Biblio

A self-managing software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent “models@runtime” approaches usually require an a priori model for a system’s dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system’s behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. In this article, we demonstrate how such an approach can be realized and effectively used to address a variety of adaptation concerns. In particular, we describe the details of one application of this approach for safely applying dynamic changes to a running software system without creating inconsistencies. We also provide an overview of two other applications of the approach, identifying potentially malicious (abnormal) behavior for self-protection, and improving deployment of software components in a distributed setting for performance self-optimization. Finally, we report on our experiments with engineering self-management features in an emergency deployment system using the proposed mining approach.

Conventional security mechanisms at network, host, and source code levels are no longer sufficient in detecting and responding to increasingly dynamic and sophisticated cyber threats today. Detecting anomalous behavior at the architectural level can help better explain the intent of the threat and strengthen overall system security posture. To that end, we present a framework that mines software component interactions from system execution history and applies a detection algorithm to identify anomalous behavior. The framework uses unsupervised learning at runtime, can perform fast anomaly detection “on the fly”, and can quickly adapt to system load fluctuations and user behavior shifts. Our evaluation of the approach against a real Emergency Deployment System has demonstrated very promising results, showing the framework can effectively detect covert attacks, including insider threats, that may be easily missed by traditional intrusion detection methods. 

Self-protecting software systems are a class of autonomic systems capable of detecting and mitigating security threats at runtime. They are growing in importance, as the stovepipe static methods of securing software systems have been shown to be inadequate for the challenges posed by modern software systems. Self-protection, like other self-* properties, allows the system to adapt to the changing environment through autonomic means without much human intervention, and can thereby be responsive, agile, and cost effective. While existing research has made significant progress towards autonomic and adaptive security, gaps and challenges remain. This article presents a significant extension of our preliminary study in this area. In particular, unlike our preliminary study, here we have followed a systematic literature review process, which has broadened the scope of our study and strengthened the validity of our conclusions. By proposing and applying a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area, we have identified key patterns, trends and challenges in the existing approaches, which reveals a number of opportunities that will shape the focus of future research efforts.

A self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent “models@runtime” approaches usually require an a priori model for a system’s dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system’s behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.

Since conventional software security approaches are often manually developed and statically deployed, they are no longer sufficient against today's sophisticated and evolving cyber security threats. This has motivated the development of self-protecting software that is capable of detecting security threats and mitigating them through runtime adaptation techniques. In this paper, we argue for an architecture-based self- protection (ABSP) approach to address this challenge. In ABSP, detection and mitigation of security threats are informed by an architectural representation of the running system, maintained at runtime. With this approach, it is possible to reason about the impact of a potential security breach on the system, assess the overall security posture of the system, and achieve defense in depth. To illustrate the effectiveness of this approach, we present several architecture adaptation patterns that provide reusable detection and mitigation strategies against well-known web application security threats. Finally, we describe our ongoing work in realizing these patterns on top of Rainbow, an existing architecture-based adaptation framework.