The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
Hewu Li - One of the best experts on this subject based on the ideXlab platform.
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iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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ISCC - iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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Client-Network collaborative load balancing mechanism for WLAN based on SDN and 802.11u
2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of Wireless Local Area Network (WLAN), the densities of both Access Points (APs) and Clients have dramatically increased. In the dense and crowded WLANs, the challenges of load balance become non-negligible due to the problems raised by traditional AP selection mechanism based on signal strength. To address this issue, many existing works consider the load metrics and conduct dynamic topology adjustment for the AP selection. In these literatures, a specific server and trick communications between Client and Network are always introduced, but they are not general. In this paper, we propose a novel load balancing mechanism, which is implemented on a Client-Network collaborative architecture based on Software Defined Network (SDN) and 802.11u. In our architecture, we implement several SDN applications to collect and maintain the information of load and topology, and design Network Resource Query Protocol (NRQP) to exchange the metrics of AP selection between Client and Network based on 802.11u. With the help of this architecture, we propose a novel load aware algorithm which can take the comprehensive consideration of the link state, Client traffic and AP load. It can not only work when first association or moving handoff, but also conduct topology adjustment at proper time. We evaluate our mechanism on a real SDN prototype testbed and the results show the competitive performance.
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Deep analysis of invalid handoffs in WLANs based on Network-Client collaborative framework
2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As 802.11 Network has become an important infrastructure for the Mobile Internet, the performance of WLAN handoff is critical to the quality of user's experience. Although it has shown that there are a large number of invalid handoffs in large-scale 802.11 Networks with dense AP, the reason and seriousness still remain unclear. In this paper, we propose HandoffAnalyser, a Client-Network collaborative framework, to deeply measure and analyze the handoffs. Then, we give a clear explanation of invalid handoffs by defining two patterns termed Loose Ping-Pong (LPP) and Strict Ping-Pong (SPP) based on association history. The experimental results of real campus WLAN with about 2,700 APs and 220,000 mobile devices show that 22.1% and 31.3% of handoffs are SPP and LPP respectively. We even observe that several Clients experience more than 280 LPP and 220 SPP per day respectively. Such a large number of ping-pong will seriously affect the performance of both Client and Network. We carry out in-depth analysis and find that ping-pong has relationship to some wireless metrics such as channel utilization, Network configuration such as AP with Multi-SSID, and hardware defect such as poor 5 GHz chip. These findings and conclusions can help us to better optimize the WLAN.
Xiaokang Sang - One of the best experts on this subject based on the ideXlab platform.
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iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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ISCC - iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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Client-Network collaborative load balancing mechanism for WLAN based on SDN and 802.11u
2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of Wireless Local Area Network (WLAN), the densities of both Access Points (APs) and Clients have dramatically increased. In the dense and crowded WLANs, the challenges of load balance become non-negligible due to the problems raised by traditional AP selection mechanism based on signal strength. To address this issue, many existing works consider the load metrics and conduct dynamic topology adjustment for the AP selection. In these literatures, a specific server and trick communications between Client and Network are always introduced, but they are not general. In this paper, we propose a novel load balancing mechanism, which is implemented on a Client-Network collaborative architecture based on Software Defined Network (SDN) and 802.11u. In our architecture, we implement several SDN applications to collect and maintain the information of load and topology, and design Network Resource Query Protocol (NRQP) to exchange the metrics of AP selection between Client and Network based on 802.11u. With the help of this architecture, we propose a novel load aware algorithm which can take the comprehensive consideration of the link state, Client traffic and AP load. It can not only work when first association or moving handoff, but also conduct topology adjustment at proper time. We evaluate our mechanism on a real SDN prototype testbed and the results show the competitive performance.
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Deep analysis of invalid handoffs in WLANs based on Network-Client collaborative framework
2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As 802.11 Network has become an important infrastructure for the Mobile Internet, the performance of WLAN handoff is critical to the quality of user's experience. Although it has shown that there are a large number of invalid handoffs in large-scale 802.11 Networks with dense AP, the reason and seriousness still remain unclear. In this paper, we propose HandoffAnalyser, a Client-Network collaborative framework, to deeply measure and analyze the handoffs. Then, we give a clear explanation of invalid handoffs by defining two patterns termed Loose Ping-Pong (LPP) and Strict Ping-Pong (SPP) based on association history. The experimental results of real campus WLAN with about 2,700 APs and 220,000 mobile devices show that 22.1% and 31.3% of handoffs are SPP and LPP respectively. We even observe that several Clients experience more than 280 LPP and 220 SPP per day respectively. Such a large number of ping-pong will seriously affect the performance of both Client and Network. We carry out in-depth analysis and find that ping-pong has relationship to some wireless metrics such as channel utilization, Network configuration such as AP with Multi-SSID, and hardware defect such as poor 5 GHz chip. These findings and conclusions can help us to better optimize the WLAN.
Qian Wu - One of the best experts on this subject based on the ideXlab platform.
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iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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ISCC - iScan: Efficient WiFi Scan for mobile device based on Client and Network behavior learning
2017 IEEE Symposium on Computers and Communications (ISCC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of smart phones, dense and large-scale 802.11-based WLANs have become an important infrastructure to the Mobile Internet. Usually, mobile devices perform periodic scan to collect the information of surrounding APs for awareness of wireless environment. However, this kind of frequent scan severely degrades both the performance of Network and Client. In this paper, we first collect a real-world WLAN dataset which contains handoff information and related Client state in two weeks from about 3500 mobile devices. Based on the analysis of this dataset, we observe that such kind of frequent scan for mobility is unnecessary for stationary Client to pursue better performance, which will result in a large number of invalid scans and even the ping-pong. To address this issue, we propose an efficient WiFi scanning scheme called iScan, which triggers scan based on Client and Network behavior learning. The Network behavior is represented by the change of Network load, while the Client behavior is detected by an accelerometer. In iScan scheme, when the Client is moving, scans will be triggered if the RSSI is below the specific thresholds for different speeds. When the Client keeps stationary, iScan adjusts the scanning intervals adaptively based on the law of Network load change. We conduct experiments by both case study and simulation in campus WLAN. The case study shows that iScan can trigger scan more accurately without missing necessary handoffs when the Client is moving. and the simulation shows that iScan reduces 95.8% of invalid scans while ensuring the stationary Client discoveries the change of Network load timely.
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Client-Network collaborative load balancing mechanism for WLAN based on SDN and 802.11u
2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As the rapid growth of Wireless Local Area Network (WLAN), the densities of both Access Points (APs) and Clients have dramatically increased. In the dense and crowded WLANs, the challenges of load balance become non-negligible due to the problems raised by traditional AP selection mechanism based on signal strength. To address this issue, many existing works consider the load metrics and conduct dynamic topology adjustment for the AP selection. In these literatures, a specific server and trick communications between Client and Network are always introduced, but they are not general. In this paper, we propose a novel load balancing mechanism, which is implemented on a Client-Network collaborative architecture based on Software Defined Network (SDN) and 802.11u. In our architecture, we implement several SDN applications to collect and maintain the information of load and topology, and design Network Resource Query Protocol (NRQP) to exchange the metrics of AP selection between Client and Network based on 802.11u. With the help of this architecture, we propose a novel load aware algorithm which can take the comprehensive consideration of the link state, Client traffic and AP load. It can not only work when first association or moving handoff, but also conduct topology adjustment at proper time. We evaluate our mechanism on a real SDN prototype testbed and the results show the competitive performance.
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Deep analysis of invalid handoffs in WLANs based on Network-Client collaborative framework
2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017Co-Authors: Xiaokang Sang, Qian Wu, Hewu LiAbstract:As 802.11 Network has become an important infrastructure for the Mobile Internet, the performance of WLAN handoff is critical to the quality of user's experience. Although it has shown that there are a large number of invalid handoffs in large-scale 802.11 Networks with dense AP, the reason and seriousness still remain unclear. In this paper, we propose HandoffAnalyser, a Client-Network collaborative framework, to deeply measure and analyze the handoffs. Then, we give a clear explanation of invalid handoffs by defining two patterns termed Loose Ping-Pong (LPP) and Strict Ping-Pong (SPP) based on association history. The experimental results of real campus WLAN with about 2,700 APs and 220,000 mobile devices show that 22.1% and 31.3% of handoffs are SPP and LPP respectively. We even observe that several Clients experience more than 280 LPP and 220 SPP per day respectively. Such a large number of ping-pong will seriously affect the performance of both Client and Network. We carry out in-depth analysis and find that ping-pong has relationship to some wireless metrics such as channel utilization, Network configuration such as AP with Multi-SSID, and hardware defect such as poor 5 GHz chip. These findings and conclusions can help us to better optimize the WLAN.
V. Karamcheti - One of the best experts on this subject based on the ideXlab platform.
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Using views for customizing reusable components in component-based frameworks
High Performance Distributed Computing 2003. Proceedings. 12th IEEE International Symposium on, 2003Co-Authors: A.-a. Ivan, V. KaramchetiAbstract:Increasingly, scalable distributed applications are being constructed by integrating reusable components spanning multiple administrative domains. Dynamic composition and deployment of such applications enables flexible QoS-aware adaptation to changing Client and Network characteristics. However, dynamic deployment across multiple administrative domains needs to perform cross-domain authentication and authorization, and satisfy various Network and application-level constraints that may only be expressed in terms meaningful within a particular domain. Our solution to these problems, developed as part of the partitionable services framework, integrates a decentralized trust management and access control system (dRBAC) with a programming and run-time abstraction (object views). dRBAC encodes statements within and across domains using cryptographically signed credentials, providing a unifying and powerful mechanism for cross-domain authorization and expression of Network and application constraints. Views define multiple implementations of a reusable component, thus enriching the set of components available for dynamic deployment and enabling fine-grained, customizable access control. We describe the runtime support for views, which consists of a view generator (VIG) and a host-level communication resource (Switchboard) for creating secure channels between pairs of components. We present a simple mail application to illustrate how dRBAC, views, and Switchboard can be used to customize reusable components and securely deploy them in heterogeneous environments.
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Constrained component deployment in wide-area Networks using AI planning techniques
Proceedings International Parallel and Distributed Processing Symposium, 2003Co-Authors: T. Kichkaylo, A. Ivan, V. KaramchetiAbstract:Wide-area Network applications are increasingly being built using component-based models, which enable integration of diverse functionality in modules distributed across the Network. In such models, dynamic component selection and deployment enables an application to flexibly adapt to changing Client and Network characteristics, achieve load-balancing, and satisfy QoS requirements. Unfortunately, the problem of finding a valid component deployment is hard because one needs to decide on the set of components while satisfying various constraints resulting from application semantic requirements, Network resource limitations, and interactions between the two. In this paper, we propose a general model for the component placement problem and present an algorithm for it, which is based on AI planning algorithms. We validate the effectiveness of our algorithm by demonstrating its scalability with respect to Network size and number of components in the context of deployments generated for two example applications - a security-sensitive mail service, and a Webcast service - in a variety of Network environments.
A. Michajlowski - One of the best experts on this subject based on the ideXlab platform.
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Assessing Information Security: Strategies, Tactics, Logic and Framework
Governance An International Journal Of Policy And Administration, 2010Co-Authors: Alexander Vladimirov, K. Gavrilenko, A. MichajlowskiAbstract:Second edition. Build a strategic response to cyber attacks The activities of the cyber criminal are both deliberate and hostile, and they can be compared to military operations. Many people in business understand that the insights from the classics of military strategy are as relevant to modern commerce as they are to war. It is clear that organisations need to develop a view of cybersecurity that goes beyond technology: all staff in the organisation have a role to play, and it is the senior managers who must ensure, like generals marshalling their forces, that all staff know the cyber security policies that. ""Cover""; ""Title""; ""Copyright""; ""Contents""; ""Introduction""; ""Chapter 1: Information Security Auditing and Strategy""; ""The mindsets of ignorance""; ""Defence-in-depth""; ""Compelling adversaries to adapt""; ""Chapter 2: Security Auditing, Governance, Policies and Compliance""; ""General security policy shortcomings""; ""Addressing security audits in policy statements""; ""The erroneous path to compliance""; ""Getting down to earth""; ""Chapter 3: Security Assessments Classification""; ""Black, grey and white box tests""; ""Assessments specialisations and actual scopes"" ""On technical information security assessments""""Server, Client and Network-centric tests""; ""IT security testing levels and target areas""; ""'Idiosyncratic' technical security tests""; ""On non-technical information security audits""; ""Premises and physical security checks""; ""Social engineering tests""; ""Security documentation reviews""; ""Assessing security processes""; ""Chapter 4: Advanced Pre-Assessment Planning""; ""The four-stage framework""; ""Selecting the targets of assessment""; ""Evaluating what is on offer""; ""Professional certifications and education"" ""Publications and tools""""The auditor company history and size""; ""Dealing with common assessment emergencies""; ""Chapter 5: Security Audit Strategies and Tactics""; ""Centres of gravity and their types""; ""Identifying critical points""; ""The strategic exploitation cycle""; ""External technical assessment recon""; ""Social engineering recon""; ""Internal technical assessment recon""; ""Technical vulnerability discovery process""; ""A brief on human vulnerabilities""; ""The tactical exploitation cycle""; ""Front, flank, simple, complex""; ""The strategies of creating gaps"" ""Chapter 6: Synthetic Evaluation of Risks""""Risk, uncertainty and ugly Black Swans""; ""On suitable risk analysis methodologies""; ""On treatment of information security risks""; ""Relevant vulnerability categories""; ""Gauging attacker skill""; ""Weighting vulnerability impact""; ""Contemplating the vulnerability remedy""; ""Defining vulnerability risk level""; ""Risks faced by large components""; ""Compound risks, systempunkts and attacker logic""; ""Total risk summary utilisation and dissection""; ""Chapter 7: Presenting the Outcome and Follow-Up Acts""; ""The report audience and style"" ""The report summary""""The report interpretation chapter""; ""The bulk of the report""; ""Explaining the overall security state""; ""Elaborating on breakdown of risks""; ""Using vulnerability origin investigations""; ""Post-audit assistance and follow-up hurdles""; ""Chapter 8: Reviewing Security Assessment Failures and Auditor Management Strategies""; ""Bad tactics and poor tests""; ""On the assessment team ordnance""; ""Of serpents and eagles""; ""ITG Resources""