The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.

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Internet of Things: Security and Solutions Survey

This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

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Simplified Phasmatodea population evolution algorithm for optimization

By studying the causality contained in financial texts, we can further reveal more potential laws of economic activities, such as “factors promoting stable and healthy economic development,” “The central bank's use of the loan window to issue money will increase the probability of inflation,” “The consequence of overcapacity is a decline in product prices,” and so on. Causal sentence recognition usually includes two sub-tasks: one is to design rules or templates to find candidate causal sentences; the other is to design a classifier to sort candidate causal sentences to finally identify the causal sentence. This article first focuses on the characteristics of complex sentence patterns of multiple causes and one effect, multiple effects and one cause, and multiple causes and multiple effects in financial review texts, and provides a relatively complete candidate causal sentence identification rules, which can identify both simple causal sentences and complex causal sentences. A BERT-CNN (Bidirectional Encoder Representations from Transformers-Convolutional Neural Networks) combination model is proposed for the classification of candidate causal sentences. On the one hand, by adding a CNN (Convolutional Neural Networks) structure to the specific task layer of the BERT (Bidirectional Encoder Representations from Transformers) model to capture important local information in the text. On the other hand, in order to make better use of the self-attention mechanism, the local text representation and the output of the BERT are input together in the multi-layer transformer encoder. A complete representation of the text is finally obtained through a single-layer transformer encoder. Experimental results show that our model is significantly better than the most advanced baseline model, with a 5.31 pts improvement in F1 over previous analyzers.

Financial causal sentence recognition based on BERT-CNN text classification

Mining weighted sequential patterns in incremental uncertain databases

Discovering high utility-occupancy patterns from uncertain data

A smart grid (SG) is an advanced power grid system deployed in a cloud center and smart meters (at the consumer end) that provides higher reliability, better data protection, improved power efficiency, automatic monitoring, and effective management of power consumption. However, an SG also poses certain challenges that need to be addressed. For example, data provided by a smart meter are time-sensitive and cannot handle high latency in an SG. Moreover, a smart meter depends on memory, energy, and other factors. Besides, the security between a cloud center and a smart meter is a critical issue that needs to be resolved. Edge computing, an extension of cloud computing deployed in an edge network between a cloud center and the end devices, is an efficient solution to the aforementioned issues. Therefore, in this study, we propose a secure mutual authentication protocol based on edge computing for use in an SG.

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Lightweight authentication protocol in edge-based smart grid environment

OHUQI: Mining on-shelf high-utility quantitative itemsets

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

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Decentralized Private Information Sharing Protocol on Social Networks

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.

Lili Chen

Papers

Discovering high utility-occupancy patterns from uncertain data

Lightweight authentication protocol in edge-based smart grid environment

OHUQI: Mining on-shelf high-utility quantitative itemsets

Decentralized Private Information Sharing Protocol on Social Networks

Anomaly Rule Detection in Sequence Data.