Thanh van Do

An analysis of current mobile services and enabling technologies

This paper presents the major technology enablers for mobile services in a comprehensive way and in relation to each other. Mobile services are subject to requirements not only from end-users and mobile network operators but also from wireless application service providers, the content providers, and equipment manufacturers. Each technology enabler is usually aiming at only a subset of the requirements and can be incomplete, inconsistent and overlapping with other technology enablers.

Detecting IMSI-Catcher Using Soft Computing

Lately, from a secure system providing adequate user’s protection of confidentiality and privacy, the mobile communication has been degraded to be a less trustful one due to the revelation of IMSI catchers that enable mobile phone tapping. To fight against these illegal infringements there are a lot of activities aiming at detecting these IMSI catchers. However, so far the existing solutions are only device-based and intended for the users in their self-protection. This paper presents an innovative network-based IMSI catcher solution that makes use of machine learning techniques. After giving a brief description of the IMSI catcher the paper identifies the attributes of the IMSI catcher anomaly. The challenges that the proposed system has to surmount are also explained. Last but least, the overall architecture of the proposed Machine Learning based IMSI catcher Detection system is described thoroughly.

Threat assessment model for mobile malware

Today the smartphone is definitely the most popular and used device, surpassing by far the laptop. Unfortunately, its popularity makes it also the most targeted goal for malicious attacks. On the other hand mobile security is still in its infancy and improvements are needed to provide adequate protection to the users. This paper contributes with threat assessment model which enables a systematic analysis and evaluation of mobile malware. The same model can also be used to evaluate the effectiveness of security protection measures.

A big data analytics approach to combat telecommunication vulnerabilities

Both the telecommunication networks and the mobile communication networks are using the Signaling System No. 7 (SS7) as the nervous system. It allows mobile users to communicate using SMS and phone calls, manage billing for operators and much more. Primarily, it is a set of protocols that allows telecommunication network elements to communicate, collaborate and deliver services to its users. Deregulation and migration to IP have made SS7 vulnerable to serious attacks such as location tracking of subscribers, interception of calls and SMS, fraud, and denial of services. Unfortunately, current protection measures such as firewalls, filters, and blacklists are not able to provide adequate protection of SS7. In this paper, a method for detection of SS7 attacks using big data analytics and machine learning is proposed. The paper clarifies the vulnerabilities of SS7 networks and explains how the proposed techniques can help improve SS7 security. A proof-of-concept SS7 protection system based on big data techniques and machine learning is also described thoroughly.

A disruption analysis of mobile communication services using business ecosystem concept

Todays businesses are no longer arranged as a value chain but rather as a complex network of entities with intertwined relations. To identify disruptions it is hence more efficient to consider businesses as ecosystems. The mobile communication ecosystem which was until now quite healthy finds itself now under attacks by players from the internet. Like other disruptive technologies, IP communication services are still inferior to mobile communication services with respect to trust and security but introduce a new set of performance attributes such as flexibility of combination, combination with video and free-of-charge. As their qualities are improving they are becoming more and more menacing. The mobile network operators (MNOs) are left with the only option of embracing IP communication services and integrating with their mobile service to produce best services.

Detection of DNS Tunneling in Mobile Networks Using Machine Learning

Lately, costly and threatening DNS tunnels on the mobile networks bypassing the mobile operator’s Policy and Charging Enforcement Function (PCEF), has shown the vulnerability of the mobile networks caused by the Domain Name System (DNS) which calls for protection solutions. Unfortunately there is currently no really adequate solution. This paper proposes to use machine learning techniques in the detection and mitigation of a DNS tunneling in mobile networks. Two machine learning techniques, namely One Class Support Vector Machine (OCSVM) and K-Means are experimented and the results prove that machine learning techniques could yield quite efficient detection solutions. The paper starts with a comprehensive introduction to DNS tunneling in mobile networks. Next the challenges in DNS tunneling detections are reviewed. The main part of the paper is the description of proposed DNS tunneling detection using machine learning.

Better Protection of SS7 Networks with Machine Learning

Deregulation and migration to IP have made SS7 vulnerable to serious attacks such as location tracking of subscribers, interception of calls and SMS, fraud, and denial of services. Unfortunately, current protection measures such as firewalls, filters, and blacklists are not able to provide adequate protection of SS7. In this paper, a method for detection of SS7 attacks using machine learning is proposed. The paper clarifies the vulnerabilities of SS7 networks and explains how machine learning techniques can help improve SS7 security. A proof- of- concept SS7 protection system using machine learning is also described thoroughly.

Strengthening Mobile Network Security Using Machine Learning

Lately, several episodes of tapping and tracking of mobile phones in Europe including Norway have been revealed, showing the vulnerabilities of both the mobile network and mobile phones. A better protection of the user’s confidentiality and privacy is urgently required. This paper will present an innovative mobile network security system using machine learning. The paper will start with a vulnerability and threat analysis of the evolving mobile network, which is a fusion of mobile wireless technologies and Internet technologies, complemented with the Internet of Things. The main part of the paper will concentrate on clarifying how machine learning can help improving mobile network security. The focus will be on elucidating what makes machine learning superior to other techniques. A special case study on the detection of IMSI Catcher, the fake base station that is used in mobile phone tracking and tapping, will be explained.

Better User Protection with Mobile Identity

So far mobile network users are enjoying better protection than Internet users thanks to the SIM card, a tampered resistant module providing strong authentication. To offer better protection to Internet users this paper proposes a solution called Universal Mobile Identity, which extends the usage of the current mobile identity to the access of Internet services. For signing on from mobile phone, tablet, laptop, etc. the SIM card is used to provide strong and user-friendly authentication. The architecture of Universal Mobile Identity is thoroughly described and illustrated by use cases.

Identity Federation for Cellular Internet of Things

Although the vision of 5G is to accommodate billions IoT devices and applications, its success depends very much on its ability to provide enhanced and affordable security. This paper introduces an Identity Federation solution which reuses the SIM authentication for cellular IoT devices enabling single-sign-on. The proposed solution alleviates the IoT providers burden of device identity management at the same time as the operational costs are reduced considerably. The proposed solution is realized by open source software for LTE, identity management and IoT.