A rough set-based effective rule generation method for classification with an application in intrusion detection

Abstract: In this paper, we use Rough Set Theory (RST) to address the important problem of generating decision rules for data mining. In particular, we propose a rough set-based approach to mine rules from inconsistent data. It computes the lower and upper approximations for each concept, and then builds concise classification rules for each concept satisfying required classification accuracy. Estimating lower and upper approximations substantially reduces the computational complexity of the algorithm. We use UCI ML Repository data sets to test and validate the approach. We also use our approach on network intrusion data sets captured using our local network from network flows. The results show that our approach produces effective and minimal rules and provides satisfactory accuracy. Keywords: rough set; LEM2; inconsistency; minimal; redundant; PCS; intrusion detection; network flow data. Reference to this paper should be made as follows: Gogoi, P., Bhattacharyya, D.K. and Kalita, J.K. (2013) 'A rough set-based effective rule generation method for classification with an application in intrusion detection', Int

Adaptation Strategies for Climate Variability in the High Rainfall Zone of India, Assam

The NICRA project is being implemented in two villages viz., Chamua (since 2010–2011) and Ganakdalani (since 2012–2013 till 2016–2017), which are situated in the west of Lakhimpur district of North Bank Plains Zone of Assam. Chamua village is situated in Kherajkhat Mauza (Taluka), which is 45 km away from North Lakhimpur, the headquarter of district Lakhimpur. On the other hand, Ganakdoloni is situated at Dhalpur Mauza, situated 60 km away from North Lakhimpur and 15 km away from the local township Narayanpur. During 2017–2018 four villages viz., Jakaipelua, Borbali, Borkhet, and Nogaya were adopted under the project. Analysis of long-term rainfall data confirmed the significant decreasing trend of annual as well as monsoonal rainfall in both the Brahmaputra and Barak basins of Assam, India. Variability of rainfall has been increasing in terms of the increased frequency of high-intensity rains and the reduced number of rainy days, leading to localized flash floods and the occurrence of multiple dry spells. Mean season-wise rainfall 2011–2021 indicates long dry periods during the winter season, leading to prolonged dry spells affecting crop growth. About 69% of total rainfall (average annual rainfall of Assam is 2000 mm) is received during the monsoon season, resulting in flash floods leading to crop damage. Out of 12 years of investigation, 10 years are deficit years, resulting in crop stress both during the monsoon and post-monsoon period. Preparation and implementation of real-time crop contingencies are important in responding to weather aberrations in different strategies like preparedness, real-time response, etc. Identification of various adaptation strategies, including climate-resilient crops and cultivars, rainwater harvesting and recycling, efficient energy management through farm mechanization, dissemination of weather information, and weather-based agro-advisories to farmers in a real-time basis, is important adaptation technologies for building climate-resilient agriculture. The study showed that adaption of climate-resilient crop and cropping system and use of harvested rainwater resulted in a 12 to 30% increase in yield observed by the cultivation of high-yielding rice varieties (HYVs) (Ranjit, Gitesh, Mahsuri, etc.) when sown in time (before 15th June) over late sowing conditions (after 20th June). In the case of early season drought, replacement of long duration traditional varieties with short duration HYV and life-saving irrigation using harvested rainwater increased yield by about 59% (short duration var. Dishang) over non-irrigated fields. In case of mid-season and terminal drought, application of an additional dose of 22 kg ha−1 MOP at maximum tillering to grain growth period an increase in yield of about 33% (Ranjit), 32% (Gitesh), 64% (Shraboni), and 57.5% (Mulagabharu) has been observed over farmers’ practice. In highly flood-affected areas under lowland situations replacement of submergence tolerant varieties (Jalashree and Jalkuwari) with traditional deepwater rice varieties resulted in reduced crop loss due to the genetic trait of the deepwater rice, which can withstand water logging for a long period. With an increase in the level of mechanization through the use of machinery available in the custom hiring center the human and animal hour requirement for paddy cultivation was reduced from 795 to 350 hrha−1 and 353 to 23 hrha−1, respectively. Alternate land use in terms of low-cost poly house, vermicompost production, and mushroom cultivation also resulted in nutritional security and generation of higher income for the farmer

MCA techniques on health awareness among the Bodo Women of Udalguri District, Assam

According to WHO report, “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infinity”,[1]. Consequently further developing health status of everything is a piece of population control program. Besides health status can improve or fall apart dependent on the attitudes and activities taken towards health. In demographic analysis, we come across some interesting problems which involve simultaneous consideration of several predictor variables to study their relationships with a dependent variable. Sometimes we are interested to know how well all the variables taken together explain variation in the dependent variable and also how each predictor variable is related to the dependent variable. In this paper, an endeavor has been made to examine the health status among the respondents of Bodo women of Udalguri District, Assam using "Multiple Classification Analysis, MCA". In fact, MCA is a technique for examining the inter-relationship between several predictor variables and dependent variables within the context of additive model. It may be easily explained as multiple regressions with dummy variables and ‘adjusted derivatives’ along with ‘unadjusted effects’

MCA techniques on health awareness among the Bodo women of Udalguri District, Assam

According to WHO report, “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infinity”,[1]. Consequently further developing health status of everything is a piece of population control program. Besides health status can improve or fall apart dependent on the attitudes and activities taken towards health. In demographic analysis, we come across some interesting problems which involve simultaneous consideration of several predictor variables to study their relationships with a dependent variable. Sometimes we are interested to know how well all the variables taken together explain variation in the dependent variable and also how each predictor variable is related to the dependent variable. In this paper, an endeavor has been made to examine the health status among the respondents of Bodo women of Udalguri District, Assam using "Multiple Classification Analysis, MCA". In fact, MCA is a technique for examining the inter-relationship between several predictor variables and dependent variables within the context of additive model. It may be easily explained as multiple regressions with dummy variables and ‘adjusted derivatives’ along with ‘unadjusted effects’

Aluminium Chloride–Catalyzed Synthesis of 4-Benzyl Cinnolines from Aryl Hydrazones

<div><p></p><p>An efficient synthesis of 4-benzyl cinnolines from aryl phenylallylidene hydrazone is described. In this report aluminium chloride as a Lewis acid catalyst and toluene as a solvent are used for the synthesis. This method is expected to more advantageous than the other reported methods of synthesis of the cinnoline rings because of its low cost, better yield, and benign reaction conditions.</p> <p>[Supplementary materials are available for this article. Go to the publisher's online edition of <i>Synthetic Communications</i>® for the following free supplemental resource(s): Full experimental and spectral details.]</p> </div

Supervised Anomaly Detection using Clustering-based Normal Behaviour Modeling

In this paper we present a clustering based supervised anomaly detection technique. A set of training data consisting of normal data only are divided into clusters which are represented by their profiles to form the normality model. Any deviation from the normality model is treated as attack. Methods for clustering, training and detection are provided. Our technique produces good results for KDD CUP 1999 datasets. Performance measuring methods like Recall, Precision, and F1measure for good clustering are applied. Measurings of performance are evaluated with important algorithms of ANI

Efficient Rule Set Generation using Rough Set Theory for Classification of High Dimensional Data

In this paper, a rough set theory (RST) based approach is proposed to mine concise rules from inconsistent data. The approach deals with inconsistent data. At first, it computes the lower and upper approximation for each concept, then adopts a learning from an algorithm to build concise classification rules for each concept satisfying the given classification accuracy. Lower and upper approximation estimation is designed for the implementation, which substantially reduce the computational complexity of the algorithm. UCI ML Repository datasets are used to test and validate the proposed approach. We have also used our approach on network intrusion dataset captured using our local network from network flow. The results show that our approach produces effective and minimal rules and provide satisfactory accuracy over several real life dataset