Improved Performance of Network Attack Detection using Combination Data Mining Techniques

Network Attack detection is very important mechanism for detecting attack in computer networks. Data mining techniques play very important role in detecting intrusions in computer networks. Intrusions can damage to the data and compromise integrity and confidentiality and availability of the data. Intrusions are the activities that violate the security policy of system. Intrusion Detection is the process used to identify network attack. Network security is to be considered as a major issue in recent years, since the computer network keeps on expanding every day. A Network Attack Detection System (NADS) is a system for detecting intrusions and reporting to the authority or to the network administration. Data mining techniques have been applied in many fields like Network Management, Education, Science, Business, Manufacturing, Process control, and Fraud Detection. Data mining algorithms like J48, Randam Forest ,Random Tree, Hoefding Tree and Rep Tree are used to build intrusion detection models using KDD CUP 1999. The performance of network attack detection model is evaluated using KDD CUP 1999 test dataset using series of experiments and measured using correct classi?cation and detection of attack. The combination of data mining algorithm will increase performance of network attack detection i.e false positive and false negative, novel or unknown attacks

Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-hoc Network

This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs

General Noise-Perturbed Superior Julia Sets

The aim of this paper is to offer an integrated approach to study the additive and multiplicative noises with respect to perturbations in superior Julia sets. External and internal perturbations in superior Julia sets are analyzed under the mixed effect of additive and multiplicative noises

Antibacterial finish of textile using papaya peels derived silver nanoparticles

The present study is aimed at the extracellular synthesis of highly stable silver nanoparticles for the development of nanosafe textile using the extracts of yellow papaya peel. Fabric is treated with nanoparticles using dip and dry method to observe the effect of antibacterial activity. The synthesized nanoparticles are also characterized and quantified. Due to their potent antibacterial activity, papaya peels derived silver nanoparticles can be incorporated into fabrics and the manufacturers can make textiles free from spoilage by microorganisms

Transforming Health Systems Towards Holistic Outcomes in the G20 and Beyond

The transition from the COVID-19 pandemic offers a strategic opportunity to build back better and use the disruption to transform health systems. This Policy Brief calls on the G20 to take the lead in health systems transformation. Health systems need to reorient to primary health care to address fragmentation and deliver health services in a comprehensive manner. Further, the promotion of cultural integration in health systems to move beyond reductionism and elevate Indigenous practices on equal footing with Western traditions will help achieve a more holistic approach in public health interventions. Finally, there should be effective community participation in G20 policymaking that places people at the centre of health systems and ensures that health services are responsive to communities’ needs. India’s G20 presidency offers a unique opportunity to bring the Global North and South together in shaping the future of health systems in the true spirit of Vasudhaiva Kutumbakam—One Earth. One Family. One Future

Minimum relevant features to obtain AI explainable system for predicting breast cancer in WDBC

The potential to explain why a machine learning model produces a certain prediction in incomprehensible terms is becoming increasingly crucial, as it provides accountability and confidence in the algorithm's decision-making process. The interpretation of complex models is difficult. Various approaches to dealing with this issue are being offered. These problems are typically handled in tree ensemble methods by assigning priority levels to input features globally or for a specific prediction.  We show that current feature attribution approaches are inconclusive, and develop solutions using SHAP (SHapley Additive Explanation) values, LIME (Local Interpretable Model-Agnostic Explanations), and the Skope Rules package. We employ feature selection methods from SHAP and LIME in this work, which uses the Breast cancer Wisconsin data sets. In the suggested method, features are chosen at the first level of feature selection using Decision tree entropy values. Based on the SHAP and LIME reports, level 2 features are chosen from fewer options. The features are tested on a Decision Tree (DT) model and a DT and Support Vector Machine (SVM) ensemble. Experiments suggest that the ensemble works better as compared to DT. We have also used the Skope Rules package to generate global rules for generalization

Effect of Stochastic Noise on Superior Julia Sets

Julia sets are considered one of the most attractive fractals and have wide range of applications in science and engineering. The strong physical meaning of Mandelbrot and Julia sets is broadly accepted and nicely connected by Christian Beck (Physica D 125(3–4):171–182, 1999) to the complex logistic maps, in the former case, and to the inverse complex logistic map, in the latter. Argyris et al. (Chaos Solitons Fractals 11(13):2067–2073, 2000) have studied the effect of noise on Julia sets and concluded that Julia sets are stable for noises of low strength, and a small increment in the strength of noise may cause considerable deterioration in the configuration of the Julia sets. It is well-known that the method of function iterates plays a crucial role in discrete dynamics utilizing the techniques of fractal theory. However, recently Rani and Kumar (J. Korea Soc. Math. Edu. Ser. D: Res. Math. Edu. 8(4):261–277, 2004) introduced superior iterations as a generalization of function iterations in the study of Julia sets and studied superior Julia sets. This technique is further utilized to study effectively new Mandelbrot sets and related properties (see, for instance, Negi and Rani, Chaos Solitons Fractals 36(2):237–245, 2008; 36(4):1089–1096, 2008, Rani and Kumar, J. Korea Soc. Math. Edu. Ser. D: Res. Math. Edu. 8(4):279–291, 2004). The intent of this paper is to study certain effects of noise on superior Julia sets. We find that the superior Julia sets are drastically more stable for higher strength of noises than the classical Julia sets. Finally, we make a humble attempt to discuss some applications of superior orbit in discrete dynamics and of superior Julia sets in particle dynamics