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Web­sites like Face­book, LinkedIn and other social-​​media net­works con­tain mas­sive amounts of valu­able public infor­ma­tion. Auto­mated web tools called web crawlers sift through these sites, pulling out infor­ma­tion on mil­lions of people in order to tailor search results and create tar­geted ads or other mar­ketable content.

But what hap­pens when "the bad guys" employ web crawlers? For Engin Kirda, Sy and Laurie Stern­berg Inter­dis­ci­pli­nary Asso­ciate Pro­fessor for Infor­ma­tion Assur­ance in the Col­lege of Com­puter and Infor­ma­tion Sci­ence and the Depart­ment of Elec­trical and Com­puter Engi­neering, they then become tools for spam­ming, phishing or tar­geted Internet attacks.

"You want to pro­tect the infor­ma­tion," Kirda said. "You want people to be able to use it, but you don't want people to be able to auto­mat­i­cally down­load con­tent and abuse it."

Kirda and his col­leagues at the Uni­ver­sity of California–Santa Bar­bara have devel­oped a new soft­ware call Pub­Crawl to solve this problem. Pub­Crawl both detects and con­tains mali­cious web crawlers without lim­iting normal browsing capac­i­ties. The team joined forces with one of the major social-​​networking sites to test Pub­Crawl, which is now being used in the field to pro­tect users' information.

Kirda and his col­lab­o­ra­tors pre­sented a paper on their novel approach at the 21st USENIX Secu­rity Sym­po­sium in early August. The article will be pub­lished in the pro­ceed­ings of the con­fer­ence this fall.

In the cyber­se­cu­rity arms race, Kirda explained, mali­cious web crawlers have become increas­ingly sophis­ti­cated in response to stronger pro­tec­tion strate­gies. In par­tic­ular, they have become more coor­di­nated: Instead of uti­lizing a single com­puter or IP address to crawl the web for valu­able infor­ma­tion, efforts are dis­trib­uted across thou­sands of machines.

"That becomes a tougher problem to solve because it looks sim­ilar to benign user traffic," Kirda said. "It's not as straightforward."

Tra­di­tional pro­tec­tion mech­a­nisms, like a CAPTCHA, which oper­ates on an indi­vidual basis, are still useful, but their deploy­ment comes at a cost: Users may be annoyed if too many CAPTCHAs are shown. As an alter­na­tive, non­in­tru­sive approach, Pub­Crawl was specif­i­cally designed with dis­trib­uted crawling in mind. By iden­ti­fying IP addresses with sim­ilar behavior pat­terns, such as con­necting at sim­ilar inter­vals and fre­quen­cies, Pub­Crawl detects what it expects to be dis­trib­uted web-​​crawling activity.

Once a crawler is detected, the ques­tion is whether it is mali­cious or benign. "You don't want to block it com­pletely until you know for sure it is mali­cious," Kirda explained. "Instead, Pub­Crawl essen­tially keeps an eye on it."

Poten­tially mali­cious con­nec­tions can be rate-​​limited and a human oper­ator can take a closer look. If the oper­a­tors decide that the activity is mali­cious, IPs can also be blocked.

In order to eval­uate the approach, Kirda and his col­leagues used it to scan logs from a large-​​scale social net­work, which then pro­vided feed­back on its suc­cess. Then, the social net­work deployed it in real time, for a more robust eval­u­a­tion. Cur­rently, the social net­work is using the tool as a part of its pro­duc­tion system. Going for­ward, the team expects to iden­tify areas where the soft­ware could be evaded and make it even stronger.