Biblio

Big data of mobile network privacy is vulnerable to clear text attack in the process of storage and mixed network information sharing, which leads to information leakage. Through the mixed encryption of data of mobile network privacy big data to improve the confidentiality and security of mobile network privacy big data, a mobile network privacy big data hybrid encryption algorithm based on hyperchaos theory is proposed. The hybrid encryption key of mobile network privacy big data is constructed by using hyperchaotic nonlinear mapping hybrid encryption technology. Combined with the feature distribution of mobile network privacy big data, the mixed encrypted public key is designed by using Logistic hyperchaotic arrangement method, and a hyperchaotic analytic cipher and block cipher are constructed by using Rossle chaotic mapping. The random piecewise linear combination method is used to design the coding and key of mobile network privacy big data. According to the two-dimensional coding characteristics of mobile network privacy big data in the key authorization protocol, the hybrid encryption and decryption key of mobile network privacy big data is designed, and the mixed encryption and decryption key of mobile network privacy big data is constructed, Realize the privacy of mobile network big data mixed encryption output and key design. The simulation results show that this method has good confidentiality and strong steganography performance, which improves the anti-attack ability of big data, which is used to encrypt the privacy of mobile network.

While enjoying the convenience brought by mobile phones, users have been exposed to high risk of private information leakage. It is known that many applications on mobile devices read private data and send them to remote servers. However how, when and in what scale the private data are leaked are not investigated systematically in the real-world scenario. In this paper, a framework is proposed to analyze the usage data from mobile devices and the traffic data from the mobile network and make a comprehensive privacy leakage detection and privacy inference mining on a large scale of realworld mobile data. Firstly, this paper sets up a training dataset and trains a privacy detection model on mobile traffic data. Then classical machine learning tools are used to discover private usage patterns. Based on our experiments and data analysis, it is found that i) a large number of private information is transmitted in plaintext, and even passwords are transmitted in plaintext by some applications, ii) more privacy types are leaked in Android than iOS, while GPS location is the most leaked privacy in both Android and iOS system, iii) the usage pattern is related to mobile device price. Through our experiments and analysis, it can be concluded that mobile privacy leakage is pervasive and serious.

In this paper, we propose the first traffic monitoring and labeling system for iOS devices, named iMonitor, which not just captures mobile network traffic in .pcap files, but also provides comprehensive APP-related and user-related information of captured packets. Through further analysis, one can obtain the exact APP or device where each packet comes from. The labeled traffic can be used in many research areas for mobile security, such as privacy leakage detection and user profiling. Given the implementation methodology of NetworkExtension framework of iOS 9+, APP labels of iMonitor are reliable enough so that labeled traffic can be regarded as training data for any traffic classification methods. Evaluations on real iPhones demonstrate that iMonitor has no notable impact upon user experience even with slight packet latency. Also, the experiment result supports our motivation that mobile traffic monitoring for iOS is absolutely necessary, as traffic generated by different OSes like Android and iOS are different and unreplaceable in researches.

with the development of SDN, ICN and 5G networks, the research of future network becomes a hot topic. Based on the design idea of SDN network, this paper analyzes the propagation model and detection method of malicious code in future network. We select characteristics of SDN and analyze the features use different feature selection methods and sort the features. After comparison the influence of running time by different classification algorithm of different feature selection, we analyze the choice of reduction dimension m, and find out the different types of malicious code corresponding to the optimal feature subset and matching classification method, designed for malware detection system. We analyze the node migration rate of malware in mobile network and its effect on the outbreak of the time. In this way, it can provide reference for the management strategy of the switch node or the host node by future network controller.

Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them.

In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.

Mobile users access location services from a location based server. While doing so, the user's privacy is at risk. The server has access to all details about the user. Example the recently visited places, the type of information he accesses. We have presented synergetic technique to safeguard location privacy of users accessing location-based services via mobile devices. Mobile devices have a capability to form ad-hoc networks to hide a user's identity and position. The user who requires the service is the query originator and who requests the service on behalf of query originator is the query sender. The query originator selects the query sender with equal probability which leads to anonymity in the network. The location revealed to the location service provider is a rectangle instead of exact co-ordinate. In this paper we have simulated the mobile network and shown the results for cloaking area sizes and performance against the variation in the density of users.