on false data-injection attacks against power system state estimation modeling and countermeasures

Abstract With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate. Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.

Slide present statistical mining of Malicious-Executable dataset collected from various antivirus log-files and other sources. Further classifications of malicious code as per their impact on user's system & distinguishes threats on the muse in their connected severity. Implementation of the algorithms JRIP ,PART and RIDOR in additional economical manner to acquire a level of accuracy to the classification results.

This document discusses detecting malware using n-grams and machine learning algorithms. It analyzes executable files to extract n-gram sequences from the opcode, creates a feature vector table (FVT) of n-grams and their frequencies. This FVT is used to train and test machine learning classifiers like J48, SVM, and Random Forest. Dimensionality reduction using PCA is also applied before classification. The models are evaluated based on metrics like accuracy, misclassification rate, and precision on n-gram datasets of different sizes. Random Forest performs best with over 95% accuracy on 2-grams.