Jitendra Agrawal

Survey on Anomaly Detection using Data Mining Techniques

Abstract In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to attack. Although various techniques or applications are available to protect data, loopholes exist. Thus to analyze data and to determine various kind of attack data mining techniques have emerged to make it less vulnerable. Anomaly detection uses these data mining techniques to detect the surprising behaviour hidden within data increasing the chances of being intruded or attacked. Various hybrid approaches have also been made in order to detect known and unknown attacks more accurately. This paper reviews various data mining techniques for anomaly detection to provide better understanding among the existing techniques that may help interested researchers to work future in this direction.

A Survey on Anomaly Detection in Network Intrusion Detection System Using Particle Swarm Optimization Based Machine Learning Techniques

The progress in the field of Computer Networks & Internet is increasing with tremendous volume in recent years. This raises important issues with regards to security. Several solutions emerged in the past which provide security at the host or network level. These traditional solutions like antivirus, firewall, spyware & authentication mechanism provide security to some extends but they still face the challenges of inherent system flaws & social engineering attacks. Some interesting solution emerged like Intrusion Detection & Prevention Systems but these too have some problems like detecting & responding in real time & discovering novel attacks. Several Machine Learning techniques like Neural Network, Support Vector Machine, Rough Set etc. Were proposed for making an efficient and Intelligent Network Intrusion Detection System. Also Particle Swarm Optimization is currently attracting considerable interest from the research community, being able to satisfy the growing demand of reliable & intelligent Intrusion Detection System (IDS). Recent development in the field of IDS shows that securing the network with a single technique proves to be insufficient to cater ever increasing threats, as it is very difficult to cope with all vulnerabilities of today’s network. So there is a need to combine all security technologies under a complete secure system that combines the strength of these technologies under a complete secure system that combines the strength of these technologies & thus eventually provide a solid multifaceted well against intrusion attempts. This paper gives an insight into how Particle Swarm Optimization and its variants can be combined with various Machine Learning techniques used for Anomaly Detection in Network Intrusion Detection System by researchers so as to enhance the performance of Intrusion Detection System.

Machine learning techniques for data mining: A survey

Data mining (DM) is a most popular knowledge acquisition method for knowledge discovery. Classification is one of the data mining (machining learning) technique that maps the data into the predefined class and groups. It is used to predict group membership for data instance. There are many areas that adapt Data Mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents the various classification techniques including decision tree, Support vector Machine, Nearest Neighbor etc. This survey provides a comparative Analysis of various classification algorithms.

Neural Network Techniques for Cancer Prediction: A Survey☆

Abstract Cancer is a dreadful disease. Millions of people died every year because of this disease. It is very essential for medical practitioners to opt a proper treatment for cancer patients. Therefore cancer cells should be identified correctly. Neural networks are currently a burning research area in medical science, especially in the areas of cardiology, radiology, oncology, urology and etc. In this paper, we are surveying various neural network technologies for classification of cancer. The main aim of this survey in medical diagnostics is to guide researchers to develop most cost effective and user friendly systems, processes and approaches for clinicians.

A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

In multi-label classification problems, every instance is associated with multiple labels at the same time. Binary classification, multi-class classification and ordinal regression problems can be seen as unique cases of multi-label classification where each instance is assigned only one label. Text classification is the main application area of multi-label classification techniques. However, relevant works are found in areas like bioinformatics, medical diagnosis, scene classification and music categorization. There are two approaches to do multi-label classification: The first is an algorithm-independent approach or problem transformation in which multi-label problem is dealt by transforming the original problem into a set of single-label problems, and the second approach is algorithm adaptation, where specific algorithms have been proposed to solve multi-label classification problem. Through our work, we not only investigate various research works that have been conducted under algorithm adaptation for multi-label classification but also perform comparative study of two proposed algorithms. The first proposed algorithm is named as fuzzy PSO-based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with singular value decomposition. Both the proposed algorithms are applied to real-world datasets, i.e., yeast and scene dataset. The experimental results show that both the proposed algorithms meet or beat ML-RBF and ML-KNN when applied on the test datasets.

Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies

Data mining is an interdisciplinary field of computer science and is referred to extracting or mining knowledge from large amounts of data. Classification is one of the data mining techniques that maps the data into the predefined classes and groups. It is used to predict group membership for data instances. There are many areas that adapt Data mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. The C4.5 can be referred as the statistic Classifier. This algorithm uses gain radio for feature selection and to construct the decision tree. It handles both continuous and discrete features. C4.5 algorithm is widely used because of its quick classification and high precision. This paper proposed a C4.5 classifier based on the various entropies (Shannon Entropy, Havrda and Charvt entropy, Quadratic entropy) instance of Shannon entropy for classification. Experiment results show that the various entropy based approach is effective in achieving a high classification rate. Keywordsata Mining, Classification technique, Machine learning,

Acceleration based Particle Swarm Optimization (APSO) for RNA Secondary Structure Prediction

RNA Secondary Structure prediction is one of the most significant research areas in bioinformatics. Many research works have been developed in the area of RNA secondary structure prediction with optimization methods. But there are certain drawbacks still prevailing in the existing optimization methods. To avoid such drawbacks in the existing methods, we have proposed an Acceleration base Particle Swarm Optimization (APSO) algorithm for finding minimum free energy of RNA secondary structures. The experimental result shows that our proposed APSO algorithm efficiently locates the RNA structures. Furthermore, the performance of our proposed APSO algorithm is evaluated by invoking eight benchmark functions and also compared with Genetic Algorithm (GA) and standard Particle Swarm Optimization (PSO). The test result shows that the APSO is efficient both in test benchmark functions and prediction model and better than other algorithms.

SET-PSO-based approach for mining positive and negative association rules

Data mining is the process of determining new, unanticipated, valuable patterns from existing databases by considering historical and recent developments in statistics, artificial intelligence, and machine learning. It can help companies focus on the most important information in their data warehouses. Association rule mining is one of the most highly researched and popular data mining techniques for finding associations between items in a set. It is frequently used in marketing, advertising, and inventory control. Typically, association rules only consider items in transactions (positive association rules). They do not consider items that do not occur together, which can be used to create rules that are also useful for market basket analysis. Also, existing algorithms often generate too many candidate itemsets when mining the data and scan the database multiple times. To resolve these issues in association rule mining algorithms, we propose SARIC (set particle swarm optimization for association rules using the itemset range and correlation coefficient). Our method uses set particle swarm optimization to generate association rules from a database and considers both positive and negative occurrences of attributes. SARIC applies the itemset range and correlation coefficient so that we do not need to specify the minimum support and confidence, because it automatically determines them quickly and objectively. We verified the efficiency of SARIC using two differently sized databases. Our simulation results demonstrate that SARIC generates more promising results than Apriori, Eclat, HMINE, and a genetic algorithm.

Acceleration Based Particle Swarm Optimization for Graph Coloring Problem

Abstract The graph coloring problem is one of the combinatorial optimization problems. Although many heuristics and metaheuristics algorithm were developed to solve graph coloring problem but they have some limitations in one way or another. In case of tabu search, the algorithm becomes slow, if the tabu list is big. This is because lots of memory to keep the list and also a lot of time to travel through the list, is needed in each step of the algorithm. Simulated annealing has a big handicap when applied to graph coloring problem because there are lots of neighboring states that have the same energy value. The problem with ant colony optimization is that the number of ants that must be checked is n times bigger than other algorithms. Therefore, there will be a need of a large amount of memory and the computational time of this algorithm can be very large. A swarm intelligence based technique called as particle swarm optimization is therefore employed to solve the graph coloring problem. Particle swarm optimization is simple and powerful technique but its main drawback is its ability of being trapped in the local optimum. Therefore, to overcome this, an efficient Acceleration based Particle Swarm Optimization (APSO) is introduced in this paper. Empirical study of the proposed APSO algorithm is performed on the second DIMACS challenge benchmarks. The APSO results are compared with the standard PSO algorithm and experimental results validates the superiority of the proposed APSO.

F-FDRPSO: A Novel Approach Based on Hybridization of Fuzzy C-means and FDRPSO for Gene Clustering

Gene Clustering is one among the most popular issues involve in the field of Bioinformatics and is defined as the process of grouping related genes in the same cluster. Among the various algorithm proposed for clustering, the fuzzy c-means and there hybridization with some other methods has been used by most of the researchers to deal with the problem of premature convergence in fuzzy c-means clustering algorithm, but the results obtained were not satisfactory because the gene expression has huge amounts of ambiguous and uncertain biological data which requires advanced computing tools for processing such data. Particle Swarm Optimization (PSO) one of the variant of Swarm Intelligence (SI) has recently emerged as a nature inspired algorithms, especially known for their ability to produce low cost, fast and reasonably accurate solutions to complex search problems. PSO based Fuzzy C-Means algorithm were proposed but they all uses the traditional PSO algorithm. In traditional PSO algorithm each particle is attracted toward the best ever position discovered by any particle in the swarm, that limits the exploration capability. Instead if particle learn from the experience of the neighbouring that has better fitness than itself, the swarm can be more effectively and efficiently explored. So a method based on hybridization of fuzzy c-means and Fitness Distance Ratio based PSO is proposed. Initially this approach distributes the membership on the basis of the distance between sample and cluster centre making membership meet the constraints of FCM then the ratio of relative fitness and the distance of other particle is used to determine the direction in which each component of the particle position needs to be changed. The experiments were conducted on four real data sets and results shows that F-FDRPSO performs significantly better than FPSO and FCM algorithm.