@article {Iraqi2022,
	title = {Communizer: A collaborative cloud-based self-protecting software communities framework - Focus on the alert coordination system},
	journal = {Computers and Security},
	volume = {117},
	year = {2022},
	note = {cited By 0},
	abstract = {Popular software has always been appealing to adversaries, as related vulnerabilities are synonymous with millions of exposed businesses. Collaborative intrusion detection, as well as software self-protection, try to alleviate this situation. However, they lack either autonomy and adaptation, or Internet-scale oversight and mitigation. In this work, we present Communizer: a collaborative cloud-based framework that creates communities of self-protecting software across organizations. It allows community members to turn their common weaknesses into collaborative and proactive self-protection, empowering them to detect intrusions, exchange alerts, and anticipate attacks. We start by integrating multiple autonomic MAPE-K loops through cloud-based coordination, and a novel hierarchical, regional coordination pattern (HRCP), optimizing scalability, resiliency, accuracy and privacy. Then, we design a trust-based multi-level alert coordination system (TMACS), as well as a lightweight alert coordination message exchange format (ACMEF). At its core, TMACS aggregates, validates, and shares security alerts among community members while fostering agreement and managing trust. It also addresses insider attacks by detecting and blacklisting rogue members. Moreover, TMACS identifies and neutralizes selfish members through a specifically designed probabilistic model. The analysis, optimization, and evaluation of TMACS show a good trade-off between the precision and recall of untrustworthy and selfish members detection. More importantly, we demonstrate a drastic reduction of monitoring loads on community members while ensuring a high collaborative attack detection and anticipation rate, even for small-scope attacks. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {Autonomic Computing, collaboration, Coordination systems, Economic and social effects, Intrusion detection, Mape, MAPE-K, Self protecting, Self-protecting software, Selfishness, Software community, Trust, Trusted computing},
	doi = {10.1016/j.cose.2022.102692},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127747467\&doi=10.1016\%2fj.cose.2022.102692\&partnerID=40\&md5=246bfc07692396ec6709a73d31126fb6},
	author = {Iraqi, O. and Bakkali, H.E.}
}
@article {Kamri2020298,
	title = {Machine Learning Approach for Smart Self-diagnosing Autonomic Computing Systems},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {298-307},
	abstract = {While modern systems and networks are continuously growing in size, complexity and diversity, the monitoring and diagnosing of such systems is becoming a real challenge. Technically and economically, more automation of the classical diagnosing tasks is needed. This has triggered a considerable research initiative, grouped under the terms self-management and Autonomic Computing. In this paper we propose a new model for smart self-diagnosing systems based on Autonomic Computing principles and Machine Learning techniques. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Autonomic Computing, Autonomic computing system, Diagnosing system, Intelligent systems, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, planning, Research initiatives, Self management, Self-diagnosing, Sustainable development, wireless networks},
	doi = {10.1007/978-3-030-36674-2_31},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080896883\&doi=10.1007\%2f978-3-030-36674-2_31\&partnerID=40\&md5=3dce8bc657338047166fb3ded16c10c8},
	author = {Kamri, H. and Bounabat, B.}
}
