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The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.
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.
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.
The 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies including Computing, communication, IOT, LiDAR, Image Analysis, wireless communication and other new technologies
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 ...
Each tutorial reviews currents communications topics in network management and computer and wireless communications. Available tutorials, which are 2.5 to 5 hours in length contains the original visuals and voice-over by the presenter. IEEE Communications Surveys & Tutorials features two distinct types of articles: original articles and reprints. The original articles are exclusively written for IEEE Communications Surveys & Tutorials ...
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.
The purpose of TDSC is to publish papers in dependability and security, including the joint consideration of these issues and their interplay with system performance. These areas include but are not limited to: System Design: architecture for secure and fault-tolerant systems; trusted/survivable computing; intrusion and error tolerance, detection and recovery; fault- and intrusion-tolerant middleware; firewall and network technologies; system management ...
2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2018
Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P ...
2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), 2017
Botnet Detection has been an active research area over the last decades. Researchers have been working hard to develop effective techniques to detect Botnets. From reviewing existing approaches it can be noticed that many of them target specific Botnets. Also, many approaches try to identify any Botnet activity by analysing network traffic. They achieve this by concatenating existing Botnet datasets ...
2018 International Conference on Communication and Signal Processing (ICCSP), 2018
Among various network attacks, botnet led attacks are considered as the most serious threats. A botnet, i.e., the network of compromised computers is able to perform large scale illegal activities such as Distributed Denial of Service attacks, click fraud, bitcoin mining etc. These attacks are considered as the major concern now-a-days. In this paper, we present a comprehensive review of ...
2016 European Intelligence and Security Informatics Conference (EISIC), 2016
We studied how Internet Service Providers (ISPs) are involved in botnet mitigation in the Netherlands. Although Dutch ISPs on average perform very well with respect to botnet mitigation, botnets still are a significant threat and many end-user systems are infected by bot-malware. We created a reference model which summarizes measures for botnet mitigation from scientific literature that ISPs can take. ...
IEEE Communications Surveys & Tutorials, 2017
Malicious botnets have become a common threat and pervade large parts of the Internet today. Existing surveys and taxonomies focus on botnet topologies, command and control protocols, and botnet objectives. Building on these research results, network-based detection techniques have been proposed that are capable of detecting known botnets. Methods for botnet establishment and operation have evolved significantly over the past ...
Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.
Botnet Detection has been an active research area over the last decades. Researchers have been working hard to develop effective techniques to detect Botnets. From reviewing existing approaches it can be noticed that many of them target specific Botnets. Also, many approaches try to identify any Botnet activity by analysing network traffic. They achieve this by concatenating existing Botnet datasets to obtain larger datasets, building predictive models using these datasets and then employing these models to predict whether network traffic is safe or harmful. The problem with the first approaches is that data is usually scarce and costly to obtain. By using small amounts of data, the quality of predictive models will always be questionable. On the other hand, the problem with the second approaches is that it is not always correct to concatenate datasets containing network traffic from different Botnets. Datasets can have different distributions which means they can downgrade the predictive performance of machine learning models. Our idea is instead of concatenating datasets, we propose using transfer learning approaches to carefully decide what data to use. Our hypothesis is “Predictive Performance can be improved by using transfer learning techniques across datasets containing network traffic from different Botnets”.
Among various network attacks, botnet led attacks are considered as the most serious threats. A botnet, i.e., the network of compromised computers is able to perform large scale illegal activities such as Distributed Denial of Service attacks, click fraud, bitcoin mining etc. These attacks are considered as the major concern now-a-days. In this paper, we present a comprehensive review of botnets, their lifecycle and types. We also discuss the peer-to-peer botnet detection techniques' behaviors using various latest detection techniques.
We studied how Internet Service Providers (ISPs) are involved in botnet mitigation in the Netherlands. Although Dutch ISPs on average perform very well with respect to botnet mitigation, botnets still are a significant threat and many end-user systems are infected by bot-malware. We created a reference model which summarizes measures for botnet mitigation from scientific literature that ISPs can take. Our model is structured according to the five stages in the anti-botnet lifecycle: prevention, detection, notification, remediation, and recovery. We validated our reference model in an empirical study by means of semi-structured interviews with a representative sample of Dutch ISPs. Our study identified which measures actually have been taken by ISPs, and why other measures have not been taken (yet). It became clear that ISPs spend most effort on prevention and notification towards customers, thereby focusing on individual bots. ISPs currently have little incentive to implement further measures for detection, remediation, and recovery. Although ISPs are well capable of applying advanced detection and follow up actions, they do not apply such measures mainly due to privacy concerns of customer data. Furthermore, although ISPs do cooperate in various ways, there still is room for improvement, particularly in the sharing of information on botnet infections and mitigation practices with stakeholders and peer ISPs.
Malicious botnets have become a common threat and pervade large parts of the Internet today. Existing surveys and taxonomies focus on botnet topologies, command and control protocols, and botnet objectives. Building on these research results, network-based detection techniques have been proposed that are capable of detecting known botnets. Methods for botnet establishment and operation have evolved significantly over the past decade resulting in the need for detection methods that are capable of detecting new, previously unknown types of botnets. In this paper we present an in-depth analysis of all network communication aspects in botnet establishment and operation. We examine botnet topology, protocols, and analyze a large set of very different and highly sophisticated existing botnets from a network communication perspective. Based on our analysis, we introduce a novel taxonomy of generalized communication patterns for botnet communication using standardized unified modeling language sequence diagrams. We furthermore examine data exchange options and investigate the influence of encryption and hiding techniques. Our generalized communication patterns provide a useful basis for the development of sophisticated network-based botnet detection mechanisms and can offer a key component for building protocol- and topology-independent network-based detectors.
In order to restrain botnet security issues arising from operation of the network, in the dissect of botnets based on the principle of a real botnet tracking, detection methods, effectively restrain botnet network security threats. Through the understanding of the concept of botnets and botnet generated on the principle of the internal working mechanism of development, type and risk of such a comprehensive study, gives the tracking, detection and prevention of specific methods of different botnets. Experiments show that this method is effective to inhibit the breeding of botnets in the slow extension of the network, defense and control of active botnets, strengthen the operation of the network user data safety and security has a very important significance.
The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.
With the rapid development of the information industry, the applications of Internet of things, cloud computing and artificial intelligence have greatly affected people's life, and the network equipment has increased with a blowout type. At the same time, more complex network environment has also led to a more serious network security problem. The traditional security solution becomes inefficient in the new situation. Therefore, it is an important task for the security industry to seek technical progress and improve the protection detection and protection ability of the security industry. Botnets have been one of the most important issues in many network security problems, especially in the last one or two years, and China has become one of the most endangered countries by botnets, thus the huge impact of botnets in the world has caused its detection problems to reset people's attention. This paper, based on the topic of botnet detection, focuses on the latest research achievements of botnet detection based on machine learning technology. Firstly, it expounds the application process of machine learning technology in the research of network space security, introduces the structure characteristics of botnet, and then introduces the machine learning in botnet detection. The security features of these solutions and the commonly used machine learning algorithms are emphatically analyzed and summarized. Finally, it summarizes the existing problems in the existing solutions, and the future development direction and challenges of machine learning technology in the research of network space security.
Nowadays, Botnets pose a major threat to the security of online ecosystems and computing assets. A Botnet is a network of computers which are compromised under the influence of Bot (malware) code. This paper clarifies Botnet phenomenon and discusses Botnet mechanism, Botnet architecture and Botnet detection techniques. Botnet detection techniques can be categorized into six classes: honey pot based, signature-based, mining-based, anomaly-based, DNS- based and network-based. It provides a brief comparison of the above mentioned Botnet detection techniques. Finally, we discuss the importance of honey pot research to detect the infection vector and dealing with new Botnet approaches in the near future.
Internet of Things is one of the most popular themes of recent years. The Internet of Things devices are used in industry, houses, medicine. Mirai botnet is the largest registered botnet that using the Internet of Things. At the peak of its activity, the botnet managed to organize an attack where about 100 thousand devices participated. As a result there were about 1.2 million infected devices, 170 thousand of which were active. The devices of the Internet of things should be revoked from the actions of botnets. This paper describes a new technique enabling to detect attempts to attack the Internet of Things devices. A game-theoretic mathematical model of attack on IoT devices is suggested for this purpose. This technique enables to fix the attempts to crack Internet of Things.
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