Pattern-Lock is one of graphical authentication schemes that shows high popularity today. Based on recent research, the security requirements metrics of Pattern-Lock applications have not proposed yet. The goal of this study is to define security requirements metrics for Pattern-Lock applications on mobile devices. Our study has identified 12 threat statements and 18 requirements statements by analyzing STRIDE (Spoofing the identity, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) and Extended Misuse Case diagram. To develop the metrics we have used Goal-Question-Metric (GQM) paradigm. Based on these, we develop 3 Goals and 7 Questions and resulted in 20 metrics for security requirements. The metrics have been evaluated using 30 App Locker Android applications, and the results show that some metrics have higher values than others. Number of Pattern Characteristics that Successfully Detected, Ability to Relock, and Grid Size metrics have the three highest values. These metrics requires higher priorities to look into when developers need to build the App Locker applications. Moreover, developers should ensure that App Locker applications have values higher than average of security goals and metrics achievements.

Effective and efficient public service delivery like healthcare services are very important in today’s society, especially in the rural areas. People in rural areas are expected to have access to public facilities at all times. However, these services are not always available when they are needed. This paper discusses the problems faced by rural areas of Mafikeng in South Africa (SA) when public and basic healthcare facilities like medical ambulance transports are needed during emergency situation. The challenges ranges from poor communication, poor road network and unstructured address to non-arrival of ambulances leading loss of lives that are preventable. This paper designed and implemented a system prototype using mobile application technologies to offer cost-effective services to patients during emergencies. It is intended to reduce long queues in hospitals and long waiting periods for an ambulance via location-based services. By using this application, lives in the rural areas can be made easier and loss of lives prevented by providing timely response from the appropriate healthcare providers during emergencies.

Massive increase in the mobile data traffic volume has recently resulted in a big interest towards the distributed mobility management solutions that aim to address the limitations and drawbacks of centralized mobility management. Location management is an important requirement in a distributed mobility management environment. To provide seamless Internet data services to a mobile node, the location of a mobile node is stored and periodically updated on a location server through a location update message that is sent by the mobile node. In this paper, we propose an intelligent approach of setting the period of sending location update messages on the basis of a mobile node’s patterns of data sessions and IP handovers. We use a machine learning approach on the location server. The results show that our approach significantly reduces the signaling load of the location management and the overall reduction is more than 50%.

With the pervasiveness of information technology, one of the growing trends today is a phenomenon which can be termed one-user-to-many-computing-devices. In many cases, the need to manage information across multiple electronic devices and storage media arises. The challenge therefore is finding a file synchronization system that can effectively replicate files across these different devices. This paper presents the design, implementation, and evaluation of FileSyncer, a rapid and efficient file synchronization tool that, in addition to the traditional synchronization capabilities, supports manual update selection and mechanism to revert a synchronization process back to the last previous state. The system employs last modified time, file size and CRC checksum for update detection and to ensure integrity of synchronized files. The synchronization times of the system for files of different sizes were compared with those of four existing file synchronization systems. Results showed increased efficiency in terms of time taken by FileSyncer to complete a synchronization operation with increase in file size compared to the other systems. In the future, we plan to release FileSyncer to the open source community for further development.

In present scenario, where data is transmitted from transmitter end to receiver end, security and authenticity of the data are the major issues. Hence, the need of an efficient technique which can assure secure transmission of data comes into the picture. There are several techniques which have been developed for this purpose over the time. Cryptography is one such technique. In this paper a new model is presented that is based on the implementation of Hash techniques with the Polynomial Message Authenticating scheme to increase the security level of transmission. The comparative analysis of Secure Hash Algorithms i.e. SHA-1 and SHA-256, implemented using Polynomial Message Authenticating scheme, is presented on the basis of different parameters like processing gain, delivery ratio, energy consumed, duty cycle, Hashing length and degree of polynomial.

The energy resource-contrived is the most difficult aspect of WSN and that energy cannot be re-charged. When every sensor node is needed to move ahead the data packet to the sink node, the problem emerges there. Through unmoving, listening and retrans-mitting, the accessible energy in every node could be lost in this procedure because of collisions as well as over-hearing. Network topology management and energy min-imization is done due to Cluster-based WSN routing pro-tocols. In clustering in WSN, sensor nodes are sorted into little disjoint clusters, where every cluster has an organizer referred as CH. In this paper, energy efficient clustering techniques related to WSNs are discussed and com-parative analysis is also performed.

The continuous changing networks introduce new attacks, which represent an explicit problem that affects the security of enterprise resources. Thus, there is a real need to build up intelligent intrusion detection systems that can learn from the network behavior. In this paper, a learnable anomaly intrusion detection system based on attributional rules is presented. The proposed model is chosen with the advantages of being expressive, flexible and can operate in noisy and inconsistent environments. The system is a real-time intrusion detector that utilizes incremental supervised machine learning technique. Such technique makes use of the Algorithm Quasi-optimal (AQ) which is based on attributional calculus.
Here, an Algorithm Quasi-optimal for Intrusion Detection System (AQ4IDS) is exploited and implemented using attributional rules to discriminate between normal and anomalous network traffic. The behavior of AQ4IDS is tested, and to illustrate its superiority. The experimental results showed that, the model automatically accommodates new rules from continuous network stream. Many experiments have verified the fact that AQ4IDS can efficiently discriminate between normal and anomalous network traffic, in addition to offering the advantage of detecting novel and zero day attacks.

Symmetric searchable encryption (SSE) schemes allow a data owner to encrypt its data in such a way that it could be searched in encrypted form. When searching over encrypted data the retrieved data, search query, and search query outcome everything must be protected. A series of SSE schemes have been proposed in the past decade. In this paper, we are going to propose our an efficient multi-keyword symmetric searchable encryption scheme for secure data outsourcing and evaluate the performance of our proposed scheme on a real data set.