Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis. One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates

The Third World Perspective on the Cold War: Making Curriculum and Pedagogy Relevant in History Classrooms

American and global history curriculum frameworks for high schools across the fifty states generally present the topic of the Cold War from the Western political perspective and contain material about the impact of the US-Soviet ideological rivalry on American society. This article argues that since the Cold War impacted the lives of the people in the Third World as well, their stories deserve a place in the history curriculum. Also, the article suggests that since the American society is now culturally diverse, it is imperative for history educators to teach history in the learners' social and cultural contexts. Therefore, both the Cold War history content and pedagogy should include perspective from the Third World so that the past is relevant and meaningful for the students learning about the global Cold War. 

Using Social Science Inquiry for Explaining Major Events in Global History: The Disintegration of the Soviet Union as a Case Study

The disintegration of the Soviet Union in 1991 was a major global historical event of the twentieth century that permanently changed the destiny of hundreds of millions of people around the world. It was not a revolution. It was not a transition to democracy. It was not a struggle for decolonization. No one expected a world power like the Soviet Union to disintegrate into fifteen autonomous republics. Historians, social science researchers, and other observers of the Soviet Union were all surprised by the sudden collapse of a political system that was sustained for seventy years by a political ideology and which had dominated a significant portion of the global land mass, its people, cultures, and resources. How do we explain the disintegration of a superpower? What theories of change may be valid in a case that has no precedent. This paper seeks to explore the causes of the disintegration of the Soviet Union through the formulation and testing of a correlative hypothesis: a strong correlation exists between the break-up of the Communist Party of the Soviet Union (CPSU) and the disintegration of the  Soviet state. This hypothesis is specific, testable, verifiable, and also, it is supported by historical evidence and events examined in the paper

President's Message

 A message from Iftikhar Ahmad, President of the International Assembly