Novel Defense Schemes for Artificial Intelligence Deployed in Edge Computing Environment

The last few years have seen the great potential of artificial intelligence (AI) technology to efficiently and effectively deal with an incredible deluge of data generated by the Internet of Things (IoT) devices. If all the massive data is transferred to the cloud for intelligent processing, it not only brings considerable challenges to the network bandwidth but also cannot meet the needs of AI applications that require fast and real-time response. Therefore, to achieve this requirement, mobile or multiaccess edge computing (MEC) is receiving a substantial amount of interest, and its importance is gradually becoming more prominent. However, with the emerging of edge intelligence, AI also suffers from several tremendous security threats in AI model training, AI model inference, and private data. This paper provides three novel defense strategies to tackle malicious attacks in three aspects. First of all, we introduce a cloud-edge collaborative antiattack scheme to realize a reliable incremental updating of AI by ensuring the data security generated in the training phase. Furthermore, we propose an edge-enhanced defense strategy based on adaptive traceability and punishment mechanism to effectively and radically solve the security problem in the inference stage of the AI model. Finally, we establish a system model based on chaotic encryption with the three-layer architecture of MEC to effectively guarantee the security and privacy of the data during the construction of AI models. The experimental results of these three countermeasures verify the correctness of the conclusion and the feasibility of the methods.

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Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy

Electronics ◽

10.3390/electronics10040391 ◽

2021 ◽

Vol 10 (4) ◽

pp. 391

Author(s):

Dongjun Na ◽

Sejin Park

Keyword(s):

Data Privacy ◽

Single Point ◽

Consensus Algorithm ◽

Security And Privacy ◽

Public Key ◽

Byzantine Fault Tolerance ◽

Blockchain Technology ◽

Private Data ◽

Security Problem ◽

Iot Devices

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).

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An edge-cloud collaborative computing platform for building AIoT applications efficiently

10.21203/rs.3.rs-218949/v1 ◽

2021 ◽

Author(s):

Guoping Rong ◽

Yangchen Xu ◽

Xinxin Tong ◽

Haojun Fan

Keyword(s):

Artificial Intelligence ◽

Model Building ◽

Security And Privacy ◽

Collaborative Computing ◽

Computing Platform ◽

Network Bandwidth ◽

Computing Paradigm ◽

High Responsiveness ◽

Great Pressure ◽

Iot Devices

Abstract The convergence of the Artificial Intelligence (AI) and the Internet of Things (IoT), i.e. the Artificial Intelligence of Things (AIoT), is a very promising technology that redefines the way people interact with the surrounding devices. Practical AIoT applications not only have high demands on computing and storage resources, but also desire for high responsiveness. Traditional cloud-based computing paradigm faces the great pressure on the network bandwidth and communication latency, hence the newly emerged edge computing paradigm gets involved. Consequently, AIoT applications can be implemented in an edge-cloud collaborative manner, where the model building and model inferencing are offloaded to the cloud and the edge, respectively. However, developers still face challenges building AIoT applications in practice due to the inherent heterogeneity of the IoT devices, the declining accuracy of once trained models, the security and privacy issues, etc. In this paper, we present the design of an industrial edge-cloud collaborative computing platform that aims to facilitate building AIoT applications in practice. Furthermore, a real-world use case is presented in this paper, which proved the efficiency of building an AIoT application on the platform.

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AIoT with PUF: A Concrete Security

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f4520.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 476-481

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Machine Intelligence ◽

Customer Relationships ◽

Physical Unclonable Functions ◽

Security Problem ◽

Fast Solution ◽

Iot Devices ◽

Efficiency And Productivity ◽

Exchange Data

Artificial Intelligence in contrast to Natural Intelligence also known as Machine Intelligence is intelligence revealed by machine. It is the science and engineering of making machines intelligent. Therefore, it is a technique that makes a machine work like humans. The IOT Internet of Things is a network of internet-connected objects which can connect and exchange data. The combination of AI and IoT called AIoT is the combination of Artificial Intelligence and Internet of Things to achieve more efficient IoT operations. When Artificial Intelligence is added to IoT it means that the devices can analyze data and make decisions and act accordingly without the intervention of humans. The combination of AI and IOT has several advantages like saving money, building deeper customer relationships, increased operational efficiency and productivity and enhanced security and safety. This research paper focuses on what is AIoT, its applications and challenges and further, it also focuses on AIoT security concern and how can we solve the security problem with the use of PUF which is hardware security which is a simple and fast solution for security purpose. PUF is also more compatible with AIoT gadgets. Attacks on IoT devices are on the upsurge. Physical Unclonable functions (PUFs) are recognized as a robust and mild-weight way for AIoT

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Preserving Data Privacy in the Internet of Medical Things Using Dual Signature ECDSA

Security and Communication Networks ◽

10.1155/2020/4960964 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Maria-Dolores Cano ◽

Antonio Cañavate-Sanchez

Keyword(s):

Elliptic Curve ◽

Private Information ◽

Data Privacy ◽

Edge Computing ◽

Security And Privacy ◽

The Internet ◽

Consumer Privacy ◽

Digital Signature Algorithm ◽

Internet Of Medical Things ◽

Iot Devices

The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.

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A Novel Framework of an IOT-Blockchain-Based Intelligent System

Wireless Communications and Mobile Computing ◽

10.1155/2022/4741923 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Aliaa M. Alabdali

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Security ◽

Intelligent System ◽

Security And Privacy ◽

Self Monitoring ◽

Smart Systems ◽

Blockchain Technology ◽

Iot Devices ◽

Smart Mobile

With the growing need of technology into varied fields, dependency is getting directly proportional to ease of user-friendly smart systems. The advent of artificial intelligence in these smart systems has made our lives easier. Several Internet of Things- (IoT-) based smart refrigerator systems are emerging which support self-monitoring of contents, but the systems lack to achieve the optimized run time and data security. Therefore, in this research, a novel design is implemented with the hardware level of integration of equipment with a more sophisticated software design. It was attempted to design a new smart refrigerator system, which has the capability of automatic self-checking and self-purchasing, by integrating smart mobile device applications and IoT technology with minimal human intervention carried through Blynk application on a mobile phone. The proposed system automatically makes periodic checks and then waits for the owner’s decision to either allow the system to repurchase these products via Ethernet or reject the purchase option. The paper also discussed the machine level integration with artificial intelligence by considering several features and implemented state-of-the-art machine learning classifiers to give automatic decisions. The blockchain technology is cohesively combined to store and propagate data for the sake of data security and privacy concerns. In combination with IoT devices, machine learning, and blockchain technology, the proposed model of the paper can provide a more comprehensive and valuable feedback-driven system. The experiments have been performed and evaluated using several information retrieval metrics using visualization tools. Therefore, our proposed intelligent system will save effort, time, and money which helps us to have an easier, faster, and healthier lifestyle.

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TrustChain: A Privacy Preserving Blockchain with Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2019/2014697 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 10

Author(s):

Upul Jayasinghe ◽

Gyu Myoung Lee ◽

Áine MacDermott ◽

Woo Seop Rhee

Keyword(s):

Real Time ◽

Personal Data ◽

Privacy Preserving ◽

Edge Computing ◽

Security And Privacy ◽

Privacy Concerns ◽

Massive Scale ◽

Potential Alternative ◽

Iot Devices ◽

Scarcity Of Resources

Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.

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Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

Security and Communication Networks ◽

10.1155/2020/8872586 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zhanyang Xu ◽

Wentao Liu ◽

Jingwang Huang ◽

Chenyi Yang ◽

Jiawei Lu ◽

...

Keyword(s):

Artificial Intelligence ◽

Privacy Preservation ◽

Edge Computing ◽

Learning Ability ◽

Security And Privacy ◽

Model Parameters ◽

Paradigm Shifts ◽

Privacy Leakage ◽

Computing Paradigm ◽

Comprehensive Survey

With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by transferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high network latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes (ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However, compared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for restricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful learning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain extent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the contribution of AI to the IoT security in EC is presented. First, the research status and some basic definitions are introduced. Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC are discussed.

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EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing

Sensors ◽

10.3390/s18113616 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3616 ◽

Cited By ~ 3

Author(s):

Kai Fan ◽

Jie Yin ◽

Kuan Zhang ◽

Hui Li ◽

Yintang Yang

Keyword(s):

Cloud Computing ◽

Data Management ◽

Low Cost ◽

Bloom Filter ◽

Edge Computing ◽

Security And Privacy ◽

Network Bandwidth ◽

Computing Model ◽

Retrieval Scheme ◽

Data Source

Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing tasks from the original cloud computing model to the edge device, the message is running on computing resources close to the data source. The edge computing model can effectively reduce the pressure on the cloud computing center and lower the network bandwidth consumption. However, the security and privacy issues in edge computing are worth noting. In this paper, we propose an efficient auto-correction retrieval scheme for data management in edge computing, named EARS-DM. With automatic error correction for the query keywords instead of similar words extension, EARS-DM can tolerate spelling mistakes and reduce the complexity of index storage space. By the combination of TF-IDF value of keywords and the syntactic weight of query keywords, keywords who are more important will obtain higher relevance scores. We construct an R-tree index building with the encrypted keywords and the children nodes of which are the encrypted identifier FID and Bloom filter BF of files who contain this keyword. The secure index will be uploaded to the edge computing and the search phrase will be performed by the edge computing which is close to the data source. Then EDs sort the matching encrypted file identifier FID by relevance scores and upload them to the cloud server (CS). Performance analysis with actual data indicated that our scheme is efficient and accurate.

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Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey

Remote Sensing ◽

10.3390/rs13214387 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4387

Author(s):

Jia Liu ◽

Jianjian Xiang ◽

Yongjun Jin ◽

Renhua Liu ◽

Jining Yan ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Remote Sensing ◽

Cloud Computing ◽

Precision Agriculture ◽

Graphics Processing Units ◽

Edge Computing ◽

Data Sources ◽

Network Bandwidth ◽

Wide Range

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.

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A Survey on Emerging Security Issues, Challenges, and Solutions for Internet of Things (IoTs)

10.4018/978-1-7998-7789-9.ch009 ◽

2022 ◽

pp. 148-175

Author(s):

Anish Khan ◽

Dragan Peraković

Keyword(s):

Internet Of Things ◽

Security And Privacy ◽

Future Research ◽

Security Issues ◽

Digital World ◽

Private Data ◽

Wide Range ◽

Future Research Directions ◽

Iot Devices ◽

Privacy Issues

The internet of things is a cutting-edge technology that is vulnerable to all sorts of fictitious solutions. As a new phase of computing emerges in the digital world, it intends to produce a huge number of smart gadgets that can host a wide range of applications and operations. IoT gadgets are a perfect target for cyber assaults because of their wide dispersion, availability/accessibility, and top-notch computing power. Furthermore, as numerous IoT devices gather and investigate private data, they become a gold mine for hostile actors. Hence, the matter of fact is that security, particularly the potential to diagnose compromised nodes, as well as the collection and preservation of testimony of an attack or illegal activity, have become top priorities. This chapter delves into the timeline and the most challenging security and privacy issues that exist in the present scenario. In addition to this, some open issues and future research directions are also discussed.

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