Accurate threat hunting in industrial internet of things edge devices

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and processing.Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT.Moreover,they can allow malicious software installed on end nodes to penetrate the network.This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices.The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting.Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one of the biggest problems.Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of networks.Therefore,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge devices.In the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system.The initial stage is followed by the reconstructing and the processing state.The processed data is transmitted to the nodes through controlled deep learning techniques.The entire stage of data collection,transmission and data reduction between edge devices uses less energy.The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy.Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme.Predictive accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

A review on edge analytics:Issues,challenges,opportunities,promises,future directions,and applications

Edge technology aims to bring cloud resources(specifically,the computation,storage,and network)to the closed proximity of the edge devices,i.e.,smart devices where the data are produced and consumed.Embedding computing and application in edge devices lead to emerging of two new concepts in edge technology:edge computing and edge analytics.Edge analytics uses some techniques or algorithms to analyse the data generated by the edge devices.With the emerging of edge analytics,the edge devices have become a complete set.Currently,edge analytics is unable to provide full support to the analytic techniques.The edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply,small memory size,limited resources,etc.This article aims to provide a detailed discussion on edge analytics.The key contributions of the paper are as follows-a clear explanation to distinguish between the three concepts of edge technology:edge devices,edge computing,and edge analytics,along with their issues.In addition,the article discusses the implementation of edge analytics to solve many problems and applications in various areas such as retail,agriculture,industry,and healthcare.Moreover,the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues,emerging challenges,research opportunities and their directions,and applications.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

Modulation depth of series SQUIDs modified by Josephson junction area

The superconducting quantum interference device（SQUID） amplifier is widely used in the field of weak signal detection for its low input impedance, low noise, and low power consumption. In this paper, the SQUIDs with identical junctions and the series SQUIDs with different junctions were successfully fabricated. The Nb/Al-AlOx/Nb trilayer and input Nb coils were prepared by asputtering equipment. The SQUID devices were prepared by a sputtering and the lift-off method.Investigations by AFM, OM and SEM revealed the morphology and roughness of the Nb films and Nb/Al-AlOx/Nb trilayer.In addition, the current–voltage characteristics of the SQUID devices with identical junction and different junction areas were measured at 2.5 K in the He^3 refrigerator. The results show that the SQUID modulation depth is obviously affected by the junction area. The modulation depth obviously increases with the increase of the junction area in a certain range. It is found that the series SQUID with identical junction area has a transimpedance gain of 58 Ω approximately.

New Lite YOLOv4-Tiny Algorithm and Application on Crack Intelligent Detection

Conforming to the rapidly increasing market demand of crack detection for tall buildings,the idea of integrating deep network technology into wall-climbing robot for crack detection is put forward in this paper.Taking the dependence and hardware requirements when deployed on such edge devices as Raspberry Pi into consideration,the Darknet neural network is selected as the basic framework for detection.In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks,the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly.The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets,which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files.Meanwhile,the AlexeyAB version of Darknet accelerated by NNpack package is deployed on Raspberry Pi 4B,and the crack detection experiments are carried out.Some characteristics,e.g.,fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm,are confirmed by comparison with those of original YOLOv4-tiny algorithm.The innovations of this paper focus on the simple network structure,fewer network layers,and earlier forward transmission of features to prevent over-fitting,showing the new lite neural network exceeds the original YOLOv4-tiny network significantly.

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability;(2) known Edge Device Fault Probability Distribution (EDFPD);(3) known edge device fault number and EDFPD;(4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a Cause Event (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.