Fujitsu Strengthens Cyber-Security with AI Technology to Protect Against Deception Attacks

With the use of AI technologies progressing in various fields in recent years, the risk of attacks that intentionally interfere with AI's ability to make correct judgements represents a source of growing concern. Many suitable conventional security resistance enhancement technologies exist for media data like images and sound. Their application to sequential data such as communication logs and service usage history remains insufficient, however, because of the challenges posed by preparing simulated attack data and the loss of accuracy.

To overcome these challenges, Fujitsu has developed a robustness enhancement technology for AI models applicable to sequential data. This technology automatically generates a large amount of data simulating an attack and combines it with the original training data set to improve resistance to potential deception attacks while maintaining the accuracy of judgment.

By applying this technology to an AI model developed by Fujitsu to judge the necessity of countermeasures against cyber-attacks, it was confirmed that misjudgment of about 88% can be prevented in our own attack test data.

Newly Developed Technology

Fujitsu has developed a technology that can automatically generate simulated attack data for training, which can be applied to AI models that analyze sequential data and enable training with less deterioration in the accuracy of attack detection. The features of the developed technology are as follows:

1. Automatic generation of simulated attack data

When creating simulated attack data, we first prepare the original attack data as a base and the data used for impersonation. In the case of cyber-attacks, the attacker wants to disguise malicious operations as benign operations, so the base data is the communication log data of the malicious operation, and the data used for the disguise is the communication log data of the benign operation. Next, the communication log data of benign operations used for the impersonation is analyzed by the AI model before the countermeasure, and the data with the impersonation effect which is easy to be judged as the benign operation is extracted referring to the result. This extracted data is combined with the communication log data of the base malicious operation, and it is generated as simulated attack data. Since the communication log data of the base malicious operation remains unchanged, a large amount of simulated attack data can be generated automatically without losing its original characteristics.

2. Ensemble adversarial training techniques

Using the original learning data set and the simulated attack data set generated with the new technique described above, two kinds of AI models are constructed: an AI model which works accurately for the original learning data and an AI model which works accurately for deception attack data and the decision results of the two AI models are integrated by ensemble learning using features indicative of possible deception attack data. In the case a cyber-attack is detected, it becomes possible to use ensemble learning to automatically and appropriately train AI models to decide which AI model's decision should be strongly reflected, since the number of log rows of communication log data and the number of duplicate log rows are used as indicative features. As a result, this technique increases the robustness to deception attacks while keeping loss of accuracy in the original data under control.

Effect

By using the newly developed technology, it is possible to strengthen the resistance of the AI model against deception attacks against sequential data. To demonstrate its effectiveness, Fujitsu applied the newly developed techniques to an AI model that determines the necessity of responding to cyber-attacks.

As a result, it was confirmed that the accuracy of judgment against simulated attack test data could be improved to about 88% without lowering the accuracy of judgment against the original test data. In addition, when analyzing the test data of the deception attack that went undetected, it was discovered that it was possible to deal with a simple rule such as judging a combination of specific operations as a deception attack, which made it possible to prevent virtually all deception attacks.

Future plans

In the future, Fujitsu plans to expand the application of this technology not only to counter cyber-attacks, but also to a variety of other areas to improve the security of systems using AI. In fiscal 2021, Fujitsu additionally aims to commercialize these techniques as part of a security enhancement technology offering.

Press release by Fujitsu