The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Eugene J Obrien - One of the best experts on this subject based on the ideXlab platform.
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a methodology for calculating congested Traffic Characteristic loading on long span bridges using site specific data
Computers & Structures, 2017Co-Authors: Alessandro Lipari, Colin Christopher Caprani, Eugene J ObrienAbstract:Abstract A probabilistic methodology is developed for the evaluation of Characteristic maximum Traffic load on long-span bridges for which congestion is the governing condition. It considers all the congestion types that can occur such as stop-and-go waves and oscillating congested Traffic. The approach uses site-specific Traffic data, such as flow and incident data. Statistics on driver behaviour and Traffic incident frequency are assembled from the literature. As an example application of the method, a calibrated Traffic microsimulation model is used to obtain the input data for the proposed methodology. The results counter many of the prevailing assumptions for long-span bridge loading.
George Yannis - One of the best experts on this subject based on the ideXlab platform.
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combined impact of road and Traffic Characteristic on driver behavior using smartphone sensor data
Accident Analysis & Prevention, 2020Co-Authors: Virginia Petraki, Apostolos Ziakopoulos, George YannisAbstract:The objective of this research is to exploit high resolution driving behavior data collected via sensors of smartphones from 303 drivers in order to examine driver behavior at road segment and junction level. These sensor data are combined with Traffic and road geometry Characteristics and subsequently depicted spatially using Geographical Information System software. Events of harsh driver behavior (8592 harsh accelerations and 3946 harsh brakings) were mapped to delimited segments and junctions of two urban expressways in Athens, Greece. For the analysis, two multiple linear regression models and two log-linear regression models were developed. Results indicate that in road segments there is an increase in the number of harsh events if average Traffic flow per lane increases in the respective areas. Furthermore, as the average occupancy increases in junctions, there is an increase in harsh accelerations, and as the average speed increases, more harsh deceleration events occur. It is evident that Traffic Characteristics (Traffic flow & speed) have the most statistically significant impact on the frequency of harsh events compared to factors related to road geometry and driver behavior.
Sreekar Shenoy Govind - One of the best experts on this subject based on the ideXlab platform.
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Architecture support for intrusion detection systems
Universitat Politècnica de Catalunya, 2012Co-Authors: Sreekar Shenoy GovindAbstract:System security is a prerequisite for efficient day-to-day transactions. As a consequence, Intrusion Detection Systems (IDS) are commonly used to provide an effective security ring to systems in a network. An IDS operates by inspecting packets flowing in the network for malicious content. To do so, an IDS like Snort[49] compares bytes in a packet with a database of prior reported attacks. This functionality can also be viewed as string matching of the packet bytes with the attack string database. Snort commonly uses the Aho-Corasick algorithm[2] to detect attacks in a packet. The Aho-Corasick algorithm works by first constructing a Finite State Machine (FSM) using the attack string database. Later the FSM is traversed with the packet bytes. The main advantage of this algorithm is that it provides a linear time search irrespective of the number of strings in the database. The issue however lies in devising a practical implementation. The FSM thus constructed gets very bloated in terms of the storage size, and so is area inefficient. This also affects its performance efficiency as the memory footprint also grows. Another issue is the limited scope for exploiting any parallelism due to the inherent sequential nature in a FSM traversal. This thesis explores hardware and software techniques to accelerate attack detection using the Aho-Corasick algorithm. In the first part of this thesis, we investigate techniques to improve the area and performance efficiency of an IDS. Notable among our contributions, includes a pipelined architecture that accelerates accesses to the most frequently accessed node in the FSM. The second part of this thesis studies the resilience of an IDS to evasion attempts. In an evasion attempt an adversary saturates the performance of an IDS to disable it, and thereby gain access to the network. We explore an evasion attempt that significantly degrades the performance of the Aho-Corasick al- gorithm used in an IDS. As a counter measure, we propose a parallel architecture that improves the resilience of an IDS to an evasion attempt. The final part of this thesis explores techniques to exploit the network Traffic Characteristic. In our study, we observe significant redundancy in the payload bytes. So we propose a mechanism to leverage this redundancy in the FSM traversal of the Aho-Corasick algorithm. We have also implemented our proposed redundancy-aware FSM traversal in Snort
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Architecture support for intrusion detection systems
Universitat Politècnica de Catalunya, 2012Co-Authors: Sreekar Shenoy GovindAbstract:System security is a prerequisite for efficient day-to-day transactions. As a consequence, Intrusion Detection Systems (IDS) are commonly used to provide an effective security ring to systems in a network. An IDS operates by inspecting packets flowing in the network for malicious content. To do so, an IDS like Snort[49] compares bytes in a packet with a database of prior reported attacks. This functionality can also be viewed as string matching of the packet bytes with the attack string database. Snort commonly uses the Aho-Corasick algorithm[2] to detect attacks in a packet. The Aho-Corasick algorithm works by first constructing a Finite State Machine (FSM) using the attack string database. Later the FSM is traversed with the packet bytes. The main advantage of this algorithm is that it provides a linear time search irrespective of the number of strings in the database. The issue however lies in devising a practical implementation. The FSM thus constructed gets very bloated in terms of the storage size, and so is area inefficient. This also affects its performance efficiency as the memory footprint also grows. Another issue is the limited scope for exploiting any parallelism due to the inherent sequential nature in a FSM traversal. This thesis explores hardware and software techniques to accelerate attack detection using the Aho-Corasick algorithm. In the first part of this thesis, we investigate techniques to improve the area and performance efficiency of an IDS. Notable among our contributions, includes a pipelined architecture that accelerates accesses to the most frequently accessed node in the FSM. The second part of this thesis studies the resilience of an IDS to evasion attempts. In an evasion attempt an adversary saturates the performance of an IDS to disable it, and thereby gain access to the network. We explore an evasion attempt that significantly degrades the performance of the Aho-Corasick al- gorithm used in an IDS. As a counter measure, we propose a parallel architecture that improves the resilience of an IDS to an evasion attempt. The final part of this thesis explores techniques to exploit the network Traffic Characteristic. In our study, we observe significant redundancy in the payload bytes. So we propose a mechanism to leverage this redundancy in the FSM traversal of the Aho-Corasick algorithm. We have also implemented our proposed redundancy-aware FSM traversal in Snort.Postprint (published version
Alessandro Lipari - One of the best experts on this subject based on the ideXlab platform.
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a methodology for calculating congested Traffic Characteristic loading on long span bridges using site specific data
Computers & Structures, 2017Co-Authors: Alessandro Lipari, Colin Christopher Caprani, Eugene J ObrienAbstract:Abstract A probabilistic methodology is developed for the evaluation of Characteristic maximum Traffic load on long-span bridges for which congestion is the governing condition. It considers all the congestion types that can occur such as stop-and-go waves and oscillating congested Traffic. The approach uses site-specific Traffic data, such as flow and incident data. Statistics on driver behaviour and Traffic incident frequency are assembled from the literature. As an example application of the method, a calibrated Traffic microsimulation model is used to obtain the input data for the proposed methodology. The results counter many of the prevailing assumptions for long-span bridge loading.
Virginia Petraki - One of the best experts on this subject based on the ideXlab platform.
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combined impact of road and Traffic Characteristic on driver behavior using smartphone sensor data
Accident Analysis & Prevention, 2020Co-Authors: Virginia Petraki, Apostolos Ziakopoulos, George YannisAbstract:The objective of this research is to exploit high resolution driving behavior data collected via sensors of smartphones from 303 drivers in order to examine driver behavior at road segment and junction level. These sensor data are combined with Traffic and road geometry Characteristics and subsequently depicted spatially using Geographical Information System software. Events of harsh driver behavior (8592 harsh accelerations and 3946 harsh brakings) were mapped to delimited segments and junctions of two urban expressways in Athens, Greece. For the analysis, two multiple linear regression models and two log-linear regression models were developed. Results indicate that in road segments there is an increase in the number of harsh events if average Traffic flow per lane increases in the respective areas. Furthermore, as the average occupancy increases in junctions, there is an increase in harsh accelerations, and as the average speed increases, more harsh deceleration events occur. It is evident that Traffic Characteristics (Traffic flow & speed) have the most statistically significant impact on the frequency of harsh events compared to factors related to road geometry and driver behavior.