12,275 resources related to Intrusion Detection
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
ICC 2021 - IEEE International Conference on Communications
IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.
All fields of satellite, airborne and ground remote sensing.
IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.
IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.
The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
2017 International Conference on Inventive Computing and Informatics (ICICI), 2017
Around the world, billions of people access the internet today. Intrusion detection technology is a new generation of security technology that monitor system to avoid malicious activities. The paper consists of the literature survey of Internal Intrusion Detection System (IIDS) and Intrusion Detection System (IDS) that uses various data mining and forensic techniques algorithms for the system to work in ...
2012 International Conference on Computing, Measurement, Control and Sensor Network, 2012
In recent years, many approaches have been proposed for intrusion detection. In this paper, we propose a cloud intrusion detection with a new statistical waveform based classification. It records network connections over a period of time to form a waveform, and then computes the suspicious characteristics of the waveform. It classifies the intrusion with these selected waveform features. In our ...
2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), 2013
In computer network security, a Network Intrusion Detection (NID) is an Intrusion Detection mechanism that attempts to discover unauthorized access to a computer network by analyzing traffic on the network for signs of malicious activity. There are many areas of research in this vast field of Network Intrusion Detection (NID) but in this survey paper, we will focus on its ...
2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018
Internet is a widely used platform nowadays by people across the globe. This has led to the advancement in science and technology. Many surveys show that network intrusion has registered a consistent increase and lead to personal privacy theft and has become a major platform for attack in the recent years. Network intrusion is any unauthorized activity on a computer ...
2011 International Conference on Computer Science and Service System (CSSS), 2011
In this paper, Data Mining is introduced into the Intrusion Detection System, which overcomes the defects of traditional detection technology. The nuclear association rules algorithm applied to the intrusion detection matrix is optimized, which make it possible to reduce the Average-Case Time Complexity, improve the efficiency considerably, and make it easy to process magnanimity data. In this way, attacks will ...
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
Critical use cases for video capturing systems in autonomous driving applications
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Analytics for Anomaly detection & Classification | DSBC 2020
Developing Automated Analysis Tools for Space/Time Sidechannel Detection - IEEE SecDev 2016
IEEE Medal for Environmental and Safety Technologies - Jerome Faist and Frank K. Tittell - 2018 IEEE Honors Ceremony
Fireside Chat: Key Opinion Leaders on Pre-Symptomatic Illness Detection - IEEE EMBS at NIH, 2019
An IEEE IPC Special Session with X. Chen from Nokia Bell Labs
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
ASC-2014 SQUIDs 50th Anniversary: 4 of 6 - Keiji Enpuku
Low Power Image Recognition: The Challenge Continues
A Recurrent Crossbar of Memristive Nanodevices Implements Online Novelty Detection - Christopher Bennett: 2016 International Conference on Rebooting Computing
Sang Gyun Kim - RFIC Industry Showcase - IMS 2020
Welcome to ICRA 2015: Robot Challenges
IEEE Highlight: Electronic Nose: Diagnosing Cancer Through Smell
Silicon THz: an Opportunity for Innovation
Around the world, billions of people access the internet today. Intrusion detection technology is a new generation of security technology that monitor system to avoid malicious activities. The paper consists of the literature survey of Internal Intrusion Detection System (IIDS) and Intrusion Detection System (IDS) that uses various data mining and forensic techniques algorithms for the system to work in real time. Data mining methods are proposed for cyber analytics in support of intrusion detection.
In recent years, many approaches have been proposed for intrusion detection. In this paper, we propose a cloud intrusion detection with a new statistical waveform based classification. It records network connections over a period of time to form a waveform, and then computes the suspicious characteristics of the waveform. It classifies the intrusion with these selected waveform features. In our evaluation, a DARPA Intrusion Detection Data Sets has been used in our evaluation, and the preliminary results confirmed that our approach is feasible.
In computer network security, a Network Intrusion Detection (NID) is an Intrusion Detection mechanism that attempts to discover unauthorized access to a computer network by analyzing traffic on the network for signs of malicious activity. There are many areas of research in this vast field of Network Intrusion Detection (NID) but in this survey paper, we will focus on its technology, development & strategic importance. Virus attacks, unauthorized access, theft of information and denial-of-service attacks were the greatest contributors to computer crime, a number of techniques have been developed in the past few years to help cyber security experts in strengthening the security of a single host or the whole computer network. Intrusion Detection is important for both Military as well as commercial sectors for the sack of their Information Security, which is the most important topic of research for the future networks.
Internet is a widely used platform nowadays by people across the globe. This has led to the advancement in science and technology. Many surveys show that network intrusion has registered a consistent increase and lead to personal privacy theft and has become a major platform for attack in the recent years. Network intrusion is any unauthorized activity on a computer network. Hence there is a need to develop an effective intrusion detection system. In this paper we acquaint an intrusion detection system that uses improved genetic k-means algorithm(IGKM) to detect the type of intrusion. This paper also shows a comparison between an intrusion detection system that uses the k-means++ algorithm and an intrusion detection system that uses IGKM algorithm while using smaller subset of kdd-99 dataset with thousand instances and the KDD-99 dataset. The experiment shows that the intrusion detection that uses IGKM algorithm is more accurate when compared to k-means++ algorithm.
In this paper, Data Mining is introduced into the Intrusion Detection System, which overcomes the defects of traditional detection technology. The nuclear association rules algorithm applied to the intrusion detection matrix is optimized, which make it possible to reduce the Average-Case Time Complexity, improve the efficiency considerably, and make it easy to process magnanimity data. In this way, attacks will be detected promptly to achieve the goal of intrusion detection. Finally, the mining of normal connection rules in the knowledge base of intrusion detection matrix will be accomplished. The experiment indicates that the matrix is able to generate new rules after extracting features, and also proves the validity and the feasibility of the IDS.
Intrusion Detection is an indispensable component of Network Security. Because exists the problems of the high false positives rate and low detection efficiency in the current intrusion detection system, in this paper, we propose a hybrid intrusion detection model and improve intrusion detection system analyzer, applying the Data fusion and data mining techniques to intrusion detection systems. We have researched this model further more and analyzed its architecture in detail.
In order to share the knowledge of intrusion among distributed hosts and make the intrusion detect packages more efficient and reliable, a framework of distributed incremental intrusion detection based on SVM is proposed in the study. In this framework, the locate SVM detects the local attacks and take charge of collecting the new typical samples. A center SVM summarizes the distributed samples and incorporates them to build the incremental SVM for locals. The simulation experiments with KDD Cup 1999 data demonstrate that our proposed method achieves the increasing performance for intrusion detection. The framework is valuable to design distributed intrusion detection system.
The design and development of distributed and collaborative architectures for network intrusion detection systems is an ongoing yet challenging research field. The decentralizing of the intrusion detection functionalities became a promising approach to keep up with the steadily increase of the network communications' capacity and the attack's signatures data bases. So far, several communication models have been proposed in the literature for distributed intrusion detection systems' components. In this paper we focus on the design and implementation of an agent centric library to support flexible and extensible messages exchanges between intrusion detection system's components. We have functionally validated our solution based on a set of tests run over a real-world prototype we have implemented.
To address the problem of low accuracy and high false alarm rate in network intrusion detection system, an intrusion detection model of SVM ensemble using rough set feature reduct is presented. Utilizing the character that rough set algorithm can keep the discernibility of original dataset after reduction, the reducts of the original dataset are calculated and used to train individual SVM classifier for ensemble, which increase the diversity between individual classifiers, and consequently, increase the probability of detection accuracy improving. To validate the effectiveness of the proposed method, simulation experiments are performed based on the KDD 99 dataset. During the process of the experiments, two arguments, the sample number and the base classification number, are discussed to test their effect on the final result. And then detection performance comparison among the SVM using all samples, SVM-bagging ensemble and rough set based SVM-bagging are performed. The results show that the Rough Set based SVM-bagging is a promised ensemble method owning to its high diversity, high detection accuracy and faster speed in intrusion detection.
Anomaly-based network intrusion detection that uses entropy has been researched for quite some time. In this paper, we present results of application of an entropy-based anomaly detector, implemented as an extension of snort intrusion detection system. The detector has been realized as a platform for case study on applicability of entropy-based techniques in network intrusion detection. The paper presents results of the detector's application to two available network traces. The analysis of results shows that nonstationarity is an important property of network traffic which has to be taken into account in entropy based intrusion detection.
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