This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.

/pdf/the-economics-and-psychology-of-personality-traits-155avz5auq.pdf

The Economics and Psychology of Personality Traits

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware.This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

/pdf/a-survey-on-automated-dynamic-malware-analysis-techniques-2b10kp3xz0.pdf

A survey on automated dynamic malware-analysis techniques and tools

Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.

/pdf/beyond-blacklists-learning-to-detect-malicious-web-sites-7uso5yqleg.pdf

Beyond blacklists: learning to detect malicious web sites from suspicious URLs

In this paper, we present a systematic study for the detection of malicious applications (or apps) on popular Android Markets. To this end, we first propose a permissionbased behavioral footprinting scheme to detect new samples of known Android malware families. Then we apply a heuristics-based filtering scheme to identify certain inherent behaviors of unknown malicious families. We implemented both schemes in a system called DroidRanger. The experiments with 204, 040 apps collected from five different Android Markets in May-June 2011 reveal 211 malicious ones: 32 from the official Android Market (0.02% infection rate) and 179 from alternative marketplaces (infection rates ranging from 0.20% to 0.47%). Among those malicious apps, our system also uncovered two zero-day malware (in 40 apps): one from the official Android Market and the other from alternative marketplaces. The results show that current marketplaces are functional and relatively healthy. However, there is also a clear need for a rigorous policing process, especially for non-regulated alternative marketplaces.

/pdf/hey-you-get-off-of-my-market-detecting-malicious-apps-in-2hdanyr5j8.pdf

Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the fundamental trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this fundamental trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.

/pdf/panorama-capturing-system-wide-information-flow-for-malware-2wme8tbeoz.pdf

Panorama: capturing system-wide information flow for malware detection and analysis

Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.

/pdf/detecting-stealth-software-with-strider-ghostbuster-59do3la6rp.pdf

Detecting stealth software with Strider GhostBuster

Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed.In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping "hooking" operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.

/pdf/gatekeeper-monitoring-auto-start-extensibility-points-aseps-n0om0l2rn2.pdf

Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management

As computer programs grow more complex, extensible, and connected, it becomes increasingly difficult for users to understand what has changed on their machines and what impact those changes have. An embodiment of the invention is described via a software tool, called AskStrider, that answers those questions by correlating volatile process information with persistent-state context information and change history. AskStrider scans a system for active components, matches them against a change log to identify recently updated and hence more interesting state, and searches for context information to help users understand the changes. Several real-world cases are provided to demonstrate the effectiveness of using AskStrider to quickly identify the presence of unwanted software, to determine if a software patch is potentially breaking an application, and to detect lingering components left over from an unclean uninstallation.

Changed file identification, software conflict resolution and unwanted file removal

Systems and methods for implementing system management which are based on reviewing of the interactions between one or more programs and the persistent state they tend to represent. The system provides for the detection of modifications that occur within a system, verifying whether the modifications are approved or not and generating notifications on detecting unknown modifications.

System analysis and management

A monitoring service is provided that detects spyware or other unwanted software at the time it is installed and/or allows for the spyware's removal. The service monitors "Auto-Start Extensibility Points" ("ASEPs") to detect spyware installations. ASEPs refer to the configuration points that can be "hooked" to allow programs to be auto-started without explicit user invocation. Such a service is particularly effective because an overwhelming majority of spyware programs infect systems in such a way that they are automatically started upon reboot and the launch of many commonly used applications. The monitoring service can thus lead to the subsequent complete removal of the spyware installation, and does not require a frequent signature-based cleaning. Spyware that is bundled with other software such as freeware or shareware can also be removed.

Managing spyware and unwanted software through auto-start extensibility points

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