Biblio

The goals, objectives and criteria of the effectiveness of the creation, maintenance and use of the Digital Information Fund of Intellectual Property (DIFIP) are considered. A formalized methodology is proposed for designing DIFIPs, increasing its efficiency and quality, based on a set of interconnected models, methods and algorithms for analysis, synthesis and normalization distributed information management of DIFIP's structure; classification of databases users of patent and scientific and technical information; synthesis of optimal logical structures of the DIFIP database and thematic databases; assessing the quality of the database and ensuring the required level of data security.

Currently, air pollution is still a problem that requires special attention, especially in big cities. Air pollution can come from motor vehicle fumes, factory smoke or other particles. To overcome these problems, a system is made that can monitor environmental conditions in order to know the good and bad of air quality in an environment and is expected to be a solution to reduce air pollution that occurs. The system created will utilize the Wireless Sensor Network (WSN) combined with Waspmote Smart Environment PRO, so that later data will be obtained in the form of temperature, humidity, CO levels and CO2 levels. From the sensor data that has been processed on Waspmote, it will then be used as input for data processing using a fuzzy algorithm. The classification obtained from sensor data processing using fuzzy to monitor environmental conditions there are 5 classifications, namely Very Good, Good, Average, Bad and Dangerous. Later the data that has been collected will be distributed to Meshlium as a gateway and will be stored in the database. The process of sending information between one party to another needs to pay attention to the confidentiality of data and information. The final result of the implementation of this research is that the system is able to classify values using fuzzy algorithms and is able to secure text data that will be sent to the database via Meshlium, and is able to display data sent to the website in real time.

This paper studies adaptive control of bilateral teleoperation systems with false data injection attacks. The model of bilateral teleoperation system with false data injection attacks is presented. An off-line identification approach based on the least squares is used to detect whether false data injection attacks occur or not in the communication channel. Two Bernoulli distributed variables are introduced to describe the packet dropouts and false data injection attacks in the network. An adaptive controller is proposed to deal stability of the system with false data injection attacks. Some sufficient conditions are proposed to ensure the globally asymptotical stability of the system under false data injection attacks by using Lyapunov functional methods. A bilateral teleoperation system with two degrees of freedom is used to show the effectiveness of gained results.

The current study aims to discern the impact of expert systems and neural network on the Jordanian commercial banks. In achieving the objective, the study employed descriptive analytical approach and the population consisted of the 13 Jordanian commercial banks listed at Amman Stock Exchange-ASE. The primary data were obtained by using a questionnaire with 188 samples distributed to a group of accountants, internal auditors, and programmers, who constitute the study sample. The results unveiled that there is an impact of the application of expert systems and neural networks on the relevance of financial information in Jordanian commercial banks. It also revealed that there is a high level of relevance of financial information in Jordanian commercial banks. Accordingly, the study recommended the need for banks to keep pace with the progress and development taking place in connection to the process and environment of expertise systems by providing modern and developed devices to run various programs and expert systems. It also recommended that, Jordanian commercial banks need to rely more on advanced systems to operate neural network technology more efficiently.

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

In this paper will be described a new security protocol for secret sharing and hiding, which use selected personal features. Such technique allows to create human-oriented personalized security protocols dedicated for particular users. Proposed method may be applied in dispersed computing systems, where secret data should be divided into particular number of parts.

A digital signature is a type of asymmetric cryptography that is used to ensure that the recipient receives the actual received message from the intended sender. Problems that often arise conventionally when requiring letter approval from the authorized official, and the letter concerned is very important and urgent, often the process of giving the signature is hampered because the official concerned is not in place. With these obstacles, the letter that should be distributed immediately becomes hampered and takes a long time in terms of signing the letter. The purpose of this study is to overcome eavesdropping and data exchange in sending data using Digital Signature as authentication of data authenticity and minimizing fake signatures on letters that are not made and authorized by relevant officials based on digital signatures stored in the database. This research implements the Rivest Shamir Adleman method. (RSA) as outlined in an application to provide authorization or online signature with Digital Signature. The results of the study The application of the Rivest Shamir Adleman (RSA) algorithm can run on applications with the Digital Signature method based on ISO 9126 testing by expert examiners, and the questionnaire distributed to users and application operators obtained good results from an average value of 79.81 based on the scale table ISO 9126 conversion, the next recommendation for encryption does not use MD5 but uses Bcrypt secure database to make it stronger.

This article proposes a health monitoring system platform for cross-river bridges based on big data. The system can realize regionalized bridge operation and maintenance management. The system has functions such as registration modification and deletion of sensor equipment, user registration modification and deletion, real-time display and storage of sensor monitoring data, and evaluation and early warning of bridge structure safety. The sensor is connected to the lower computer through the serial port, analog signal, fiber grating signal, etc. The lower computer converts a variety of signals into digital signals through the single-chip A/D sampling and demodulator, etc., and transmits it to the upper computer through the serial port. The upper computer uses ARMCortex-A9 Run the main program to realize multi-threaded network communication. The system platform is to test the validity of the model, and a variety of model verification methods are used for evaluation to ensure the reliability of the big data analysis method.

Cryptocurrencies like Bitcoin have become a popular weapon for illegal activities. They have the characteristics of decentralization and anonymity, which can effectively avoid the supervision of government departments. How to de-anonymize Bitcoin transactions is a crucial issue for regulatory and judicial investigation departments to supervise and combat crimes involving Bitcoin effectively. This paper aims to de-anonymize Bitcoin transactions and present a Bitcoin transaction traceability method based on Bitcoin network traffic analysis. According to the characteristics of the physical network that the Bitcoin network relies on, the Bitcoin network traffic is obtained at the physical convergence point of the local Bitcoin network. By analyzing the collected network traffic data, we realize the traceability of the input address of Bitcoin transactions and test the scheme in the distributed Bitcoin network environment. The experimental results show that this traceability mechanism is suitable for nodes connected to the Bitcoin network (except for VPN, Tor, etc.), and can obtain 47.5% recall rate and 70.4% precision rate, which are promising in practice.

Swarm learning (SL) is an emerging promising decentralized machine learning paradigm and has achieved high performance in clinical applications. SL solves the problem of a central structure in federated learning by combining edge computing and blockchain-based peer-to-peer network. While there are promising results in the assumption of the independent and identically distributed (IID) data across participants, SL suffers from performance degradation as the degree of the non-IID data increases. To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants. SL-GAN trains generators and discriminators locally, and periodically aggregation via a randomly elected coordinator in SL network. Under the standard assumptions, we theoretically prove the convergence of SL-GAN using stochastic approximations. Experimental results demonstrate that SL-GAN outperforms state-of-art methods on three real world clinical datasets including Tuberculosis, Leukemia, COVID-19.

With the electric power distribution grid facing ever increasing complexity and new threats from cyber-attacks, situational awareness for system operators is quickly becoming indispensable. Identifying de-energized lines on the distribution system during a SCADA communication failure is a prime example where operators need to act quickly to deal with an emergent loss of service. Loss of cellular towers, poor signal strength, and even cyber-attacks can impact SCADA visibility of line devices on the distribution system. Neural Networks (NNs) provide a unique approach to learn the characteristics of normal system behavior, identify when abnormal conditions occur, and flag these conditions for system operators. This study applies a 24-hour load forecast for distribution line devices given the weather forecast and day of the week, then determines the current state of distribution devices based on changes in SCADA analogs from communicating line devices. A neural network-based algorithm is applied to historical events on Alabama Power's distribution system to identify de-energized sections of line when a significant amount of SCADA information is hidden.

Machine Learning (ML) and Artificial Intelligence (AI) techniques are widely adopted in the telecommunication industry, especially to automate beyond 5G networks. Federated Learning (FL) recently emerged as a distributed ML approach that enables localized model training to keep data decentralized to ensure data privacy. In this paper, we identify the applicability of FL for securing future networks and its limitations due to the vulnerability to poisoning attacks. First, we investigate the shortcomings of state-of-the-art security algorithms for FL and perform an attack to circumvent FoolsGold algorithm, which is known as one of the most promising defense techniques currently available. The attack is launched with the addition of intelligent noise at the poisonous model updates. Then we propose a more sophisticated defense strategy, a threshold-based clustering mechanism to complement FoolsGold. Moreover, we provide a comprehensive analysis of the impact of the attack scenario and the performance of the defense mechanism.

Although DC microgrids using a distributed cooperative control architecture can avoid the instability or shutdown issues caused by a single-point failure as compared to the centralized approach, limited global information in the former makes it difficult to detect cyber attacks. Here, we present a false data injection attack (FDIA)–-termed as a local control input attack–-targeting voltage observers in the secondary controllers and control loops in the primary controllers. Such an attack cannot be detected by only observing the performance of the estimated voltage of each agent, thereby posing a potential threat to the system operation. To address this, a detection method using the outputs of the voltage observers is developed to identify the exact location of an FDIA. The proposed approach is based on the characteristics of the distributed cooperative network and avoids heavy dependency on the system model parameters. Next, an event-driven mitigation approach is deployed to substitute the attacked element with a reconstructed signal upon the detection of an attack. Finally, the effectiveness of the proposed resilient scheme is validated using simulation results.

The traditional approach to distributed deep neural network (DNN) training is data-distributed learning, which partitions and distributes data to workers. This approach, although has good convergence properties, has high communication cost, which puts a strain especially on edge systems and increases delay. An emerging approach is model-distributed learning, where a training model is distributed across workers. Model-distributed learning is a promising approach to reduce communication and storage costs, which is crucial for edge systems. In this paper, we design ResPipe, a novel resilient model-distributed DNN training mechanism against delayed/failed workers. We analyze the communication cost of ResPipe and demonstrate the trade-off between resiliency and communication cost. We implement ResPipe in a real testbed consisting of Android-based smartphones, and show that it improves the convergence rate and accuracy of training for convolutional neural networks (CNNs).

Distributed training is a novel approach to accelerating training of Deep Neural Networks (DNN), but common training libraries fall short of addressing the distributed nature of heterogeneous processors or interruption by other workloads on the shared processing nodes. This paper describes distributed training of DNN on computational storage devices (CSD), which are NAND flash-based, high-capacity data storage with internal processing engines. A CSD-based distributed architecture incorporates the advantages of federated learning in terms of performance scalability, resiliency, and data privacy by eliminating the unnecessary data movement between the storage device and the host processor. The paper also describes Stannis, a DNN training framework that improves on the shortcomings of existing distributed training frameworks by dynamically tuning the training hyperparameters in heterogeneous systems to maintain the maximum overall processing speed in term of processed images per second and energy efficiency. Experimental results on image classification training benchmarks show up to 3.1x improvement in performance and 2.45x reduction in energy consumption when using Stannis plus CSD compare to the generic systems.

In recent years, the “whole chain” development level of China's intellectual property creation, protection and application has been greatly improved. At the same time, cloud computing technology is booming, and intellectual property data distributed platforms based on cloud storage are emerging one after another. Firstly, this paper introduces the domestic intellectual property cloud platform services from the perspectives of government, state-owned enterprises and private enterprises; Secondly, four typical distributed platforms provided by commercial resources are selected to summarize the problems faced by the operation mode of domestic intellectual property services; Then, it compares and discusses the functions and service modes of domestic intellectual property distributed platform, and takes TSITE IP as an example, puts forward the design and construction strategies of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age. Finally, according to the development of contemporary information technology, this paper puts forward challenges and development direction for the future development of intellectual property platform.

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.

The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security.

This paper proposes a logic-based machine learning approach called Acuity which is designed to facilitate user-guided elucidation of novel phenomena from evidence sparsely distributed across large volumes of linked relational data. The work builds on systems from the field of Inductive Logic Programming (ILP) by introducing a suite of new techniques for interacting with domain experts and data sources in a way that allows complex logical reasoning to be strategically exploited on large real-world databases through intuitive hypothesis-shaping and data-caching functionality. We propose two methods for rebutting or shaping candidate hypotheses and two methods for querying or importing relevant data from multiple sources. The benefits of Acuity are illustrated in a proof-of-principle case study involving a retrospective analysis of the CryptoWall ransomware attack using data from a cyber security testbed comprising a small business network and an infected laptop.

In power networks, it is important to detect a cyber attack. In this paper, we propose a detection method of false data injection (FDI) attacks. FDI attacks cannot be detected from the estimation error in power networks. The proposed method is based on the distributed state estimation, and is used the tentative estimated state. The proposed method is demonstrated by a numerical example on the IEEE 14-bus system.

Our proposal aims to help solving a trust problem between licensors and licensees that occurs during the active life of license agreements. We particularly focus on licensing of proprietary intellectual property (IP) that is embedded in Internet of Things (IoT) devices and services (e.g. patented technologies). To achieve this we propose to encode the logic of license agreements into smart licenses (SL). We define a SL as a `digital twin' of a licensing contract, i.e. one or more smart contracts that represent the full or relevant parts of a licensing agreement in machine readable and executable code. As SL are self enforcing, the royalty computation and execution of payments can be fully automated in a tamper free and trustworthy way. This of course, requires to employ a Distributed Ledger Technology (DLT). Such an Automated Licensing Payment System (ALPS) can thus automate an established business process and solve a longstanding trust issue in licensing markets. It renders traditional costly audits obsolete, lowers entry barriers for those who want to participate in licensing markets, and enables novel business models too complex with traditional approaches.

With the rapid development of the wireless network and smart mobile equipment, many lawbreakers employ mobile devices to destroy and steal important information and property from other persons. In order to fighting the criminal act efficiently, the public security organ need to collect the evidences from the crime tools and submit to the court. In the meantime, with development of internal storage technology, the law enforcement officials collect lots of information from the smart mobile equipment, for the sake of handling the huge amounts of data, we propose a framework that combine distributed clustering methods to analyze data sets, this model will split massive data into smaller pieces and use clustering method to analyze each smaller one on disparate machines to solve the problem of large amount of data, thus forensics investigation work will be more effectively.

Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.

Information Security is a unified piece of information technology that has emerged as vibrant technology in the last two decades. To manage security, authentication assumes a significant part. Biometric is the physical unique identification as well as authentication for the third party. We have proposed the security model for preventing many attacks so we are used the innermost layer as a 3DES (Triple Encryption standard) cryptography algorithm that is providing 3- key protection as 64-bit and the outermost layer used the MD5 (Message Digest) algorithm. i. e. providing 128-bit protection as well as we is using fingerprint identification as physical security that is used in third-party remote integrity auditing. Remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depending on identity-based cryptography, which works on the convoluted testament of the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.

Aiming at the cyber security problem of the advanced metering infrastructure(AMI), an anomaly detection method based on edge computing framework for the AMI is proposed. Due to the characteristics of the edge node of data concentrator, the data concentrator has the capability of computing a large amount of data. In this paper, distributing the intrusion detection model on the edge node data concentrator of the AMI instead of the metering center, meanwhile, two-way communication of distributed local model parameters replaces a large amount of data transmission. The proposed method avoids the risk of privacy leakage during the communication of data in AMI, and it greatly reduces communication delay and computational time. In this paper, KDDCUP99 datasets is used to verify the effectiveness of the method. The results show that compared with Deep Convolutional Neural Network (DCNN), the detection accuracy of the proposed method reach 99.05%, and false detection rate only gets 0.74%, and the results indicts the proposed method ensures a high detection performance with less communication rounds, it also reduces computational consumption.