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In the last 20 years mobile devices gained an important role in daily life and became must have items for everyone. As mobile devices give us the much needed flexibility and mobility, they also represent one major concern; security. As the information is transmitted from node to node via radio frequencies, an imposter can gain access into a mobile network without the need to gain physical access to firewalls and gateways. Also, as they are light and small, mobile devices are easily lost and often used without any PIN or password protection enabled. Hence, it is not difficult for someone even without any technical knowledge to gain access to such devices if they have been left behind or stolen. As traditional intrusion detection systems are not very effective against this kind of attack, there is a need of a different approach that can assist in the identification of a potential imposter. This thesis begins by assessing the security needs of the mobile devices, and establishes the perceived inadequacy of existing safeguards in this respect. Therefore this research considers using Behaviour-Based Mobile Intrusion Detection System (BeMIDS) that aims to assist the identification of anomalous user activity. This in return presents the two main characteristics needed to classify a legitimate user inside a mobile network: first with whom, when and what type of connection is established and then at where the mobile device is left open. After this the research proposes a novel approach that investigates the application of three machine learning algorithms to profile user behaviour in mobile networks. In BeMIDS, historical user profiles are created and then compared with the real-time ones in order to detect unusual activity in mobile networks. If a user’s behaviour changes, this results in alerting the system as an anomalous activity. Specific examples of behaviours that BeMIDS appears to be particularly sensitive to include duration (of calls and of connection with cell towers), time of day (calls are made and cell towers are connected), and frequency of caller usage. In order to classify a legitimate user over a mobile network the thesis then validates this approach by implementing C4.5, RIPPER and SOM algorithms over MIT’s Reality Mining Dataset. The results support the proposed architecture and present accuracy rate as high as 96% for call logs and 94% for tower logs under training conditions.

How to reduce handoff drop and increase seamless handoff is a very important issue of Quality-of-Service (QoS) in cellular. Although the Cellular IP which separates local and wide area mobility can significantly improves the performance of existing mobile host protocols (e.g. Mobile IP), especially when mobile hosts migrate frequently, it does not have the ability to support QoS control and needs to emit beacon periodically. Hence, we propose schemes of the admission control, the bandwidth reservation for handoffs, the retransmitting data and the estimated quantity for the useful Request Message to improve the control function of QoS in the Cellular IP. Also we propose another paging way in the Cellular IP, which is, the fast find path algorithm and optimal cross point node. The admission control can limit the quantity of connection in whole networks. The bandwidth reservation particularly used for handoff can reduce the rate of handoff drops. The estimated quantity for the useful "Request Message" ignores the overload with a large number of request messages. The fast find path algorithm and optimal cross point node only apply in the first find eligible routing path. It can reduce the paging time of mobile host and let the mobile host attach fast.

Humanity, since its inception, has been interested in the materialization of knowledge. Various ancient cultures generated a lot of information through their writing systems. The beginning of the increase of information could date back to 1880 when a census performed in the United States of America took 8 years to be tabulated. In the 1930s the demographic growth exacerbated this increase of data. Already in 1940, libraries had collected a large amount of writing and it is in this decade when scientists begin to use the term “information explosion”. The term first appears in the Lawton (Oklahoma) Constitution newspaper in 1941. Currently, it can be said that we live in the age of big data. Exabytes of data are generated every day; therefore, the term big data has become one of the most important concepts in information systems. Big data refer to large amounts of data on a large scale that exceeds the capacity of conventional software to be captured, processed, and stored in a reasonable time. As a general criterion, most experts consider big data to be the largest volume of data, the variety of formats and sources from which it comes, the immense speed at which it is generated, the veracity of its content, and the value of the information extracted/processed. Faced with this reality, several questions arise: How to manipulate this large amount of data? How to obtain important results to gain knowledge from this data? Therefore, the need to create a connecting bridge between big data and wisdom is evident. People, machines, applications, and other elements that make up a complex and constantly evolving ecosystem are involved in this process. Each project presents different peculiarities in the development of an framework based on big data. This, in turn, makes the landscape more complex for the designer since multiple options can be selected for the same purpose. In this work, we focus on an framework for processing mobile network performance management data. In mobile networks, one of the fundamental areas is planning and optimization. This area analyzes the key performance indicators to evaluate the behavior of the network. These indicators are calculated from the raw data sent by the different network elements. The network administration teams, which receive these raw data and process them, use systems that are no longer adequate enough due to the great growth of networks and the emergence of new technologies such as 5G and 6G that also include equipment from the Internet of things. For the aforementioned reasons, we propose in this work a big data framework for processing mobile network performance management data. We have tested our proposal using performance files from real networks. All the processing carried out on the raw data with XML format is detailed and the solution is evaluated in the ingestion and reporting components. This study can help telecommunications vendors to have a reference big data framework to face the current and future challenges in the performance management in mobile networks. For instance, to reduce the processing time data for decisions in many of the activities involved in the daily operation and future network planning.

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry.

Student Number : 0006936H - MSc research report - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Code division duplexing (CDD) has steadily garnered attention in the telecommunication community. In this project report we propose a physical layer implementation of CDD that utilizes orthogonal Gold codes as the means of differentiating transmission directions, in order to implement an ad-hoc networking infrastructure that is overlaid on a standard mobile networking topology, and hence creating a hybrid networking topology. The performance of the CDD based system is then comparatively assessed in two ways: from the perspective of the physical layer using point-to-point simulations and from the perspective of the network layer using an iterative snapshot based simulation where node elements are able to setup connections based on predefined rules.

Les terminaux mobiles (smartphones et tablettes) sont devenus les terminaux les plus populaires pour accéder à Internet. Cependant, l'écosystème incluant les terminaux mobiles est maintenu opaque à cause des intérêts financiers des différents acteurs : les concepteurs des systèmes d'exploitation et des applications, les opérateurs des "stores", et les FAI. Cette opacité est renforcée par le peu de contrôle qu'ont les utilisateurs sur les informations échangées par leur terminal. Pour résoudre ce problème d'opacité et de manque de contrôle, on a créé une plate-forme, Meddle, qui utilise la redirection de trafic des terminaux mobiles pour analyser et modifier ce trafic. Contrairement aux solutions qui nécessitent d'être implémentées sur le terminal, Meddle combine les techniques de VPN et de "middlebox" pour offrir une solution indépendante de l'OS, du FAI et de l'accès radio. On a utilisé Meddle pour des expérimentations contrôlées et pour une étude utilisateurs approvée par un IRB. On a observé que des applications populaires sous iOS et Android exposaient des informations personnelles dans le traffic réseau en clair et chiffré. On a ensuite exploité Meddle pour prévenir ces fuites d'informations privées. On a également utilisé Meddle pour étudier les caractéristiques réseaux du trafic vidéo sur Internet. On a trouvé que ce trafic dépend fortement du type de terminal, de l'application utilisée pour regarder la vidéo (application native ou navigateur Web) et du contenant (HTML5, Flash, Silverlight). Ce résultat montre qu'un changement dans le terminal, l'application ou le contenant peut avoir un impact important sur le réseau. / Mobile devices are increasingly becoming the primary device to access the Internet. Despite this thriving popularity, the current mobile ecosystem is largely opaque because of the vested monetary interests of its key players: mobile OS providers, creators of mobile applications, stores for mobile applications and media content, and ISPs. This problem of opaqueness is further aggravated by the limited control end-users have over the information exchanged by their mobile devices. To address this problem of opaqueness and lack of control, we create a user-centric platform, Meddle, that uses traffic indirection to diagnose mobile devices. Compared to an on-device solution, Meddle uses two well-known technologies, VPNs and middleboxes, and combines them to provide an OS, ISP, and access technology agnositic solution. We use Meddle for controlled experiments and an IRB approved study, and ob- served that popular iOS and Android applications leak personally identifiable information in the clear and also over SSL. We then use Meddle to prevent further leaks. We also use our platform to detail the network characteristics of video streaming services, the most popular Web-service in the current Internet. We observe that the network traffic characteristics vary vastly with the device (mobile or desktop), application (native applications and also between individual desktop browsers), and container (HTML5, Flash, and Silverlight). This observation is important because the increased adoption of one application or streaming service could have a significant impact on the network traffic, for example, an increase in the usage of mobile applications.

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc.

Mobile network equipments are widely popularized and advanced mobile communication services are provided increasingly such that ubiquitous computing environments will come true soon. It is a pleasure for mobile users to work or get recreations in the mobile network environments. However, just as the cases in wireline environments, there are a lot of security threats to mobile network systems and their impact on the security is more serious than that in wireline environments owing to the feature of wireless transmissions and the ubiquity property in mobile network systems. The secret personal information, important data, or classified missives which mobile users carry may be stolen by malicious entities. In order to guarantee the quality of the advanced communication services, the security and privacy would be the important issues when mobile users roam to the mobile networks. In this thesis, an anonymous authentication protocol will be proposed to protect both the security of the mobile network system and the privacy of mobile users. Not only does the proposed scheme provide mutual authentication between each user and the system, but also the identity of each user can be kept secret against anyone else including the system. Although the users are anonymously authenticated by the system, it can still make correct bills to charge these anonymous users. Finally, our protocol also achieves the goal of fair privacy which allows the judge to be able to revoke the anonymity and trace the illegal users when they misused the anonymity property such as they committed crimes.

In this paper, in order to reduce the location update cost in wireless mobile networks, we propose a cooperative location update scheme. The proposed scheme first discovers the statistical relation between mobile stations according to their history of location update and paging. In order to reduce the total cost of mobility management, we propose integrating the cooperative location update scheme with the concurrent search scheme. We use analytical results and simulation results to justify the usage of the proposed approach.