Image: North Carolina State University
Researchers from North
Carolina State University have developed a way to
measure how badly a Wi-Fi network would be disrupted by different types of
attacks—a valuable tool for developing new security technologies.
“This information can be used to help us design more effective security
systems, because it tells us which attacks—and which circumstances—are most
harmful to Wi-Fi systems,” says Wenye Wang, an assistant professor of
electrical and computer engineering at NC State and coauthor of a paper
describing the research.
Wi-Fi networks, which allow computer users to access the Internet via radio
signals, are commonplace—found everywhere from offices to coffee shops. And,
increasingly, Wi-Fi networks are important channels for business communication.
As a result, attacks that jam Wi-Fi networks, blocking user access, are not
only inconvenient but have significant economic consequences.
Wang and her team examined two generic Wi-Fi attack models. One model
represented persistent attacks, where the attack continues non-stop until it
can be identified and disabled. The second model represented an intermittent
attack, which blocks access on a periodic basis, making it harder to identify
and stop. The researchers compared how these attack strategies performed under
varying conditions, such as with different numbers of users.
After assessing the performance of the models, the researchers created a
metric called an “order gain” to measure the impact of the attack strategies in
various scenarios. Order gain compares the probability of an attacker having
access to the Wi-Fi network to the probability of a legitimate user having
access to the network. For example, if an attacker has an 80% chance of
accessing the network, and other users have the other 20%, the order gain would
be 4—because the attackers odds of having access are 4 to 1.
This metric is important because a Wi-Fi network can only serve once
computer at a time, and normally functions by rapidly cycling through multiple
requests. Attacks work by giving the attacker greater access to the network,
which effectively blocks other users.
“If we want to design effective countermeasures,” Wang says, “we have to
target the attacks that can cause the most disruption. It’s impossible to
prevent every conceivable attack.” So, one suggestion the researchers have is
for countermeasures to focus on continuous attacks that target networks with
large numbers of users—because that scenario has the largest order gain.
Beyond that, network security professionals can use the new approach to assess
a complicated range of potential impacts that vary according to type of attack
and number of users.