Assessing security awareness among users is essential for protecting industrial control systems (ICSs) from social engineering attacks. This research aimed to determine the effect of cyber security awareness on the emergency response to cyber security incidents in the ICS. Additionally, this study has adopted a variety of cyber security emergency response process measures and frameworks and comprehensively proposes a new organizational model of cyber security incident response. The corresponding measures are evaluated based on the MP²DR² risk control matrix model to assess their practical value in the evaluation stage. This study found that after adding security awareness measures to response control measures, the influential value ranking of other control measures changed. The practical value of security awareness control measures was given a higher priority than that of other control measures. The research results highlight the importance of cyber security awareness and aim to inspire ICSs to place a higher priority on staff cyber security awareness in relation to cyber security incidents, which can effectively prevent the occurrence of cyber security incidents and make the field of industrial control application agency respond to incidents faster to restore the regular progress of all works.

As deep neural networks (DNNs) are widely used in the field of remote sensing image recognition, there is a model security issue that cannot be ignored. DNNs have been shown to be vulnerable to small perturbations in a large number of studies in the past, and this security risk naturally exists in remote sensing object detection models based on DNNs. The complexity of remote sensing object detection models makes it difficult to implement adversarial attacks on them, resulting in the current lack of systematic research on adversarial examples in the field of remote sensing image recognition. In order to better deal with the adversarial threats that remote sensing image recognition models may confront and to provide an effective means for evaluating the robustness of the models, this paper takes the adversarial examples for remote sensing image recognition as the research goal and systematically studies vanishing attacks against a remote sensing image object detection model. To solve the problem of difficult attack implementation on remote sensing image object detection, adversarial attack adaptation methods based on interpolation scaling and patch perturbation stacking are proposed in this paper, which realizes the adaptation of classical attack algorithms. We propose a hot restart perturbation update strategy and the joint attack of the first and second stages of the two-stage remote sensing object detection model is realized through the design of the attack loss function. For the problem of the modification cost of global pixel attack being too large, a local pixel attack algorithm based on sensitive pixel location is proposed in this paper. By searching the location of the sensitive pixels and constructing the mask of attack area, good local pixel attack effect is achieved. Experimental results show that the average pixel modification rate of the proposed attack method decreases to less than 4% and the vanishing rate can still be maintained above 80%, which effectively achieves the balance between attack effect and attack cost.

IPv6 is a next-generation IP protocol that replaces IPv4. It not only expands the number of network address resources but also solves the problem of multiple access devices connected to the Internet. While IPv6 has brought excellent convenience to the public, related security issues have gradually emerged, and an assessment of the security situation in IPv6 has also become more important. Unlike passive defense, the honeypot is a security device for active defense. The real network application and the fake network application, disguised by the honeypot, are located on a similar subnet, and provide a network application service; but, in both cases, behavior logs from unauthorized users are caught. In this manner, and to protect web-based applications from attacks, this article introduces the design and implementation of a web-based honeypot that includes a weak password module and an SQL inject module, which supports the IPv6 network to capture unauthorized access behavior. We also propose the Security Situation Index (SSI), which can measure the security situation of the network application environment. The value of SSI is established according to the different parameters that are based on honeypots. There is a firewall outside the test system environment, so the obtained data should be used as the real invasion data, and the captured behavior is not a false positive. Threats can be spotted smartly by deploying honeypots; this paper demonstrates that the honeypot is an excellent method of capturing malicious requests and can be measured with the SSI of the whole system. According to the information, the administrator can modify the current security policy, which can improve the security level of a whole IPv6 network system.