AnFlo: Detecting anomalous sensitive information flows in Android apps

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for "Health" apps to share heart rate information through the Internet but is anomalous for "Travel" apps. In this paper, we propose a novel approach to detect anomalous sensitive data flows in Android apps, with improved accuracy. To achieve this objective, we first group trusted apps according to the topics inferred from their functional descriptions. We then learn sensitive information flows with respect to each group of trusted apps. For a given app under analysis, anomalies are identified by comparing sensitive information flows in the app against those flows learned from trusted apps grouped under the same topic. In the evaluation, information flow is learned from 11,796 trusted apps. We then checked for anomalies in 596 new (benign) apps and identified 2 previously-unknown vulnerable apps related to anomalous flows. We also analyzed 18 malware apps and found anomalies in 6 of them

Automatic Input Rectification

We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that is satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the program is highly likely to process correctly. Our experimental results show that, for a set of benchmark applications (namely, Google Picasa, ImageMagick, VLC, Swfdec, and Dillo), this approach effectively converts malicious inputs (which successfully exploit vulnerabilities in the application) into benign inputs that the application processes correctly. Moreover, a manual code analysis shows that, if an input does satisfy the learned constraints, it is incapable of exploiting these vulnerabilities. We also present the results of a user study designed to evaluate the subjective perceptual quality of outputs from benign but atypical inputs that have been automatically rectified by SOAP to conform to the learned constraints. Specifically, we obtained benign inputs that violate learned constraints, used our input rectifier to obtain rectified inputs, then paid Amazon Mechanical Turk users to provide their subjective qualitative perception of the difference between the outputs from the original and rectified inputs. The results indicate that rectification can often preserve much, and in many cases all, of the desirable data in the original input

Malware detection and analysis via layered annotative execution

Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving to be more sophisticated and evasive to strike against current malware analysis and defense systems. This dissertation takes a root-cause oriented approach to the problem of automatic malware detection and analysis. In this approach, we aim to capture the intrinsic natures of malicious behaviors, rather than the external symptoms of existing attacks. We propose a new architecture for binary code analysis, which is called whole-system out-of-the-box fine-grained dynamic binary analysis, to address the common challenges in malware detection and analysis. to realize this architecture, we build a unified and extensible analysis platform, codenamed TEMU. We propose a core technique for fine-grained dynamic binary analysis, called layered annotative execution, and implement this technique in TEMU. Then on the basis of TEMU, we have proposed and built a series of novel techniques for automatic malware detection and analysis. For postmortem malware analysis, we have developed Renovo, Panorama, HookFinder, and MineSweeper, for detecting and analyzing various aspects of malware. For proactive malware detection, we have built HookScout as a proactive hook detection system. These techniques capture intrinsic characteristics of malware and thus are well suited for dealing with new malware samples and attack mechanisms

pDroid

When an end user attempts to download an app on the Google Play Store they receive two related items that can be used to assess the potential threats of an application, the list of permissions used by the application and the textual description of the application. However, this raises several concerns. First, applications tend to use more permissions than they need and end users are not tech-savvy enough to fully understand the security risks. Therefore, it is challenging to assess the threats of an application fully by only seeing the permissions. On the other hand, most textual descriptions do not clearly define why they need a particular permission. These two issues conjoined make it difficult for end users to accurately assess the security threats of an application. This has lead to a demand for a framework that can accurately determine if a textual description adequately describes the actual behavior of an application. In this Master Thesis, we present pDroid (short for privateDroid), a market-independent framework that can compare an Android application’s textual description to its internal behavior. We evaluated pDroid using 1562 benign apps and 243 malware samples, and pDroid correctly classified 91.4% of malware with a false positive rate of 4.9%

09141 Abstracts Collection -- Web Application Security

From 29th March to 3rd April 2009 the Dagstuhl Seminar 09141 Web Application Security was held in Schloss Dagstuhl -- Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. Links to full papers (if available) are provided in the corresponding seminar summary document

Automatic input rectification

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 51-55).We present a novel technique, automatic input rectification, and a prototype implementation, SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that it satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the program is highly likely to process correctly. Our experimental results show that, for a set of benchmark applications (Google Picasa, ImageMagick, VLC, Swfdec, and Dillo), this approach effectively converts malicious inputs (which successfully exploit vulnerabilities in the application) into benign inputs that the application processes correctly. Moreover, a manual code analysis shows that, if an input does satisfy the learned constraints, it is incapable of exploiting these vulnerabilities. We also present the results of a user study designed to evaluate the subjective perceptual quality of outputs from benign but atypical inputs that have been automatically rectified by SOAP to conform to the learned constraints. Specifically, we obtained benign inputs that violate learned constraints, used our input rectifier to obtain rectified inputs, then paid Amazon Mechanical Turk users to provide their subjective qualitative perception of the difference between the outputs from the original and rectified inputs. The results indicate that rectification can often preserve much, and in many cases all, of the desirable data in the original input.by Fan Long.S.M

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

A taxonomy of approaches for integrating attack awareness in applications

Software applications are subject to an increasing number of attacks, resulting in data breaches and financial damage. Many solutions have been considered to help mitigate these attacks, such as the integration of attack-awareness techniques. In this paper, we propose a taxonomy illustrating how existing attack awareness techniques can be integrated into applications. This work provides a guide for security researchers and developers, aiding them when choosing the approach which best fits the needs of their application

Android Encrypted Network Traffic to Identify User Actions

Network forensics is a sub-branch of digital forensics relating to the monitoring and analysis of computer network traffic for the purposes of information gathering, legal evidence. Unlike other areas of digital forensics, network investigations deal with volatile and dynamic information. Network traffic is transmitted and then lost, so network forensics is often a pro-active investigation. Network forensics generally has two uses. The first, relating to security, involves monitoring a network for anomalous traffic and identifying intrusions. The second form relates to law enforcement. In this case analysis of captured network traffic can include tasks such as reassembling transferred files, searching for keywords and parsing human communication such as emails or chat sessions. Nowadays use of mobile apps to communicate with friends. Not only communication purpose it gets information about sensitive topics such as diseases, sexual or religious preferences, etc. Numerous worries have been raised about the capabilities of these portable devices to occupy the privacy of users actually becoming “tracking devices”. Above problem they influence in our work to find solution using machine learning techniques. It is used to protect the content of a packet. Our framework analyzes the network communications and leverages information available in TCP/IP packets like IP addresses and ports, together with other information like the size, the direction, and the timing. Our system, for each app they ?rst pre-process a dataset of network packets labeled with the user actions that originated them, they cluster them in ?ow typologies that represent recurrent network ?ows, and ?nally it analyze them in order to create a training set that will be used to feed a classi?er. The trained classi?er will then be able to classify new traf?c traced. Our approach results shows it accuracy and precision more than 95% for most of the considered actions