This paper focuses on the domain of Network Intrusion Detection Systems, an area where the goal is to detect security violations by passively monitoring network traffic and raising an alarm when an attack occurs. But the problem is that new attacks are being deployed all the time. This particular system has been developed using a range of data mining techniques so as to automatically be able to...

This paper describes a field trial for a recently developed ensemble called Additive Groves on KDD Cup’09 competition. Additive Groves were applied to three tasks provided at the competition using the ”small” data set. On one of the three tasks, appetency, we achieved the best result among participants who similarly worked with the small dataset only. Postcompetition analysis showed that less s...

In this paper an intrusion detection algorithm based on GP ensembles is proposed. The algorithm runs on a distributed hybrid multiisland model-based environment to monitor security-related activity within a network. Each island contains a cellular genetic program whose aim is to generate a decision-tree predictor, trained on the local data stored in the node. Every genetic program operates coop...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

Below we describe the winning system that we built for the KDD Cup 2002 Task 1 competition. Our system is a Rule-based Information Extraction (IE) system. It combines pattern matching, Natural Language Processing (NLP) tools, semantic constraints based on the domain and the specific task, and a post-processing stage for making the final curation decision based on the various evidence (positive ...

Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable renewable sources. Due cyber infiltration a lack transparency, the traditional transaction process is inefficient, unsafe expensive. grid systems now efficient, safe transparent owing development blockchain (BC) technology its smart contract (SC) solution. In this study, federated le...

with the rapid growth of attack patterns, the number of attributes for detecting attacks gradually increased. Moreover, an automatic attack classification method, as the next thing of intrusion detection, is needed. For solving the above problems, an intrusion classifier based on multiple attribute selection algorithms has been proposed. The classifier includes various combinations with differe...

This work presents DWMiner, an association rules efficient mining tool to process data directly over a relational DBMS data warehouse. DWMiner executes the Apriori algorithm as SQL queries in parallel, using a database PC Cluster middleware developed for SQL query optimization in OLAP applications. DWMiner combines intraand inter-query parallelism in order to reduce the total time needed to fin...

We describe an approach based on latent factor models to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items which are sampled from the same distribution. Using the item relationship inf...

A new adaptive anomaly detection framework, based on the use of unsupervised evolving connectionist systems, is proposed to address the issue of concept drift. It is designed to adapt to normal behavior changes while still recognizing anomalies. The evolving connectionist systems learn a subject’s behavior in an online, adaptive fashion without a priori knowledge of the underlying data distribu...