Active Monitoring for Control Systems under Anticipatory Semantics

Zhao, Changzhi; Dong, Wei; Qi, Zhichang

doi:10.1109/qsic.2010.82

Cited by 5 publications

(5 citation statements)

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“…Different from the controlling and steering problem of hardware, for software, we must have the ability to predict the occurrence of the failure in advance before it has occurred and enforce the corresponding control actions in time to prohibit them. For this purpose, software active monitoring (SAM) is proposed [4]. In SAM, a labeled transition system model which represents all the possible behavior of the system, is imported.…”

Section: On-line Security Goals Assurancementioning

confidence: 99%

“…Thus, in order to eliminate undesirable security control while assure the implementation of security goals, dynamic control can be used as an alternate way. In this paper, we present how to use software active monitoring (SAM) [4] to control dynamically the security relevant behavior of system further to assure the implementation of security goals. SAM is proposed by us to improve the traditional runtime verification (RV).…”

Section: Introductionmentioning

confidence: 99%

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Security Goals Assurance Based on Software Active Monitoring

Zhao

Dong

Leucker

et al. 2011

2011 Fifth International Conference on Secure Software Integration and Reliability Improvement

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Access control is a vital security mechanism in today's operating systems, and the security policies dictating the security relevant behaviors is lengthy and complex, for example in Security-Enhanced Linux (SELinux). It is extremely difficult to verify the consistency between the security policies and the security goals desired by applications. In this paper, we present how to predict whether the information flow security goal is violated or not during runtime, how to generate the corresponding control actions on-line when divergence is detected and how to apply these actions in time based on software active monitoring technique. The symbolic security information flow model of SElinux is generated from a formalization of the access control mechanism which can be used to generate the N-step ahead projection of the future behavior. Information flow security goals are expressed in linear temporal logic (LTL) which provides clear description of the objectives desired by applications. Anticipatory monitor is generated from LTL formula automatically. We consider an on-line scheme where after the occurrence of an event, the next control action is determined on the basis of the N-step ahead projection of the future behavior. This procedure is repeated after the occurrence of next security relevant event. Thus, a closed-loop system is generated that all behavior sequences will satisfy the security goals.

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Section: On-line Security Goals Assurancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Security Goals Assurance Based on Software Active Monitoring

Zhao

Dong

Leucker

et al. 2011

2011 Fifth International Conference on Secure Software Integration and Reliability Improvement

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“…Obviously, monitoring of software, while it is in operation, is an important topic of research. Extending the notion of runtime monitors for runtime verification enables the designer to explore possible fault states of the software in advance (see [31,8]). The dynamic monitoring of highly reliable and redundant software poses its own challenges (e.g., [12]).…”

Section: Iswhm Goalsmentioning

confidence: 99%

Towards software health management with bayesian networks

Schumann

Mengshoel

Srivastava

et al. 2010

Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research

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More and more systems (e.g., aircraft, machinery, cars) rely heavily on software, which performs safety-critical operations. Assuring software safety though traditional V&V has become a tremendous, if not impossible task, given the growing size and complexity of the software.We propose that iSWHM (Integrated SoftWare Health Management) can increase safety and reliability of highassurance software systems. iSWHM uses advanced techniques from the area of system health management in order to continuously monitor the behavior of the software during operation, quickly detect anomalies and perform automatic and reliable root-cause analysis, while not replacing traditional V&V. Information provided by the iSWHM system can be used for automatic mitigation mechanisms (e.g., recovery, dynamic reconfiguration) or presented to a human operator. iSWHM's prognostic capabilities will further improve reliability and availability as it provides information about soon-to-occur failures or looming performance bottlenecks. In this paper, we will discuss challenges and future potential and describe how Bayesian networks (BN) could be used for iSWHM modeling.

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“…To avoid the occurrence of latent faults, we need an active approach, such that non-conformance can be foreseen by looking ahead, and steering actions can be generated to keep the system away from upcoming executions that could cause these faults. We call such a process, active monitoring [6]. When the monitor collects information from the running system and generates steering actions to control the execution of the system, the system and monitor make up a control loop.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we presented a systematic method of constructing such monitors [9]. Based on these results, we proposed the concept of active monitoring in [6], and briefly described its vision in [10].…”

Section: Introductionmentioning

confidence: 99%