@article {Houmz2021,
	title = {Detecting the impact of software vulnerability on attacks: A case study of network telescope scans},
	journal = {Journal of Network and Computer Applications},
	volume = {195},
	year = {2021},
	note = {cited By 1},
	abstract = {Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85\% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities{\textquoteright} impact on scans. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {Case-studies, Classification algorithm, CVE, Forecasting, Machine learning, Network scan, Network scanning, Network security, Network telescopes, NVD, OR-networks, Scanning, Software vulnerabilities, Telescopes, Times series},
	doi = {10.1016/j.jnca.2021.103230},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116335984\&doi=10.1016\%2fj.jnca.2021.103230\&partnerID=40\&md5=1d7f8d5d4111761b27fc93badc70f925},
	author = {Houmz, A. and Mezzour, G. and Zkik, K. and Ghogho, M. and Benbrahim, H.}
}
@conference {Cherqi2021,
	title = {Leveraging Open Threat Exchange (OTX) to Understand Spatiooral Trends of Cyber Threats: Covid-19 Case Study},
	booktitle = {Proceedings - 2021 IEEE International Conference on Intelligence and Security Informatics, ISI 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Understanding the properties exhibited by Spatialoral evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks{\textquoteright} spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading. {\textcopyright} 2021 IEEE.},
	keywords = {Attack analysis, Case-studies, Computer viruses, COVID-19, Crime, Cybe attack analyse, Cybe threat forecasting, Cyber attacks, Cyber threats, Cyber-attacks, Cyber-crimes, Information analysis, Network security, Spatialoral data analyse for cybe-crime analyse, Threat intelligence, Viruses},
	doi = {10.1109/ISI53945.2021.9624677},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123484404\&doi=10.1109\%2fISI53945.2021.9624677\&partnerID=40\&md5=729113fca1f2d9817bbaa46a6f5d0036},
	author = {Cherqi, O. and Hammouchi, H. and Ghogho, M. and Benbrahim, H.}
}
