Download or read book Intrusion Detection in Mobile Phone Systems Using Data Mining Techniques written by Bharat Kumar Addagada and published by . This book was released on 2010 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intrusion Detection written by Zhenwei Yu and published by World Scientific. This book was released on 2011 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. This title also includes the performance comparison of various IDS via simulation.
Download or read book Intrusion Detection written by Nandita Sengupta and published by Springer Nature. This book was released on 2020-01-24 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.
Download or read book Improving Intrusion Detection Systems Using Data Mining Techniques written by Abdulrazaq Z. Almutairi and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intrusion Detection and Prevention for Mobile Ecosystems written by Georgios Kambourakis and published by CRC Press. This book was released on 2017-09-06 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art contributions from both scientists and practitioners working in intrusion detection and prevention for mobile networks, services, and devices. It covers fundamental theory, techniques, applications, as well as practical experiences concerning intrusion detection and prevention for the mobile ecosystem. It also includes surveys, simulations, practical results and case studies.
Download or read book Intrusion Detection Using Data Mining Techniques written by and published by . This book was released on 2004 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal Investigator and research fellows conduct research in the investigation of network-based intrusion detection using data mining techniques based on advanced computational statistics and visualization techniques. These new methods will provide the means to detect the presence of covert channels operating on user systems, passively perform continuous user authentication, discern subtle network attacks and information-gathering activities, and provide interface support for "storm center" situational display of intrusion detection alerts, damage assessment, and current network state-of-health.
Download or read book Machine Learning Methods to Improve Network Intrusion Detection Systems written by Nazli Ansari and published by . This book was released on 2019 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining techniques play an increasing role in the intrusion detection by analyzing network data and classifying it as 'normal' or 'intrusion'. In recent years, several data mining techniques such as supervised, semi-supervised and unsupervised learning are widely used to enhance the intrusion detection. This work proposes a dedicated pre-processing steps and feature extraction for IDS. Recently published data set CICIDS2017 is used in this work to evaluate the machine learning models. The result shows a high accuracy with Random Forest for binary classification.
Download or read book Mobile Hybrid Intrusion Detection written by Álvaro Herrero and published by Springer. This book was released on 2011-01-28 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.
Download or read book Intrusion Detection Systems written by Pawel Skrobanek and published by BoD – Books on Demand. This book was released on 2011-03-22 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current structure of the chapters reflects the key aspects discussed in the papers but the papers themselves contain more additional interesting information: examples of a practical application and results obtained for existing networks as well as results of experiments confirming efficacy of a synergistic analysis of anomaly detection and signature detection, and application of interesting solutions, such as an analysis of the anomalies of user behaviors and many others.
Download or read book Machine Learning in Intrusion Detection written by Yihua Liao and published by . This book was released on 2005 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detection of anomalies in data is one of the fundamental machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate that our approach is able to effectively detect intrusive program behavior while a low false positive rate is achieved. Second, we describe an adaptive anomaly detection framework that is de- signed to handle concept drift and online learning for dynamic, changing environments. Through the use of unsupervised evolving connectionist systems, normal behavior changes are efficiently accommodated while anomalous activities can still be recognized. We demonstrate the performance of our adaptive anomaly detection systems and show that the false positive rate can be significantly reduced.
Download or read book Intrusion Detection System Using Data Mining Technique Journal of the ACS written by Naelah Okasha and published by . This book was released on 2010 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Data Mining Approach to Network Intrusion Detection written by Mrutyunjaya Panda and published by LAP Lambert Academic Publishing. This book was released on 2015-02-06 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The menace of illegal access to data resources is a growing concern of researchers in the field of computer science. A significant amount of effort is required to monitor the activities in a computer network with a view to detect any attempt for intrusion. From this perspective, the main motivation behind this research is to design an efficient intrusion detection system using some novel data mining approaches that have the capability to detect intrusions with high detection rate with low false positive rate. In this work, we take multiple supports Apriori algorithm with various interestiness measures to obtain the most significant rules in detecting network intrusions. Further, we propose some novel ensemble of classifiers in order to enhance the detection rate of network attacks. Some unsupervised clustering algorithms have been proposed to further increase the detection rate of new or unseen attacks that fall under rare attacks categories. Finally, certain hybrid data mining approaches have been employed in order to design an efficient anomaly based network intrusion detection system that can achieve high detection rate and low false positive rate.
Download or read book Data Mining Approaches for Intrusion Detection written by and published by . This book was released on 2000 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra- and inter- audit record patterns, which are essential in describing program or user behavior.
Download or read book IDDM written by Tamas Abraham and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Practical Data Mining Techniques and Applications written by Ketan Shah and published by CRC Press. This book was released on 2023-06-19 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique selling point: Applied data mining techniques in multiple domains and real-world settings Core audience: Researchers, graduate and post graduate students, and academics Place in the market: Applied technology reference book
Download or read book Anomaly Based Intrusion Detection System written by Jyothsna Veeramreddy and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anomaly-based network intrusion detection plays a vital role in protecting networks against malicious activities. In recent years, data mining techniques have gained importance in addressing security issues in network. Intrusion detection systems (IDS) aim to identify intrusions with a low false alarm rate and a high detection rate. Although classification-based data mining techniques are popular, they are not effective to detect unknown attacks. Unsupervised learning methods have been given a closer look for network IDS, which are insignificant to detect dynamic intrusion activities. The recent contributions in literature focus on machine learning techniques to build anomaly-based intrusion detection systems, which extract the knowledge from training phase. Though existing intrusion detection techniques address the latest types of attacks like DoS, Probe, U2R, and R2L, reducing false alarm rate is a challenging issue. Most network IDS depend on the deployed environment. Hence, developing a system which is independent of the deployed environment with fast and appropriate feature selection method is a challenging issue. The exponential growth of zero-day attacks emphasizing the need of security mechanisms which can accurately detect previously unknown attacks is another challenging task. In this work, an attempt is made to develop generic meta-heuristic scale for both known and unknown attacks with a high detection rate and low false alarm rate by adopting efficient feature optimization techniques.
Download or read book HOST BASED INTRUSION DETECTION SYSTEM USING DATA MINING APPROACH written by DANIEL TAN YUN SHENG (TP028534) and published by . This book was released on 2015 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt:
