Node State Monitoring Scheme in Fog Radio Access Networks for Intrusion Detection

An, Xueli; Xing, Lu; Yang, Lei; Zhou, Xianwei; Lin, Fuhong

doi:10.1109/access.2019.2899017

Cited by 15 publications

(11 citation statements)

References 36 publications

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“…Figure 16 shows a 3D figure for the accuracy rate per class for TIDCS as a function of time for TVSU = 300. First, we can remark that TIDCS has a 100% accuracy for 6 class classifications which are (0,4,5,6,7,8) and has a weak detection performance for the rest (1,2,3,9). In particular, class 10 has 0 packets detected from 100 received.…”

Section: F Tidcs Vs Tidcs-amentioning

confidence: 95%

“…where the attacker can steal property or information for a personal gain), a fog administrator, who can manage fog instances, can instantiate a rogue fog instance rather than a legitimate one. The existence of fake fog node presents a big threat to personal data privacy and security [3]. Furthermore, the virtual machine instances are dynamically created/added/removed which makes it hard to maintain a blacklist of rogue nodes and to establish longterm trust between nodes.…”

Section: Introductionmentioning

confidence: 99%

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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

et al. 2020

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Machine learning techniques are becoming mainstream in intrusion detection systems as they allow real-time response and have the ability to learn and adapt. By using a comprehensive dataset with multiple attack types, a well-trained model can be created to improve the anomaly detection performance. However, high dimensional data present a significant challenge for machine learning techniques. Processing similar features that provide redundant information increases the computational time, which is a critical problem especially for users with constrained resources (battery, energy). In this paper, we propose two models for intrusion detection and classification scheme Trust-based Intrusion Detection and Classification System (TIDCS) and Trust-based Intrusion Detection and Classification System-Accelerated (TIDCS-A) for secure network. TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. Initially, the features are grouped randomly to increase the probability of making them participating in the generation of different groups, and sorted based on their accuracy scores. Only the high ranked features are then selected to obtain a classification for any received packet from the nodes in the network, which is saved as part of the node's past performance. TIDCS proposes a periodic system cleansing where trust relationships between participant nodes are evaluated and renewed periodically. TIDCS-A proposes a dynamic algorithm to compute the exact time for nodes cleansing states and restricts the exposure window of the nodes. The final classification decision for both models is estimated by incorporating the node's past behavior with the machine learning algorithm. Any detected attack reduces the trustworthiness of the nodes involved, leading to a dynamic system cleansing. An evaluation of TIDCS and TIDCS-A using the NSL-KDD and UNSW datasets shows that both models can detect malicious behaviors providing higher accuracy, detection rates, and lower false alarm than state-of-art techniques. For instance, for UNSW dataset, the accuracy detection is 91% for TICDS, 83.47%by using online AODE, 88% for CADF, 90% for EDM, 90% for TANN and 69.6% for NB. Consequently, TICDS has better performance than the state of art techniques in terms of accuracy detection, while providing good detection and false alarm rates.

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Section: F Tidcs Vs Tidcs-amentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

et al. 2020

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“…In this section, we implement this process with an Ed-Max filtering strategy (EMFS) based on Skyline query. Ed-Max algorithm was first proposed in [41] and the authors proved that the data filtered by this algorithm will not affect the global Skyline set. In addition, the filtering effect of this algorithm is better than that of MinMax algorithm.…”

Section: E Ed-max Filtering Strategymentioning

confidence: 99%

“…In the following, we introduce the content recommendation algorithm pseudocode in Algorithm 1. In this algorithm, the step 3 is similar to the fog node filtering strategy (FNFS) proposed in [41], for both of them need to find the optimal Euclidean distance.…”

Section: E Ed-max Filtering Strategymentioning

confidence: 99%

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

et al. 2019

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With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived. Vehicle networking technology can not only facilitate people's travel but also effectively alleviate traffic congestion. The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV). Intelligent navigator is a very important part of human-computer interaction in IoV. It carries a large number of tasks of recommending content for users. In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. First, the user data are analyzed to establish the association rule mining algorithm. Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user. From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy. INDEX TERMS Content recommendation, association rules, Internet of Vehicles, fog computing.

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“…Intrusion detection has been also possible by analyzing IDS log statistics in the fog nodes with a query-based strategy plus uncertainty tests to calculate the degree of a potential threat. This approach was tested for fog radio access networks (F-RANs) [8]. A different approach reported an IDS architecture for edge computing.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

et al. 2020

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The Internet of Things (IoT) represents a mean to share resources (memory, storage computational power, data, etc.) between computers and mobile devices, as well as buildings, wearable devices, electrical grids, and automobiles, just to name few. The IoT is leading to the development of advanced information services that will require large storage and computational power, as well as real-time processing capabilities. The integration of IoT with emerging technologies such as Fog Computing can complement these requirements with pervasive and cost-effective services capable of processing largescale geo-distributed information. In any IoT application, communication availability is essential to deliver accurate and useful information, for instance, to take actions during dangerous situations, or to manage critical infrastructures. IoT components like gateways, also called Fog Nodes, face outstanding security challenges as the attack surface grows with the number of connected devices requesting communication services. These Fog nodes can be targeted by an attacker, preventing the nodes from delivering important information to the final users or to perform accurate automated actions. This paper introduces an Anomaly Behavior Analysis Methodology based on Artificial Neural Networks, to implement an adaptive Intrusion Detection System (IDS) capable of detecting when a Fog node has been compromised, and then take the required actions to ensure communication availability. The experimental results reveal that the proposed approach has the capability for characterizing the normal behavior of Fog Nodes despite its complexity due to the adaptive scheme, and also has the capability of detecting anomalies due to any kind of sources such as misuses, cyber-attacks or system glitches, with high detection rate and low false alarms. INDEX TERMS Anomaly behavior, cyber security, fog computing, IoT, neural networks.

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Node State Monitoring Scheme in Fog Radio Access Networks for Intrusion Detection

Cited by 15 publications

References 36 publications

TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

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