GIFT, as a lightweight block cipher algorithm, is mainly suitable for resource-constrained environments, such as the Internet of Things, to achieve efficient cryptographic communication. For the diffusion characteristics of round function of the GIFT algorithm, this article proposes two byte-based differential fault attack models. Theoretically, the first and second models require 53.44 and 12.42 byte faults to recover the master key. Experimental results show that the first and second models require about 79 and 16 byte faults, respectively, to recover the master key. The models proposed in this article have made significant breakthroughs in terms of both the attack range and the number of required faults, which provide important guidance for algorithmic security research and the design of fault-tolerant mechanisms for IoT devices.

AEGIS is a lightweight cipher for resource constrained environments such as the Internet of Things (IoT), which is one of the seven algorithms that ultimately won the CAESAR competition, and has become a focus of cryptographic research. Differential fault attack is a method to recover important secret data by researching the difference in state before and after injecting a fault, which is one of the most serious threats to lightweight cryptographic algorithms. This paper propose differential fault attacks on AEGIS based on the random bit fault model, which theoretically respectively requires only 141, 176, and 246 random bit faults to fully recover the intermediate state of AEGIS-128, AEGIS-256, and AEGIS-L, which are far superior to the known results in terms of the number of faults, computational complexity, and difficulty of models. The attack method on AEGIS contributes to assessing the security of IoT devices and systems, while also guiding the design of IoT security defense strategies. The approach proactively detects vulnerabilities in lightweight cryptographic algorithms implemented on resource-constrained devices such as sensors and smart home chips, thus driving enhancements in algorithms and hardware, and has great significance for the security of IoT platforms.

Thread-Level Speculation (TLS) is a thread-level automatic parallelization technique to accelerate sequential programs on multi-core. Thread partition is a core step for this technique, so how to automatically and effectively partition an unknown program is a key to improve the efficiency of this technique. In order to solve this problem, this paper proposes a Back Propagation Neural Network based threading partition approach(TPoBP). This approach is used to study the implicit knowledge of partition in the sample set to guide the partition for unknown programs. The knowledge in the sample is composed of the characteristics of the sample and the partition scheme, which are used as the input and output of the network to train the network until the specified accuracy is reached. During validation period, the trained network makes use of profiling information (obtained during pre-execution) of a validation program as input, and runs to obtain the predicted partition scheme for the validation program. Experimental results show that TPoBP can effectively predict the partition schemes of validation programs, and average prediction accuracy almost reaches 0.7. Moreover, these predicted schemes are further used to guide partition for validation programs, and Olden benchmarks reach a maximum 11.8% speedup improvement. Experiments demonstrate that the model proposed by this paper is effective to predict partition scheme for unseen programs.

The tight cyber–physical coupling in industrial control system (ICS) makes it vulnerable to attacks, where attackers can exploit system vulnerabilities to cross domain boundaries, causing significant losses. A novel quantitative risk assessment framework for ICS is proposed, integrating both the cyber domain and the physical domain to address potential risk assessment issues. First, an entropy-weighted technique for order preference by similarity to ideal solution method introducing triangular fuzzy numbers is proposed to solve the vulnerability index for multiattribute decision making to obtain the a prior probability. Second, the risk propagation mechanism of complex network topology and device interaction under ICS was studied, and the posterior probability model based on susceptible-exposed-infectious-recovered was designed to dynamically capture the propagation characteristics of risks in the network. Finally, the safety loss level is defined based on standard criteria to achieve quantitative risk assessment of ICS. The proposed risk assessment model was experimentally validated on the SWaT testbed, with results confirming its feasibility and effectiveness.

Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method based on local differential privacy (LDP-APRR). LDP-APRR determines the sensitive level through the user scoring strategy, introduces the concept of sensitive weights for adaptive allocation of privacy budget, and realizes the personalized privacy protection of sensitive attributes and attribute values. To verify the distorted data availability, LDP-APRR is applied to frequent items mining scenarios and compared with mining associations with secrecy konstraints (MASK), and grouping-based randomization for privacy-preserving frequent pattern mining (GR-PPFM). Results show that the LDP-APRR achieves personalized protection of sensitive attributes and attribute values with user participation, and the maxPrivacy and avgPrivacy are improved by 1.2% and 4.3%, respectively, while the availability of distorted data is guaranteed.

Industrial Internet plays an important role in key critical infrastructure sectors and is the target of different security threats and risks. There are limitations in many existing attack detection approaches, such as function redundancy, overfitting, and low efficiency. A combinatorial optimization method—Lagrange multiplier—is designed to optimize the underlying feature screening algorithm. The optimized feature combination is fused with random forest and XG-Boost selected features to improve the accuracy and efficiency of attack feature analysis. Using both the UNSW-NB15 and natural gas pipeline datasets, we evaluate the performance of the proposed method. It is observed that the influence degrees of the different features associated with the attack behavior can result in the binary classification attack detection increasing to 0.93 and the attack detection time reducing by 6.96 times. The overall accuracy of multi-classification attack detection is also observed to improve by 0.11. We also observe that nine key features of attack behavior analysis are essential to the analysis and detection of general attacks targeting the system, and by focusing on these features one could potentially improve the effectiveness and efficiency of real-time critical industrial system security. In this article, the CICDDoS2019 and CICIDS2018 datasets are used to prove the generalization. The experimental results show that the proposed method has good generalization and can be extended to the same type of industrial anomaly datasets.

Supporting students to make their reflections visible and accessible during the inquiry-based process can enhance the learning outcomes and foster reflective thinking. This research examines how technology can play a role in scaffolding students to create contextualized reflection-in-action products which can contribute to reflection-on-action in design-based learning classroom. In this paper, we present the design of a multimedia tool called ReflectionScope, which offers contextualized scaffolding to prompt students to monitor their action and create reflective videos using the digital video-camera or visual “scope” attached. Twenty-one secondary school students (aged 13) used ReflectionScope in a two-weeks design-based learning class. An analysis of the reflective video’s students created during this class and the post-interview, shows that students articulate their reflection-in-action in a structured way with context-rich information. Students perceived that the videos are beneficial for retrieving and understanding the contextual reflection-in-action moments for reflection-on-action. Based on our findings, we propose design principles that can contribute to designing for reflection practices which can be enhanced by media-technology in real-world inquiry-based learning environments.