Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value

Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose "sample selected extreme learning machine" is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value

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

This paper studies intrusion detection for fog computing in fog radio access networks (F-RANs). As fog nodes are resource constrained, a traditional intrusion detection system (IDS) cannot be directly deployed in F-RANs due to the communication overhead and computational complexity. To address this challenge, we propose a skyline query-based scheme that can analyze the IDS log statistics of fog nodes and provide a complete data processing flow. Specifically, a three-step solution is proposed. First, a lightweight fog node filtering strategy is proposed to filter the raw data, which can reduce the fog-cloud communication overhead. Second, a sliding-window-based mechanism is developed in the cloud server to efficiently process the asynchronous data flow. Then, using the pre-processed data, a set of seriously attacked nodes will be identified by the skyline query. Third, the security threat level of each individual fog node is calculated using the unascertained measure, which can determine the degree of security threat. The numerical simulations show that the proposed scheme can significantly reduce communication overhead and computational complexity

A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing

Fog computing is an emerging network paradigm. Due to its characteristics (e.g., geo-location and constrained resource), fog computing is subject to a broad range of security threats. Intrusion detection system (IDS) is an essential security technology to deal with the security threats in fog computing. We have introduced a fog computing IDS (FC-IDS) framework in our previous work. In this paper, we study the optimal intrusion response strategy in fog computing based on the FC-IDS scheme proposed in our previous work. We postulate the intrusion process in fog computing and describe it with a mathematical model based on differential game theory. According to this model, the optimal response strategy is obtained corresponding to the optimal intrusion strategy. Theoretical analysis and simulation results demonstrate that our security model can effectively stabilize the intrusion frequency of the invaders in fog computing