Dear all, A postdoctoral position on Cybersecurity/IA is available starting January 2021 or later. Initially, the position is for one year, but it can be extended for an additional year by mutual consent. Applicants for the post doc position are invited to send a letter of interest and CV, as well as at least one name of reference for recommendations, by email to: - Albert Bifet: albert.bifet@telecom-paris.fr - Mounira Msahli: mounira.msahli@telecom-paris.fr Post-Doc: Fraud Detection Keywords: Machine Learning Scam detection Security 1 General Context Telephony is now merging several security challenges. Most telephony services can be monetized that is why it can attracts scammers. Basically, all actors in telephony ecosystem can be target of security attacks. In many cases, there are no clear law about fraud and becomes difficult to address. Fraud loss was estimated by operators as 12 billion dollars based on Cyber Telecom Crime Report 2019. We believe that the real losses were more important and estimated to 3% and 10% of total revenue. This post-doc discusses all possible telephony frauds that can target billing services of mobile network and to detect malicious behavior. We could mention, for example, the Phishing scams or vishing. This fraud can also be divided into several categories, like Wangiri scams[MS19], Voicemail scams and Blackmail/ Ransom or the International Revenue Sharing Fraud [Sah+17]. It is a question of detecting abnormal behavior in network. We can start by analysing and modelling two frauds: • "Smishing" Fraud: and Vishing are age-old scams, they remain prevalent and effective; especially, with mobile devices and users. The techniques employed by the scammers may vary, end-users are lured into disclosing or revealing key and sensitive information on the Internet and also over the phone [Eze09]. • "Wangiri" Fraud: is a Japanese word meaning ‘one ring and drop’. It relies on one single ring method for a quick way to make money. Missed calls from unknown callers entice subscribers to call back unknowingly premium numbers where they are deceived to stay on the line for as long as possible in order to inflate their bill[Mai19]. 2 Objective The idea of this research is to use Machine Learning to detect fraud in telephony network. In literature, there are many studies using supervised algorithms considering measurements such as true positive rate and accuracy at a chosen threshold such as the number of correct predicted instances divided by total number of instances. In fraud detection, the cost of false positive and false negative error can differ from case to case, and can change over time. In fraud detection, a false negative error is usually more costly than a false positive error. The major issue of this work is to find the suitable machine learning algorithms to detect frauds. The work in this post-doc contributes to provide novel security architecture based on Machine Learning algorithms for telephony network. 3 Background of the candidate We are looking for a candidate with a PhD in Computer Sciences with very good background in cybersecurity in mobile networks. A background in Machine learning is essential. She or He must have a good knowledge of frauds and attacks in such networks. Knowledge in performance evaluation, optimization, and modeling will be greatly appreciated as well as programming and simulation skills. 4 Contacts - Mounira Msahli: mounira.msahli@telecom-paris.fr - Albert Bifet: albert.bifet@telecom-paris.fr 5 References [Eze09] Priscilla Mateko Amanor Ezer Osei Yeboah-Boateng. “Phishing, SMiShing Vishing: An Assessment of Threats against Mobile Devices”. In: Journal of Emerging Trends in Computing and Information Sciences. 2009. [Mai19] George Sammour Mais Arafat Abdallah Qusef. “Detection of Wangiri Telecommunication Fraud Using Ensemble Learning”. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). 2019. [MS19] Abdallah Qusef Mais Arafat and George Sammour. “Detection of Wangiri Telecommunication Fraud Using Ensemble Learning”. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). 2019. [Sah+17] Merve Sahin et al. “SoK: Fraud in Telephony Networks”. In: 2017 IEEE European Symposium on Security and Privacy EuroSP. 2017. [Pra+20] Sathvik Prasad et al. “Who’s Calling? Characterizing Robocalls through Audio and Metadata Analysis”. In: 29th USENIX Security Sympo- sium. https://www.usenix.org/conference/usenixsecurity20/ presentation/prasad. 2020 Kind Regards Mounira MSAHLI