A Modified DBSCAN Algorithm for Anomaly Detection in Time-series Data with Seasonality

Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour. The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data. In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality. For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets. From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data. The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data. Using normal DBSCAN we are able to get 19 (2.16%) anomaly points. While using the modified approach for DBSCAN we are able to get 42 (4.79%) anomaly points. In comparison we can say that we are able to get 2.11% more anomalies using the modified DBSCAN approach. Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.

An Automated Behaviour-Based Clustering of IoT Botnets

The leaked IoT botnet source-codes have facilitated the proliferation of different IoT botnet variants, some of which are equipped with new capabilities and may be difficult to detect. Despite the availability of solutions for automated analysis of IoT botnet samples, the identification of new variants is still very challenging because the analysis results must be manually interpreted by malware analysts. To overcome this challenge, we propose an approach for automated behaviour-based clustering of IoT botnet samples, aimed to enable automatic identification of IoT botnet variants equipped with new capabilities. In the proposed approach, the behaviour of the IoT botnet samples is captured using a sandbox and represented as behaviour profiles describing the actions performed by the samples. The behaviour profiles are vectorised using TF-IDF and clustered using the DBSCAN algorithm. The proposed approach was evaluated using a collection of samples captured from IoT botnets propagating on the Internet. The evaluation shows that the proposed approach enables accurate automatic identification of IoT botnet variants equipped with new capabilities, which will help security researchers to investigate the new capabilities, and to apply the investigation findings for improving the solutions for detecting and preventing IoT botnet infections.

Hubs for Circularity: Geo-Based Industrial Clustering towards Urban Symbiosis in Europe

Since the Green Deal, ambitious climate and resource neutrality goals have been set in Europe. Here, process industries hold a unique position due to their energy and material transformation capabilities. They are encouraged to develop cross-sectorial hubs for achieving not only climate ambition, but also joining a circular economy through urban–industrial symbiosis with both business and community stakeholders. This research proposes a data-based approach to identify potential hub locations by means of cluster analysis. A total of three different algorithms are compared on a set of location and pollution data of European industrial facilities: K-means, hierarchical agglomerative and density-based spatial clustering. The DBSCAN algorithm gave the best indication of potential locations for hubs because of its capacity to tune the main parameters. It evidenced that predominately west European countries have a high potential for identifying hubs for circularity (H4Cs) due to their industrial density. In Eastern Europe, the industrial landscape is more scattered, suggesting that additional incentives might be needed to develop H4Cs. Furthermore, industrial activities such as the production of aluminium, cement, lime, plaster, or electricity are observed to have a relatively lower tendency to cluster compared with the petrochemical sector. Finally, further lines of research to identify and develop industrial H4Cs are suggested.

Research on Density-Based K-means Clustering Algorithm

Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.

An improved density-based approach to risk assessment on railway investment

PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Research on the Deployment Strategy of Blockchain Nodes in the Agricultural Product Blockchain Traceability System

The agricultural product traceability system based on blockchain can monitor the entire growth cycle of agricultural products, trace it at any time, and cannot tamper with information without authorization. However, the energy consumption of the entire system is relatively high due to the introduction of blockchain technology. In order to alleviate the problem of high overall energy consumption, we are trying to reduce the communication power consumption between terminal sensors and the full nodes of the blockchain. We use the K-means algorithm, the DBSCAN algorithm and the improved DK fusion algorithm we proposed to deploy blockchain full nodes to the agricultural products sensors that have been determined to reduce the communication power consumption of the sensor terminals and improve the coverage of the full nodes to the terminal sensors.