A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading

Dbouk, Toufic; Mourad, Azzam; Otrok, Hadi; Tout, Hanine; Talhi, Chamseddine

doi:10.1109/tnsm.2019.2939221

Cited by 66 publications

(30 citation statements)

References 39 publications

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“…Based on the vocabulary list of Pascal, the resulted document is a vector with the size of 1 × 2788. In the vector, the {1, 2,8,9,15,20,1348,1350,1359,1363,1365,1394,1396,1425,1442,1452,1455,1458,1461,1465,1466,1467, 2788} digits are set as {100, 100, 100, 100, 200, 199, 1, 200, 200, 100, 100, 100, 100, 200, 200, 200, 200, 200, 200, 199, 1, 199, 1}, respectively. Note that this session should come from a test session because it contains IL_others, IC_others, and PS_others values.…”

Section: Data Preprocessmentioning

confidence: 99%

“…e security issues in MEC include application layer security, network layer security, data security, and node security. Intrusion detection systems (IDSs) protect the network layer security for MEC and have been an important component in it [2]. ere are two methods to detect intrusions in general, i.e., signature-based method and anomaly-based method.…”

Section: Introductionmentioning

confidence: 99%

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Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing

Cao

Chen

2020

Security and Communication Networks

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In this paper, a network intrusion detection system is proposed using Bayesian topic model latent Dirichlet allocation (LDA) for mobile edge computing (MEC). The method employs tcpdump packets and extracts multiple features from the packet headers. The tcpdump packets are transferred into documents based on the features. A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic. Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic. A threshold is defined in the training phase as the minimum likelihood of a host. In the test phase, when a host’s test traffic has a likelihood lower than the host’s threshold, the traffic is labeled as an intrusion. The intrusion detection system is validated using DARPA 1999 dataset. Experiment shows that our method is suitable to protect the security of MEC.

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Section: Data Preprocessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing

Cao

Chen

2020

Security and Communication Networks

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“…The WebGIS system in this paper refers to the principle of device-enhanced MEC by enabling user terminals as edge nodes for connection establishment [45]- [46]…”

Section: Device-enhanced Mecmentioning

confidence: 99%

Efficient, Customizable and Edge-Based WebGIS System

He¹,

Zhu

2020

IEEE Access

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Nowadays there is a wide range of applications for WebGIS which can add great value to modern economic, and building WebGIS system for specific scenarios is the common requirement of the industry. While currently separate WebGIS systems are deployed at different sites and operated by different owners, each of which has the whole set of functionalities of WebGIS, and thus introduce high cost of development and maintenance, which is a waste of resources as most of the functionalities are the same or similar. An edge computing based WebGIS architecture is proposed in the paper to meet the need of customization by applying the idea of SaaS. In this distributed architecture, the resource load is reasonably balanced between the server and the browser, which improves the overall performance of the system. Also it utilizes edge computing to reduce the pressure on the server by sharing map tiles among WebGIS clients. The proposed WebGIS system can not only be well customized and personalized as it is edge computing base, but also be well usable for large number of visits due to its distributed feature. The experiments show 5 concurrent requests per second, as well as response speed increases by more than 38.6% against traditional deployment. INDEX TERMS edge computing, customizable, WebGIS, high performance, map tile sharing, deviceenhanced MEC.

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“…Second is the way of distributing the services on different heterogeneous available resources, which is mapped to the container placement problem in our previous work [6]. Various approaches in the literature have also considered solving the problem of placing tasks on resources [17][18][19], where heuristics was proved to take an accurate selection and placement decisions. However, predicting the right time and best place to assign the service to remains an open problem because of the nondeterministic user behavior and random requests of service assignments.…”

Section: Fog Computing Existing Challengesmentioning

confidence: 99%

Reinforcement R-learning model for time scheduling of on-demand fog placement

2019

Self Cite

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On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. Nevertheless, taking a real-life scenario, the cloud might limit the number of fogs to place for minimizing the complexity of monitoring a large number of fogs and cost for volunteers that do not offer their resources for free. This implies choosing the right time and best volunteer to create a fog which the cloud can benefit from is essential. This choice is subject to study the demand of a particular location for services in order to maximize the resources utilization of these fogs. A simple algorithm will not be able to explore randomly changing users' demands. Therefore, there is a need for an intelligent model capable of scheduling fog placement based on the user's requests. In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters and produces a suitable fog placement schedule based on the concept of average reward. Our model aims to decrease the cloud's load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud's load. We also demonstrate the ability of our model to improve over time by adapting the new demand of users. Experiments comparing the decisions of our model with two other potential fog placement approaches used for task/service scheduling (threshold based and random based) show that the number of processed requests performed by the cloud decreases from 100 to 30% with a limited number of fogs to push. These results demonstrate that our proposed Fog Scheduling Decision model plays a crucial role in the placement of the on-demand fog to the right location at the right time while taking into account the user's needs.

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A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading

Cited by 66 publications

References 39 publications

Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing

Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing

Efficient, Customizable and Edge-Based WebGIS System

Reinforcement R-learning model for time scheduling of on-demand fog placement

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