Hybrid Intrusion Detection Model for Enhancing the Security and Reducing the Computational Cost

Artificial Intelligence (AI) is becoming essential technology in Cybersecurity. It represents a revolution in detecting and analyzing intrusions based on predictive models and classification methods. Various recent studies discussed the applications of artificial intelligence in Intrusion Detection Systems to improve the accuracy of the classifiers in detecting cyber-attacks but ignored the computational cost of running the algorithm which is considered a crucial factor of the model evaluation. The aim of this paper is to solve this security issue by using dimensionality reduction techniques and machine learning algorithms. To raise their effectiveness and thus enhance network security, a hybrid classifier with high accuracy and low computational cost is proposed. It combines Decision Tree (DT) and Linear Regression (LR) techniques with AdaBoost technique to build a powerful model for detecting cyber-attacks. The hybrid model included 5 stages, (i) selecting and analyzing the dataset, (ii) pre-processing it, (iii) reducing the dimensions using the Principal Component Analysis (PCA), (iv) classifying stage and (v) evaluating the model using the dataset UNSW-NB15. The model has been compared with several state-of-the-art algorithms. The results have shown that the proposed hybrid model achieved a high accuracy (99%) and the runtime was significantly reduced by half using PCA principle

Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

NewsMe: A case study for adaptive news systems with open user model

Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. © 2007 IEEE

MRCRAIG: MapReduce and Ensemble Classifiers for Parallelizing Data Classification Problems

In this paper, a novel technique for parallelizing data-classification problems is applied to finding genes in sequences of DNA. The technique involves various ensem- ble classification methods such as Bagging and Select Best. It then distributes the classifier training and prediction using MapReduce. A novel sequence classification voting algorithm is evaluated in the Bagging method, as well as compared against the Select Best method