A roadside units positioning framework in the context of vehicle-to-infrastructure based on integrated AHP-entropy and group-VIKOR

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task due to numerous criteria, such as the cost of RSUs, the intersection area and communication strength, which affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Towards this end, a four-stage methodology for a new RSU positioning framework using multi-criteria decision-making (MCDM) in V2I communication system context has been designed. Real time V2I hardware for data collection purpose was developed. This hardware device consisted of multi mobile-nodes (in the car) and RSUs and connected via an nRF24L01+ PA/LNA transceiver module with a microcontroller. In the second phase, different testing scenarios were identified to acquire the required data from the V2I devices. These scenarios were evaluated based on three evaluation attributes. A decision matrix consisted of the scenarios as alternatives and its assessment per criterion was constructed. In the third phase, the alternatives were ranked using hybrid of MCDM techniques, specifically the Analytic Hierarchy Process (AHP), Entropy and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The result of each decision ranking was aggregated using Borda voting approach towards a final group ranking. Finally, the validation process was made to ensure the ranking result undergoes a systematic and valid rank. The results indicate the following: (1) The rank of scenarios obtained from group VIKOR suggested the second scenario with, four RSUs, a maximum distance of 200 meters between RSUs and the antennas height of two-meter, is the best positioning scenarios; and (2) in the objective validation. The study also reported significant differences between the scores of the groups, indicating that the ranking results are valid. Finally, the integration of AHP, Entropy and VIKOR has effectively solved the RSUs positioning problems

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics

Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we present a survey on existing DNN IP protection works in terms of the above six attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. After that, we analyze the potential attacks on DNN IP protection methods from the aspects of model modifications, evasion attacks, and active attacks. Besides, a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Lastly, future research opportunities and challenges on DNN IP protection are presented

Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking

In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection