes a new technique to detect Android malware effectively based on converting malware binaries into images and applying machine learning techniques on those images. Existing research converts the whole executable files (e.g., DEX files in Android application package) of target apps into images and uses them for machine learning. However, the entire DEX file (consisting of header section, identifier section, data section, optional link data area, etc.) might contain noisy information for malware detection. In this paper, we convert only data sections of DEX files into grayscale images and apply machine learning on the images with Convolutional Neural Networks (CNN). By using only the data sections for 5,377 malicious and 6,249 benign apps, our technique reduces the storage capacity by 17.5% on average compared to using the whole DEX files. We apply two CNN models, Inception-v3 and Inception-ResNet-v2, which are known to be efficient in image processing, and examine the effectiveness of our technique in terms of accuracy. Experiment results show that the proposed technique achieves better accuracy with smaller storage capacity than the approach using the whole DEX files. Inception-ResNet-v2 with the stochastic gradient descent (SGD) optimization algorithm reaches 98.02% accuracy.

Unity is the most popular cross-platform development framework to develop games for multiple platforms such as Android, iOS, and Windows Mobile. While Unity developers can easily develop mobile apps for multiple platforms, adversaries can also easily build malicious apps based on the “write once, run anywhere” (WORA) feature. Even though malicious apps were discovered among Android apps written with Unity framework (Unity apps), little research has been done on analysing the malicious apps. We propose static and dynamic reverse engineering techniques for malicious Unity apps. We first inspect the executable file format of a Unity app and present an effective static analysis technique of the Unity app. Then, we also propose a systematic technique to analyse dynamically the Unity app. Using the proposed techniques, the malware analyst can statically and dynamically analyse Java code, native code in C or C ++, and the Mono runtime layer where the C# code is running.

As licensed programs are pirated and illegally spread over the Internet, it is necessary to filter illegally distributed or cracked programs. The conventional software filtering systems can prevent unauthorized dissemination of the programs maintained by their databases using an exact matching method where the feature of a suspicious program is the same as that of any program stored in the database. However, the conventional filtering systems have some limitations to deal with cracked or new programs which are not maintained by their database. To address the limitations, we design and implement an efficient and intelligent software filtering system based on software similarity. Our system measures the similarity of the characteristics extracted from an original program and a suspicious one (or, a cracked one) and then determines whether the suspicious program is a cracked version of the copyrighted original program based on the similarity measure. In addition, the proposed system can handle a new program by categorizing it using a machine learning scheme. This scheme helps an unknown program to be identified by narrowing the search space. To demonstrate the effectiveness of the proposed system, we perform a series of experiments on a number of executable programs under Microsoft Windows. The experimental results show that our system has achieved comparable performance.

Android applications are exposed to reverse engineering attacks. In particular, the applications written in Java are more prone to reverse engineering in comparison to the applications written in native-code languages such as C or C++ on the Android platform. This is because Java applications are distributed as byte codes, while applications written in native-code languages are distributed as low-level binary codes. In this paper, we propose a new technique to protect Android applications against reverse engineering. Three key characteristics of the proposed approach are as follows. First, we write the main parts of the application in native-code using Android NDK. This not only makes reverse engineering more difficult, but it is also more effective in terms of code reuse. Second, we introduce obfuscation, which hides the intent of the native codes and obscures theirs structure, at the intermediate representation (IR) level. Finally, we integrate an integrity verification scheme which detects whether the critical module of the application has been modified prior to execution of the application. Based on the results of experimentation on five known Android applications, we show that the proposed techniques can be applied without a significant effect on performance.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.