Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.

Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics

Social network security: Issues, challenges, threats, and solutions

The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR 
 $$\ge $$
  10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.

The individual identification method of wireless device based on dimensionality reduction and machine learning

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

Blockchain-Enabled Distributed Security Framework for Next-Generation IoT: An Edge Cloud and Software-Defined Network-Integrated Approach

Adversary models have been fundamental to the various cryptographic protocols and methods. However, their use in most of the branches of research in computer science is comparatively restricted, primarily in case of the research in cyberphysical security (e.g., vulnerability studies, position confidentiality). In this article, we propose an energy-aware green adversary model for its use in smart industrial environment through achieving confidentiality. Even though, mutually the hardware and the software parts of cyberphysical systems can be improved to decrease its energy consumption, this article focuses on aspects of conserving position and information confidentiality. On the basis of our findings (assumptions, adversary goals, and capabilities) from the literature, we give some testimonials to help practitioners and researchers working in cyberphysical security. The proposed model that runs on real-time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query, thus, facilitating energy consumption minimization. Moreover, we calculate the transferring/acceptance slots required for each query to avoid deteriorating slots. The experimental results confirm that the proposed approach can diminish energy consumption up to five times in comparison to existing approaches

Energy-Aware Green Adversary Model for Cyberphysical Security in Industrial System

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology due to the mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.

Medhane Darshan Vishwasrao

Papers

ESCAPE: Effective Scalable Clustering Approach for Parallel Execution of Continuous Position-Based Queries in Position Monitoring Applications