Autoimmune Computer Systems
For half a century, developers have protected their systems by coding rules that identify and block specific events. Edit rules look for corrupted data, firewalls enforce hard-coded permissions, virus definitions guard against known infections, and intrusion-detection systems look for activities deemed in advance to be suspicious by systems administrators.
But that approach will increasingly be supplemented by one in which systems become their own security experts, adapting to threats as they unfold and staying one step ahead of the action. A number of research projects are headed in that direction.
At the University of New Mexico, computer science professor Stephanie Forrest is developing intrusion-detection methods that mimic biological immune systems. Our bodies can detect and defend themselves against foreign invaders such as bacteria and parasites, even if the invaders haven't been seen before. Forrest's prototypes do the same thing.
Her host-based intrusion-detection system builds a model of what is normal by looking at short sequences of calls by the operating system kernel over time. The system learns to spot deviations from the norm, such as those that might be caused by a Trojan horse program or a buffer-overflow attack. When suspicious behavior is spotted, the system can take evasive action or issue alerts.
The central challenge with computer security is determining the difference between normal activity and potentially harmful activity. The common solution is to identify the threat and protect against it, but in many ways, this is the same as constantly fighting the last war, and it can be quite inefficient in environments that are rapidly changing.
In another project Forrest and her students are developing intrusion-detection systems even more directly modeled on how the immune system works. The body continuously produces immune cells with random variations. As the cells mature,the ones that match the body's own proteins are eliminated, leaving only those that represent deviations as guides to what the body should protect against. Likewise, Forrest's software randomly generates “detectors”， throws away those that match normal behavior and retains those that represent abnormal behavior.
Each machine in the network generates its own detectors based on that machine's unique behavior and experiences, and the detectors work with no central coordination or control. In fact, just how the detectors work isn't precisely known, Forrest says.
Indeed, these experimental approaches don't work perfectly, Forrest acknowledges, but she points out that no security measure, including encryption or authentication, works perfectly either. She says the most secure systems will employ multiple layers of protection, just as the human body does. The advantage of this type of system is that it is largely self-maintaining and doesn't require continual updating by experts.
在（美国）新墨西哥大学，计算机科学教授 Stephanie Forrest正在开发模仿生物免疫系统的入侵检测系统。我们的身体能探测和自我防御外来入侵者，如细菌和寄生虫，甚至在以前根本没有看到过它们。Forrest的样机做同样的事。
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