Cyber Supply Chain(CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, Procedure (TTP), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement.
This paper presents design of a climbing robot for inspection of glass curtain walls. The double-chamber structure enables the robot to climb over grooves on the glasses. In order to reduce the weight, both number and shape of the chambers are specially considered, and the pressure structure is optimized by FEA method. The statics models of different adsorption situations are also analyzed and deduced for the operational safety. In addition, design of the working arm and the wireless control system are introduced in detail. Finally, experiments of the robot are illustrated, including adsorption on different surfaces, vertical and horizontal groove-crossing as well as glass inspection. These experiments fully prove the theoretical analysis and demonstrate the climbing performance of the robot.
In this article, a new wall-climbing robot platform with protection devices for city inspection is developed. After an overall structural introduction, the main protection devices of the robot are described in detail, including the support frame, the Ethylene Vinyl Acetate (EVA) shell, and the airbag. The support frame plays the roles of chassis and protection framework, so integrative and lightweight design is required. The EVA shell covers the support frame, and it protects the robot from overturn falling down from the wall. The airbag is designed both for sealing and protection. The mechanical model of the airbag is established based on the engineering thermodynamics theory and is used for force analysis when robot falls down on the ground. In addition, two-level distributed control system is designed to achieve the control of fan speed, straight moving, differential steering, position servo, and video transmission. To verify the feasibility of the climbing robot, many experiments are conducted, that is, experiments of movement, load capacity, adaptability to the wall surfaces, endurance, camera, sensor, and antithrow. The results show that the actual working performance of the climbing robot is favorable, thus providing a train of thought and inspiration for the antithrow design of climbing robot.
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