Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly availab...Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.展开更多
Mobile Ad-Hoc Networks (MANETs) are highly vulnerable to insider jamming attacks. Several approaches to detect insider jammers in MANET have been proposed. However, once the insider jammer is detected and removed from...Mobile Ad-Hoc Networks (MANETs) are highly vulnerable to insider jamming attacks. Several approaches to detect insider jammers in MANET have been proposed. However, once the insider jammer is detected and removed from the network, it is possible for the insider jammer to leverage the knowledge of insider information to launch a future attack. In this paper, we focus on collaborative smart jamming attacks, where the attackers who have been detected as insider jammers in a MANET, return to attack the MANET based on the knowledge learned. The MANET uses a reputation-based coalition game to detect insider jammers. In the collaborative smart jamming attack, two or more smart jammers will form a coalition to attack the coalitions in the MANET. The smart jammers were detected and then excluded from their initial coalition, they then regrouped to start their own coalition and share previously gained knowledge about legitimate nodes in their erstwhile coalition with the aim of achieving a highly coordinated successful jamming attack on the legitimate coalition. The success of the attack largely depends on the insider jammer’s collective knowledge about the MANET. We present a technique to appropriately represent knowledge gathered by insider jammers which would lead to a successful attack. Simulation results in NS2 depict that coalition of jammers can leverage past knowledge to successfully attack MANET.展开更多
文摘Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.
文摘Mobile Ad-Hoc Networks (MANETs) are highly vulnerable to insider jamming attacks. Several approaches to detect insider jammers in MANET have been proposed. However, once the insider jammer is detected and removed from the network, it is possible for the insider jammer to leverage the knowledge of insider information to launch a future attack. In this paper, we focus on collaborative smart jamming attacks, where the attackers who have been detected as insider jammers in a MANET, return to attack the MANET based on the knowledge learned. The MANET uses a reputation-based coalition game to detect insider jammers. In the collaborative smart jamming attack, two or more smart jammers will form a coalition to attack the coalitions in the MANET. The smart jammers were detected and then excluded from their initial coalition, they then regrouped to start their own coalition and share previously gained knowledge about legitimate nodes in their erstwhile coalition with the aim of achieving a highly coordinated successful jamming attack on the legitimate coalition. The success of the attack largely depends on the insider jammer’s collective knowledge about the MANET. We present a technique to appropriately represent knowledge gathered by insider jammers which would lead to a successful attack. Simulation results in NS2 depict that coalition of jammers can leverage past knowledge to successfully attack MANET.
