The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
William H. Sanders - One of the best experts on this subject based on the ideXlab platform.
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SRDS - An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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a game theoretic approach to respond to attacker Lateral Movement
Decision and Game Theory for Security, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
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GameSec - A Game-Theoretic Approach to Respond to Attacker Lateral Movement
Lecture Notes in Computer Science, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
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SRDS - Lateral Movement Detection Using Distributed Data Fusion
2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016Co-Authors: Ahmed Fawaz, Atul Bohara, Carmen Cheh, William H. SandersAbstract:Attackers often attempt to move Laterally from host to host, infecting them until an overall goal is achieved. One possible defense against this strategy is to detect such coordinated and sequential actions by fusing data from multiple sources. In this paper, we propose a framework for distributed data fusion that specifies the communication architecture and data transformation functions. Then, we use this framework to specify an approach for Lateral Movement detection that uses host-level process communication graphs to infer network connection causations. The connection causations are then aggregated into system-wide host-communication graphs that expose possible Lateral Movement in the system. In order to provide a balance between the resource usage and the robustness of the fusion architecture, we propose a multilevel fusion hierarchy that uses different clustering techniques. We evaluate the scalability of the hierarchical fusion scheme in terms of storage overhead, number of message updates sent, fairness of resource sharing among clusters, and quality of local graphs. Finally, we implement a host-level monitor prototype to collect connection causations, and evaluate its overhead. The results show that our approach provides an effective method to detect Lateral Movement between hosts, and can be implemented with acceptable overhead.
Ahmed Fawaz - One of the best experts on this subject based on the ideXlab platform.
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SRDS - An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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a game theoretic approach to respond to attacker Lateral Movement
Decision and Game Theory for Security, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
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GameSec - A Game-Theoretic Approach to Respond to Attacker Lateral Movement
Lecture Notes in Computer Science, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
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SRDS - Lateral Movement Detection Using Distributed Data Fusion
2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016Co-Authors: Ahmed Fawaz, Atul Bohara, Carmen Cheh, William H. SandersAbstract:Attackers often attempt to move Laterally from host to host, infecting them until an overall goal is achieved. One possible defense against this strategy is to detect such coordinated and sequential actions by fusing data from multiple sources. In this paper, we propose a framework for distributed data fusion that specifies the communication architecture and data transformation functions. Then, we use this framework to specify an approach for Lateral Movement detection that uses host-level process communication graphs to infer network connection causations. The connection causations are then aggregated into system-wide host-communication graphs that expose possible Lateral Movement in the system. In order to provide a balance between the resource usage and the robustness of the fusion architecture, we propose a multilevel fusion hierarchy that uses different clustering techniques. We evaluate the scalability of the hierarchical fusion scheme in terms of storage overhead, number of message updates sent, fairness of resource sharing among clusters, and quality of local graphs. Finally, we implement a host-level monitor prototype to collect connection causations, and evaluate its overhead. The results show that our approach provides an effective method to detect Lateral Movement between hosts, and can be implemented with acceptable overhead.
Atul Bohara - One of the best experts on this subject based on the ideXlab platform.
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SRDS - An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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SRDS - Lateral Movement Detection Using Distributed Data Fusion
2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016Co-Authors: Ahmed Fawaz, Atul Bohara, Carmen Cheh, William H. SandersAbstract:Attackers often attempt to move Laterally from host to host, infecting them until an overall goal is achieved. One possible defense against this strategy is to detect such coordinated and sequential actions by fusing data from multiple sources. In this paper, we propose a framework for distributed data fusion that specifies the communication architecture and data transformation functions. Then, we use this framework to specify an approach for Lateral Movement detection that uses host-level process communication graphs to infer network connection causations. The connection causations are then aggregated into system-wide host-communication graphs that expose possible Lateral Movement in the system. In order to provide a balance between the resource usage and the robustness of the fusion architecture, we propose a multilevel fusion hierarchy that uses different clustering techniques. We evaluate the scalability of the hierarchical fusion scheme in terms of storage overhead, number of message updates sent, fairness of resource sharing among clusters, and quality of local graphs. Finally, we implement a host-level monitor prototype to collect connection causations, and evaluate its overhead. The results show that our approach provides an effective method to detect Lateral Movement between hosts, and can be implemented with acceptable overhead.
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Lateral Movement Detection Using Distributed Data Fusion
2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016Co-Authors: Ahmed Fawaz, Atul Bohara, Carmen Cheh, William H. SandersAbstract:Attackers often attempt to move Laterally from host to host, infecting them until an overall goal is achieved. One possible defense against this strategy is to detect such coordinated and sequential actions by fusing data from multiple sources. In this paper, we propose a framework for distributed data fusion that specifies the communication architecture and data transformation functions. Then, we use this framework to specify an approach for Lateral Movement detection that uses host-level process communication graphs to infer network connection causations. The connection causations are then aggregated into system-wide host-communication graphs that expose possible Lateral Movement in the system. In order to provide a balance between the resource usage and the robustness of the fusion architecture, we propose a multilevel fusion hierarchy that uses different clustering techniques. We evaluate the scalability of the hierarchical fusion scheme in terms of storage overhead, number of message updates sent, fairness of resource sharing among clusters, and quality of local graphs. Finally, we implement a host-level monitor prototype to collect connection causations, and evaluate its overhead. The results show that our approach provides an effective method to detect Lateral Movement between hosts, and can be implemented with acceptable overhead.
Mohammad A. Noureddine - One of the best experts on this subject based on the ideXlab platform.
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SRDS - An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017Co-Authors: Atul Bohara, Ahmed Fawaz, Mohammad A. Noureddine, William H. SandersAbstract:Lateral Movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and Lateral Movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
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a game theoretic approach to respond to attacker Lateral Movement
Decision and Game Theory for Security, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
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GameSec - A Game-Theoretic Approach to Respond to Attacker Lateral Movement
Lecture Notes in Computer Science, 2016Co-Authors: Mohammad A. Noureddine, Ahmed Fawaz, William H. Sanders, Tamer BasarAbstract:In the wake of an increasing number in targeted and complex attacks on enterprise networks, there is a growing need for timely, efficient and strategic network response. Intrusion detection systems provide network administrators with a plethora of monitoring information, but that information must often be processed manually to enable decisions on response actions and thwart attacks. This gap between detection time and response time, which may be months long, may allow attackers to move freely in the network and achieve their goals. In this paper, we present a game-theoretic approach for automatic network response to an attacker that is moving Laterally in an enterprise network. To do so, we first model the system as a network services graph and use monitoring information to label the graph with possible attacker Lateral Movement communications. We then build a defense-based zero-sum game in which we aim to prevent the attacker from reaching a sensitive node in the network. Solving the matrix game for saddle-point strategies provides us with an effective way to select appropriate response actions. We use simulations to show that our engine can efficiently delay an attacker that is moving Laterally in the network from reaching the sensitive target, thus giving network administrators enough time to analyze the monitoring data and deploy effective actions to neutralize any impending threats.
Yasmin Ashaari - One of the best experts on this subject based on the ideXlab platform.
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The modelling of Lateral Movement of soft soil using finite element analysis and laboratory model / Juhaizad Ahmad, Nor Hazwani Md Zain and Prof. Madya Dr. Yasmin Ashaari
2020Co-Authors: Juhaizad Ahmad, Nor Hazwani Md. Zain, Yasmin AshaariAbstract:Lateral Movement of sheet pile driven in soft soils has been a major problem in geotechnical engineering. It was reported that the conventional theories such as Rankine and Coulomb theory has overpredicted the Lateral Movement, hence causing inaccuracy. On top of that, the behavior of Lateral Movement on soft soil is not well understood since it is complicated and involved many parameters. So, the objectives of this study are to understand the behavior of soft soil in terms of Lateral Movement and to measure the amount of Lateral Movement on soft soil using physical laboratory model and Finite Element Method, which is PL AXIS. Excavation work has been conducted on the physical model and at the same time the available models in PLAXIS software such as MohrCoulomb (MC), Hardening-Soil (HS), Soft Soil (SS) and Soft Soil Creep (SSC) model were employed to predict the amount of Movement during excavation. Based on the results, the amount of Lateral Movement is proportional to the depth of excavation. The Lateral Movement increases as the depth of excavation increases. Moreover, through physical modeling, it was found that the angle of wedge failure is the same as Rankine theory which is (45°+0/2). Besides that, through Finite Element Method, it was observed that the Soft Soil Creep model gives more accurate results since this model is specially design for soft soil problems. As a recommendion, this study can be enhanced by using centrifuge model due to its ability to replicate the real soil unit weight, therefore producing more reliable results. In terms of Finite Element Method, 3-D model should be engaged in order to obtain more accurate results.
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UKSim - Modelling of Lateral Movement in Soft Soil Using Hardening Soil Model
2011 UkSim 13th International Conference on Computer Modelling and Simulation, 2011Co-Authors: N.h. Zain, Juhaizad Ahmad, Yasmin Ashaari, E. Shaffie, N.k. MustaffaAbstract:Excavation in soft soil will lead to changes in stress state ground mass and subsequent ground losses inevitably occur. These changes affect the surrounding ground in the form of Lateral Movement which eventually impose direct strains onto nearby structures and can cause pile deviation and cracks. This study models the Lateral Movement in soft clay due to excavation using finite element simulation known asPLAXIS. Two types of isotropic models in PLAXIS are compared that are Hardening soil Model and Mohr Coulomb Model. The soft clay was collected from a site location in Selangor, Malaysia and the physical and engineering properties test were firstly determined. For simulation in PLAXIS, a geometry model which consists of sand and clay layer was used and a vertical sheet pile was driven downwards until bottom of the soil layer. Excavations were done in stages on one side of the soil retained induced horizontal displacement to the sheet pile until resulted failure to occur. From the results obtained, it can be concluded that Mohr-Coulomb model are almost equal to Hardening-Soil model in terms of horizontal displacements. However, for the soil stresses, the researcher found that the percentage differences between the both models are higher which is caused by characteristic of Hardening-Soil model that accounted fortress dependency of stiffness increase with pressure.
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Lateral Movement and settlement of sandwiched soft soil using physical model
2011 IEEE Colloquium on Humanities Science and Engineering, 2011Co-Authors: Juhaizad Ahmad, Nor Hazwani Md. Zain, Yasmin Ashaari, Abdul Samad Abdul RahmanAbstract:Soft marine clay is a well-known problematic soil in geotechnical engineering. Soft marine clay soil is one of the problematic soils which are commonly found along the coastal areas of West Malaysia. In Klang area where the samples are taken, the thickness of the soft marine clay may vary from 20 to 40m. The general physical properties of the soft soil are presented in this study. In general, the unit weight of the soft clay is about 14 to 16 kN/m3. The result for Liquid Limit (LL) of the soft clay is very high which about 50% to 150%. Plasticity Index (PI) varies from 20% to 80%. The compressibility properties of the soft soil are mainly assessed from the laboratory consolidation test results. This study involves the laboratory test studies where the specimen of soft soil is obtained in disturbed condition. Prior to the experimentation, the physical model is prepared. The physical model is under constant condition of exposure in the laboratory to ensure any possible errors pertaining to the surrounding environment are mostly eliminated. The results of this experiment show that the Lateral Movement of soft soil is proportional to the depth of normal excavation and failure occur parallel to the soft soil plane in the geotechnical tank. Maximum settlement occurs at the centre of tank. In conclusion, it can be accepted that the soft soil has a smooth characteristic and can cause Lateral Movement of the above soil layers by dispersing its mass horizontally.
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Modelling of Lateral Movement in Soft Soil Using Hardening Soil Model
2011 UkSim 13th International Conference on Computer Modelling and Simulation, 2011Co-Authors: Md. N.h. Zain, Juhaizad Ahmad, Yasmin Ashaari, E. Shaffie, N.k. MustaffaAbstract:Excavation in soft soil will lead to changes in stress state ground mass and subsequent ground losses inevitably occur. These changes affect the surrounding ground in the form of Lateral Movement which eventually impose direct strains onto nearby structures and can cause pile deviation and cracks. This study models the Lateral Movement in soft clay due to excavation using finite element simulation known asPLAXIS. Two types of isotropic models in PLAXIS are compared that are Hardening soil Model and Mohr Coulomb Model. The soft clay was collected from a site location in Selangor, Malaysia and the physical and engineering properties test were firstly determined. For simulation in PLAXIS, a geometry model which consists of sand and clay layer was used and a vertical sheet pile was driven downwards until bottom of the soil layer. Excavations were done in stages on one side of the soil retained induced horizontal displacement to the sheet pile until resulted failure to occur. From the results obtained, it can be concluded that Mohr-Coulomb model are almost equal to Hardening-Soil model in terms of horizontal displacements. However, for the soil stresses, the researcher found that the percentage differences between the both models are higher which is caused by characteristic of Hardening-Soil model that accounted fortress dependency of stiffness increase with pressure.
