Efficient Data Mining Algorithm Network Intrusion Detection System for Masked Feature Intrusions

Most researches have been conducted to develop models, algorithms and systems to detect intrusions. However, they are not plausible as intruders began to attack systems by masking their features. While researches continued to various techniques to overcome these challenges, little attention was given to use data mining techniques, for development of intrusion detection. Recently there has been much interest in applying data mining to computer network intrusion detection, specifically as intruders began to cheat by masking some detection features to attack systems. This work is an attempt to propose a model that works based on semi-supervised collective classification algorithm. For this study, data mining algorithms were first selected based on efficiency and accessibility criteria. An experiment was conducted using real .arff dataset to develop the model. The result shows that meta.Filtered Collective Classifier is appropriate to detect intrusions with hidden features, which scored the best classification accuracy of 96.2%

Collective Multi-relational Network Mining

Our world is becoming increasingly interconnected, and the study of networks and graphs are becoming more important than ever. Domains such as biological and pharmaceutical networks, online social networks, the World Wide Web, recommender systems, and scholarly networks are just a few examples that include explicit or implicit network structures. Most networks are formed between different types of nodes and contain different types of links. Leveraging these multi-relational and heterogeneous structures is an important factor in developing better models for these real-world networks. Another important aspect of developing models for network data to make predictions about entities such as nodes or links, is the connections between such entities. These connections invalidate the i.i.d. assumptions about the data in most traditional machine learning methods. Hence, unlike models for non-network data where predictions about entities are made independently of each other, the inter-connectivity of the entities in networks should cause the inferred information about one entity to change the models belief about other related entities. In this dissertation, I present models that can effectively leverage the multi-relational nature of networks and collectively make predictions on links and nodes. In both tasks, I empirically show the importance of considering the multi-relational characteristics and collective predictions. In the first part, I present models to make predictions on nodes by leveraging the graph structure, links generation sequence, and making collective predictions. I apply the node classification methods to detect social spammers in evolving multi-relational social networks and show their effectiveness in identifying spammers without the need of using the textual content. In the second part, I present a generalized augmented multi-relational bi-typed network. I then propose a template for link inference models on these networks and show their application in pharmaceutical discoveries and recommender systems. In the third part, I show that my proposed collective link prediction model is an instance of a general graph-based prediction model that relies on a neighborhood graph for predictions. I then propose a framework that can dynamically adapt the neighborhood graph based on the state of variables from intermediate inference results, as well as structural properties of the relations connecting them to improve the predictive performance of the model