NDLI: FORECAST: skimming off the malware cream

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Understanding the prevalence and use of alternative plans in malware with network games

Distilling critical attack graph surface iteratively through minimum-cost SAT solving

"Mix-in-Place" anonymous networking using secure function evaluation

The socialbot network: when bots socialize for fame and money

A peel of onion

The science of cyber security experimentation: the DETER project

Facing the facts about image type in recognition-based graphical passwords

Improving robustness of DNS to software vulnerabilities

Exploring the potential benefits of expanded rate limiting in Tor: slow and steady wins the race with Tortoise

SEMAGE: a new image-based two-factor CAPTCHA

ASIDE: IDE support for web application security

An empirical study of visual security cues to prevent the SSLstripping attack

Key escrow from a safe distance: looking back at the Clipper Chip

Reliable telemetry in white spaces using remote attestation

Mitigating code-reuse attacks with control-flow locking

Nexat: a history-based approach to predict attacker actions

BareBox: efficient malware analysis on bare-metal

FORECAST: skimming off the malware cream

RIPE: runtime intrusion prevention evaluator

Security through amnesia: a software-based solution to the cold boot attack on disk encryption

Detecting and resolving privacy conflicts for collaborative data sharing in online social networks

PhorceField: a phish-proof password ceremony

Enabling secure VM-vTPM migration in private clouds

"Super nodes" in Tor: existence and security implication

BLOCK: a black-box approach for detection of state violation attacks towards web applications

Tracking payment card data flow using virtual machine state introspection

AdSentry: comprehensive and flexible confinement of JavaScript-based advertisements

Don't Bump, Shake on It: the exploitation of a popular accelerometer-based smart phone exchange and its secure replacement

deRop: removing return-oriented programming from malware

From prey to hunter: transforming legacy embedded devices into exploitation sensor grids

Automated remote repair for mobile malware

Detecting malware's failover C&C strategies with squeeze

Hit 'em where it hurts: a live security exercise on cyber situational awareness

Private search in the real world

Social snapshots: digital forensics for online social networks

Dynamic sample size detection in continuous authentication using sequential sampling

Exposing invisible timing-based traffic watermarks with BACKLIT

Smart metering de-pseudonymization

A server- and browser-transparent CSRF defense for web 2.0 applications

WebJail: least-privilege integration of third-party components in web mashups

Attacks on WebView in the Android system

Static detection of malicious JavaScript-bearing PDF documents

FORECAST: skimming off the malware cream

Content Provider	ACM Digital Library
Author	Kruegel, Christopher Jacob, Gregoire Comparetti, Paolo Milani Neugschwandtner, Matthias
Abstract	To handle the large number of malware samples appearing in the wild each day, security analysts and vendors employ automated tools to detect, classify and analyze malicious code. Because malware is typically resistant to static analysis, automated dynamic analysis is widely used for this purpose. Executing malicious software in a controlled environment while observing its behavior can provide rich information on a malware's capabilities. However, running each malware sample even for a few minutes is expensive. For this reason, malware analysis efforts need to select a subset of samples for analysis. To date, this selection has been performed either randomly or using techniques focused on avoiding re-analysis of polymorphic malware variants [41, 23]. In this paper, we present a novel approach to sample selection that attempts to maximize the total value of the information obtained from analysis, according to an application-dependent scoring function. To this end, we leverage previous work on behavioral malware clustering [14] and introduce a machine-learning-based system that uses all statically-available information to predict into which behavioral class a sample will fall, before the sample is actually executed. We discuss scoring functions tailored at two practical applications of large-scale dynamic analysis: the compilation of network blacklists of command and control servers and the generation of remediation procedures for malware infections. We implement these techniques in a tool called ForeCast. Large-scale evaluation on over 600,000 malware samples shows that our prototype can increase the amount of potential command and control servers detected by up to 137% over a random selection strategy and 54% over a selection strategy based on sample diversity.
Starting Page	11
Ending Page	20
Page Count	10
File Format	PDF
ISBN	9781450306720
DOI	10.1145/2076732.2076735
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-12-05
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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