An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

Samba Openldap: An Evolution And Insight

Directory services facilitate access to information organized under a variety of frameworks and applications. The Lightweight Directory Access Protocol is a promising technology that provides access to directory information using a data structure similar to that of the X.500 protocol. IBM Tivoli, Novell, Sun, Oracle, Microsoft, and many other vendor features LDAP-based implementations. The technology’s increasing popularity is due both to its flexibility and its compatibility with existing applications. A directory service is a searchable database repository that lets authorized users and services find information related to people, computers, network devices, and applications. Given the increasing need for information — particularly over the Internet — directory popularity has grown over the last decade and is now a common choice for distributed applications. Lightweight Directory Access Protocol (LDAP) accommodates the need of high level of security, single sign-on, and centralized user management. This protocol offers security services and integrated directory with capability of storage management user information in a directory. Therefore at the same time the user can determine application, service, server to be accessed, and user privileges. It is necessary to realize files sharing between different operating systems in local area network. Samba software package, as the bridge across Windows and Linux, can help us resolve the problem. In this paper, we try to explore previous literature on this topic and also consider current authors work then come out with our views on the subject matter of discussion based on our understanding

Samba Openldap Performance in a Simulated Environment

The Information Technology world is developing so fast and it is been reported that Open Source tools will eventually take over proprietary tools in no to distant future. The Open Source Community is integrating its products with that of the proprietary ones and the integration of Windows machines into Linux network is evident of such practices. The purpose of this project is to implement Samba with OpenLDAP in a simulated environment. This implementation is conducted within a virtual environment by simulating the setup of Linux and Windows Operating systems by reducing physical setup of machines. Samba will act as an interface between Linux and Windows, files will be accessible to both server and client. OpenLDAP stores the user accounts and configuration files. A performance test carried out on Samba determining effect on CPU power and Memory usage shows a decrease in the CPU power and an increase in Memory usage

Collaborative Filtering Recommender Systems

Abstract: Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decisionmaking processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms