A Clustering Approach for Protecting GIS Vector Data

The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to prevent its unauthorized use. One way of protecting this type of data is by embedding within it a digital watermark. In this paper, we build on our previous work on watermarking vector map data, to improve the robustness to (unwanted) modifications to the maps that may prevent the identification of the rightful owner of the data. More specifically, we address the simplification (removing some vertices from GIS vector data) and interpolation (adding new vertices to GIS data) modifications by exploiting a particular property of vector data called a bounding box. In addition, we experiment with bigger maps to establish the feasibility of the approach for larger maps

Exploiting Vector Map Properties for GIS Data Copyright Protection

Geographic Information System (GIS) vector maps have become more widely available, prompting a need to prevent their unauthorized use. This is commonly done through the use of a digital watermark, with many approaches applying techniques from image map watermarking, without exploiting the particular properties of vector map data. In previous work we showed that using k-medoids clustering and the bounding box property of vector maps in the embedding process leads to increased robustness against simplification (removing vertices from vector data) and interpolation (adding new vertices to the data) attacks, which may distort the watermark and prevent the identification of the map owner. In this paper we show that the advantages of using the bounding box property are maintained even with a different clustering approach (k-means), and argue that they would hold regardless of the method used for identifying the watermark embedding locations in the map

Global IoT Mobility: A Path Based Forwarding Approach

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains

Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks

Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naïve Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance