New weapon revealed for defense against zero-day attacks

LAS VEGAS — Signature-based malware detection tools are passé. “Everyone knows how to do it,” said Brad Wilson, vice president of product development for Trusted Computer Solutions Inc.

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That does not mean they are unnecessary. No network or PC is likely to be secure if it is not protected by signature-based tools that can quickly detect and block known malicious code for which signatures are available. But even the best-maintained systems remain vulnerable to zero-day exploits and that window of opportunity between the time a threat is identified and the signature is actually updated. These gaps are addressed by behavioral anomaly detections tools that identify previously unknown threats because they are behaving badly.

TCS is announcing at the Black Hat security conference this week the release this fall of the first new version of the CounterStorm network anomaly detection tools since acquiring the parent company a year ago.

Quantifying the relative risks of zero-day attacks and known threats for which signatures are available but which still penetrate networks through increasingly sophisticated delivery systems probably is not possible, Wilson said. But any threat a network is not protected against presents a risk.

CounterStorm was created from technology developed at Columbia University, with funding from the advanced research projects agencies at the Defense and Homeland Security departments. It works by “learning” what is normal on a network alerting administrators to behavior outside of those parameters. One of its primary selling points is the ability of the algorithms that detect the anomalies in near real time.

“That’s the biggest strength of the product,” said TCS Chief Operating Officer Ed Hammersla. “If you’re putting together a system to detect first-time attacks, you’d better be fast.”

The company also claims a false-positive rate of less than 10 percent for the product. False positives — behavior that is falsely flagged or blocked as malicious — is a primary concern with behavior-based tools.

The current version of CounterStorm primarily uses two engines to detect anomalies, the Volumetric Anomaly Detector and the Enhanced Behavioral Engine. The volumetric engine identifies clients or servers producing unusually high levels of network activity and looks for characteristic traits of insider activities and exploited compromised systems. The behavioral engine detects patterns of malicious network activity such as worm-like malware and also provides visibility into attackers targeting specific high-value systems.

The Version 4.0 release in late September will include two new engines. A Statistical Payload Analysis Detection Engine uses deep packet inspection to look at the bytes in network traffic and builds models of normal content. It detects malicious or atypical data traffic that falls outside the norms.

“When you see something that is embedded in the data stream, it will jump out at you,” Wilson said.

A Rogue Detection Engine searches for botnet activity inside a network and for data improperly leaving it by looking for clients communicating with servers that they do not normally access. It also can detect clients that exhibit unauthorized behavior that could indicate they have been compromised.

About the Author

William Jackson is a Maryland-based freelance writer.


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