Empirical Evaluations On Real And Synthetic Datasets State Of The Art Utility Mining Algorithms

We have considered the issue of best k high utility itemsets mining, where k is the coveted number of high utility itemsets to be mined. Two effective calculations TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One stage) are proposed for mining such itemsets without setting least utility limits. TKU is the initial two-stage calculation for mining top-k high utility itemsets, which joins five techniques PE, NU, MD, MC and SE to adequately raise the fringe least utility edges and further prune the hunt space. Then again, TKO is the first stage algorithm produced for top-k HUI mining, which incorporates the novel methodologies RUC, RUZ and EPB to extraordinarily enhance its execution. The proposed calculations have great versatility on extensive datasets and the execution of the proposed algorithms is near the ideal instance of the cutting edge two-stage and one-stage utility mining algorithms

An Suitable Minimum Utility Threshold By Trial And Error Is A Tedious Process For Users

We address the above issues by proposing another system for top-k high utility itemset mining, where k is the coveted number of HUIs to be mined. Two sorts of effective calculations named TKU (mining Top-K Utility item sets) and TKO (mining Top-K utility item sets in One stage) are proposed for mining such item sets without the need to set min util. We give a basic correlation of the two calculations with exchanges on their preferences and constraints. Exact assessments on both genuine and engineered datasets demonstrate that the execution of the proposed calculations is near that of the ideal instance of cutting edge utility mining algorithms

Predictive Cyber Situational Awareness and Personalized Blacklisting: A Sequential Rule Mining Approach

Cybersecurity adopts data mining for its ability to extract concealed and indistinct patterns in the data, such as for the needs of alert correlation. Inferring common attack patterns and rules from the alerts helps in understanding the threat landscape for the defenders and allows for the realization of cyber situational awareness, including the projection of ongoing attacks. In this paper, we explore the use of data mining, namely sequential rule mining, in the analysis of intrusion detection alerts. We employed a dataset of 12 million alerts from 34 intrusion detection systems in 3 organizations gathered in an alert sharing platform, and processed it using our analytical framework. We execute the mining of sequential rules that we use to predict security events, which we utilize to create a predictive blacklist. Thus, the recipients of the data from the sharing platform will receive only a small number of alerts of events that are likely to occur instead of a large number of alerts of past events. The predictive blacklist has the size of only 3 % of the raw data, and more than 60 % of its entries are shown to be successful in performing accurate predictions in operational, real-world settings

MINING TOP-K HIGH UTILITY ITEM SETS BY USING EFFICIENT DATA STRUCTURE TO IMPROVE THE PERFORMANCE

Association rules show strong relationship between attribute-value pairs (or items) that occur frequently in a given data set. Association rules are commonly used to determine the purchasing patterns of customers in a store. Such analysis is implemented in many decision-making processes, such as product placement, catalogue design, and cross-marketing. The discovery of association rules is based on frequent itemset mining. These frequent itemset mining algorithms mainly suffers from generation of more number of candidate itemsets and large no of database scans. These issues are addressed by two algorithms namely TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in one phase) which are recommended for mining K- high utility itemsets in two scans of the entire database. Though scans are reduced to two, processing time is more because of UP-Tree traversals which is the data structure used by TKU and TKO algorithms.  The proposed algorithm uses B+-Tree data structure instead of UP-Tree to reduce the time. Experimental analysis clearly shows that the processing time is improved and hence limitations of existing work are overcome by proposing a methodology using B+ -Tree

High Utility Itemsets Mining for Transactional Databases

Mainstream issue in data mining, which is called "high-utility itemset mining" or all the more for the most part utility mining. High Utility Itemsets which are itemsets having an utility gathering a client determined least utility edge value i.e min_util. The principle target of utility mining is to discover thing sets with highest utilities, by thinking about benefit, amount, cost or some other client inclinations. Research has been done in region of mining HUI's. Different procedures have been connected. The fundamental issue with setting edge value which is for the most part client particular, is it should be proper. In Order to set most fitting or right Threshold value for mining HUI's,user needs to do trial and mistake which thus is tedious and repetitive process, in light of the fact that if min_util is set too low, framework will bring about getting substantial data of HUI, which thus makes framework incapable with the end goal of HUI. In the event that we set min_util too high, this will bring about getting little sum or no HUI's. Consequently setting least edge value is troublesome. The proposed framework is following Top-k framework for mining top-k HUI's, which is utilizing two algorithms TKU (mining top-k utility itemsets) and TKO (mining top-k in one phase),without setting min_util edge

Predictive Methods in Cyber Defense: Current Experience and Research Challenges

Predictive analysis allows next-generation cyber defense that is more proactive than current approaches based on intrusion detection. In this paper, we discuss various aspects of predictive methods in cyber defense and illustrate them on three examples of recent approaches. The first approach uses data mining to extract frequent attack scenarios and uses them to project ongoing cyberattacks. The second approach uses a dynamic network entity reputation score to predict malicious actors. The third approach uses time series analysis to forecast attack rates in the network. This paper presents a unique evaluation of the three distinct methods in a common environment of an intrusion detection alert sharing platform, which allows for a comparison of the approaches and illustrates the capabilities of predictive analysis for current and future research and cybersecurity operations. Our experiments show that all three methods achieved a sufficient technology readiness level for experimental deployment in an operational setting with promising accuracy and usability. Namely prediction and projection methods, despite their differences, are highly usable for predictive blacklisting, the first provides a more detailed output, and the second is more extensible. Network security situation forecasting is lightweight and displays very high accuracy, but does not provide details on predicted events

Towards Automated Design of Complex Modular Systems Inspired by Nature

Katedra kybernetik