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Binary division fuzzy C-means clustering and particle swarm optimization based efficient intrusion detection for e-governance systems
Sunil K. Muttoo, Saibal K. Pal, 2016, original scientific article

Abstract: With the rapid rise of technology, many unusual and unwanted patterns have been observed in the communication network andrespective systems. This may be attributed to the increase of external threats that cause many security concerns. Such anomalies and unusual behavior lead to a strong need of studying and designing the Intrusion Detection Systems and Clustering. Currently,a variety of clustering methods and their combinations are used to develop an efficient intrusion detection system, but some metrics like low detection rate and high false alarm rate make these models unsatisfactory. The problem of local minima for clustering technique makes their search ability less efficient. An evolutionary technique called particle swarm optimization algorithm, that is based on swarm intelligence, shows a high global maxima search capability. In this paper, these two techniques have been combined to present a novel approach called fuzzy based particle swarm algorithm for the implementation of intrusion detection system. The experiment was conducted on a new data set called Kyoto data set with more number of anomalies. The obtained results were compared with two traditional clustering techniques based on K-Means and Fuzzy C-Means. It was observed that the proposed algorithm outperformed the other two traditional methods on the basis of the Detection Rate and False Alarm rate. In past some researchers have presented the combination of Fuzzy Based Particle Swarm Optimization algorithm to improve the intrusion detection rate,but this rate has been further improved because the algorithm performance depends on the termination condition and the fitness function value which are new in the proposed algorithm. Moreover, cluster numbers have been considered differently in the past, whereas the proposed algorithm works only on binary clustering.
Found in: ključnih besedah
Keywords: intrusion detection, fuzzy C-means clustering, particle swarm optimization, detection rate, e-governance
Published: 01.04.2021; Views: 512; Downloads: 0
.pdf Fulltext (785,04 KB)

Data Analytics based Techniques for Improvement of E-Governance in Developing Nations
Rajan Gupta, 2019, unpublished conference contribution

Abstract: United Nation’s E-Governance Development Index is a development assessment index for all the nations around on the E-Governance front. Every country is ranked on the basis of a quantitative parameter derived out of few important components. But such development assessment index is missing at regional level in a country so that regional development can be assessed and work can be monitored. Few countries have local assessment models but are not exhaustive enough which can be used for development assessment and further development plan formation. Therefore, there is a need for this study to develop such assessment framework and develop approaches to have a meaningful contribution in improvement of E-Governance in the country at regional level. After the assessment of the regions on the development front of E-Governance, the improvement techniques must be defined for the weak parameters, so that regional development can be enhanced. For the experiment purpose, India has been chosen as the experiment country for which datasets has been used from Indian E-Governance transactions. This problem is important to be addressed because an overall quantitative measure of E-Governance development of the country will help in improving overall E-Governance rankings at world level, attract better investors, and will help the government to prepare a more inclusive plan on the development front. Most of the studies in literature are citizen centric and thus are not fit for development assessment. The current study has not only developed a framework but has also analyzed various components related to it in detail and suggested the way ahead for E-Governance in the country
Found in: ključnih besedah
Summary of found: ...E-Governance, Development Assessment, Location Allocation, Intrusion Detection...
Keywords: E-Governance, Development Assessment, Location Allocation, Intrusion Detection
Published: 02.04.2021; Views: 510; Downloads: 0
.pdf Fulltext (932,80 KB)

Development of e-governance in an emerging economy like India
Saibal K. Pal, Rajan Gupta, Sunil K. Muttoo, 2017, published scientific conference contribution

Abstract: In this paper, we describe the key research questions handled during the doctoral work done in the area of E-Governance. The five research questions in the study are related to the concepts like Development of E-Governance & its assessment, Infrastructure management to reach out to maximum citizens, Various types of Security concerns faced during the E-Governance Development, Analyzing the E-Governance transaction pattern to capture citizen's interest, and finally the way ahead for the E-Governance development through the route of efficiency and optimization in the service designing. The methodology adopted and results obtained for various research questions are discussed at high level. Some portion of the current work is still in progress.
Found in: ključnih besedah
Summary of found: ...e-governance, location allocation, common service centers, intrusion detection system, security, analytics...
Keywords: e-governance, location allocation, common service centers, intrusion detection system, security, analytics
Published: 02.04.2021; Views: 486; Downloads: 20
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Design & analysis of clustering based intrusion detection schemes for e-governance
Saibal K. Pal, Rajan Gupta, Sunil K. Muttoo, 2016, published scientific conference contribution

Abstract: The problem of attacks on various networks and information systems is increasing. And with systems working in public domain like those involved under E-Governance are facing more problems than others. So there is a need to work on either designing an altogether different intrusion detection system or improvement of the existing schemes with better optimization techniques and easy experimental setup. The current study discusses the design of an Intrusion Detection Scheme based on traditional clustering schemes like K-Means and Fuzzy C-Means along with Meta-heuristic scheme like Particle Swarm Optimization. The experimental setup includes comparative analysis of these schemes based on a different metric called Classification Ratio and traditional metric like Detection Rate. The experiment is conducted on a regular Kyoto Data Set used by many researchers in past, however the features extracted from this data are selected based on their relevance to the E-Governance system. The results shows a better and higher classification ratio for the Fuzzy based clustering in conjunction with meta-heuristic schemes. The development and simulations are carried out using MATLAB.
Found in: ključnih besedah
Keywords: particle swarm optimization, intrusion detection, anomaly detection, intrusion detection system, network intrusion detection
Published: 02.04.2021; Views: 571; Downloads: 2
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