The Intelligent computing system, described to be a collection of the connected device working in mutual understanding to attain a particular purpose, is an incorporation of artificial intelligence and the computational intelligence, and are employed in variety of applications. The paper presents the survey on the data mining algorithms and the techniques that could be employed with the intelligent computing system, presenting a basic conception of the data mining along with the prominent algorithms of the data mining and the classification of its techniques, further the survey concludes with the challenges included in the overview of the survey done along with the future enhancement in the research that analyses the data mining techniques in the intelligent computing applications.

https://www.irojournals.com/tcsst/V1/I1/02.pdf

Survey of data mining algorithm’s for intelligent computing system

Spectral clustering has been demonstrated to often outperform K-means clustering in real applications because it improves the similarity measurement of K-means clustering. However, previous spectral clustering method still suffers from the following issues: 1) easily being affected by outliers; 2) constructing the affinity matrix from original data which often contains redundant features and outliers; and 3) unable to automatically specify the cluster number. This paper focuses on address these issues by proposing a new clustering algorithm along with the technique of half-quadratic optimization. Specifically, the proposed method learns the affinity matrix from low-dimensional space of original data, which is obtained by using a robust estimator to remove the influence of outliers as well as a sparsity regularization to remove redundant features. Moreover, the proposed method employs the l2,1-norm regularization to automatically learn the cluster number according to the data distribution. Experimental results on both synthetic and real data sets demonstrated that the proposed method outperforms the state-of-the-art methods in terms of clustering performance.

Spectral clustering via half-quadratic optimization

The amount of data generated by the current interconnected world is immeasurable, and a large part of such data is publicly available, which means that it is accessible by any user, at any time, from anywhere in the Internet. In this respect, Open Source Intelligence (OSINT) is a type of intelligence that actually benefits from that open nature by collecting, processing and correlating points of the whole cyberspace to generate knowledge. In fact, recent advances in technology are causing OSINT to currently evolve at a dizzying rate, providing innovative data-driven and AI-powered applications for politics, economy or society, but also offering new lines of action against cyberthreats and cybercrime. The paper at hand describes the current state of OSINT and makes a comprehensive review of the paradigm, focusing on the services and techniques enhancing the cybersecurity field. On the one hand, we analyze the strong points of this methodology and propose numerous ways to apply it to cybersecurity. On the other hand, we cover the limitations when adopting it. Considering there is a lot left to explore in this ample field, we also enumerate some open challenges to be addressed in the future. Additionally, we study the role of OSINT in the public sphere of governments, which constitute an ideal landscape to exploit open data.

/pdf/the-not-yet-exploited-goldmine-of-osint-opportunities-open-54jllpzd7z.pdf

The Not Yet Exploited Goldmine of OSINT: Opportunities, Open Challenges and Future Trends

As a brand-new network structure, WLAN mesh network has high capacity, high speed and wide coverage.But security issues have become a main hindrance to enlarge WLAN mesh network business. This paper provides a new Blockchain-Based Authentication Protocol for WLAN Mesh Security Access Scheme, to resolve the issues of requiring key delivery and central server during IEEE 802.11i and IEEE 802.11s authentication. This method takes the user’s authentication request as a transaction, considers all the authentication records in the mesh network as the public ledger and realizes the effective monitoring of the malicious attack. Lastly this paper analyzes the security of the Protocol in detail,and proves this method can solve the dependence of the authentication node on PKI and CA.

http://www.tspsub.com//uploads/attached/file/20190110/20190110011718_89090.pdf

A Blockchain-Based Authentication Protocol for WLAN Mesh Security Access Scheme

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

/pdf/review-of-data-fusion-methods-for-real-time-and-multi-sensor-3jy63u90va.pdf

Jian Weng

Papers

Review on mining data from multiple data sources

Deep understanding of big multimedia data