• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.

• ETRI Journal
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• v.38 no.6
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• pp.1145-1152
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• 2016

Because the nodes in a wireless sensor network (WSN) are mobile and the network is highly dynamic, monitoring every node at all times is impractical. As a result, an intruder can attack the network easily, thus impairing the system. Hence, detecting anomalies in the network is very essential for handling efficient and safe communication. To overcome these issues, in this paper, we propose a rule-based anomaly detection technique using roaming honeypots. Initially, the honeypots are deployed in such a way that all nodes in the network are covered by at least one honeypot. Honeypots check every new connection by letting the centralized administrator collect the information regarding the new connection by slowing down the communication with the new node. Certain predefined rules are applied on the new node to make a decision regarding the anomality of the node. When the timer value of each honeypot expires, other sensor nodes are appointed as honeypots. Owing to this honeypot rotation, the intruder will not be able to track a honeypot to impair the network. Simulation results show that this technique can efficiently handle the anomaly detection in a WSN.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.2
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• pp.279-286
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• 2013

The recent power shortages due to surge in demand for electricity highlights the importance of smart grid technologies for efficient use of power. The experimental content for vulnerability against availability of smart meter, an essential component in smart grid networks, has been reported. Designing availability protection mechanism to boost the realization possibilities of the secure smart grid is essential. In this paper, we propose a mechanism to detect the availability infringement attack for smart meter and also to find anomaly nodes through analyzing smart grid structure and traffic patterns. The proposed detection mechanism uses approximate entropy technique to decrease the detection load and increase the detection rate with few samples and utilizes the signal information(CIR or RSSI, etc.) that the anomaly node can not be changed to find the anomaly nodes. Finally simulation results of proposed method show that the detection performance and the feasibility.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.77-83
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• 2010

Vehicular Ad Hoc Networks are self-organizing Peer-to-Peer networks that typically have highly mobile vehicle nodes, moving at high speeds, very short-lasting and unstable communication links. VANETs are formed without fixed infrastructure, central administration, and dedicated routing equipment, and network nodes are mobile, joining and leaving the network over time. So, VANET-security is very vulnerable for the intrusion of malicious and misbehaving nodes in the network, since VANETs are mostly open networks, allowing everyone connect, without centralized control. In this paper, we propose a rough set based anomaly detection method that efficiently identify malicious behavior of vehicle node activities in these VANETs, and the performance of a proposed scheme is evaluated by a simulation in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.99-106
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• 2011

In many applications, wireless sensor networks could be thought as data-centric networks, and the sensor nodes are densely distributed over a large sensor field. The sensor nodes are normally vulnerable in terms of security since they are very often deployed in a hostile environment and open space. In this paper, we propose a scheme for contents-based anomaly detection in wireless sensor networks. In this scheme we use the characteristics of sensor networks where several nodes surrounding an event point can simultaneously detect the phenomenon occurring and the contents detected from these sensors are limited to inside a certain range. The proposed scheme consists of several phases; training, testing and refining phases. Anomaly candidates detected by the distance-based anomaly detection scheme in the testing phase are sent to the refining phase. They are then compared in the sink node with previously collected data set to improve detection performance in the refining phase. Our simulation results suggest the effectiveness of the proposed scheme in this paper evidenced by the improvements of the detection rate and the false positive rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.953-959
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• 2012

There are a number of idle nodes in sensor networks, these can act as detector nodes for anomaly detection in the network. For detecting node selection problem modeled as optimization equation, the conventional method using centralized genetic algorithm was evaluated. In this paper, a method to improve the convergence of the optimal value, while improving energy efficiency as a method of considering the characteristics of the network topology using parallel genetic algorithm is proposed. Through simulation, the proposed method compared with the conventional approaches to the convergence of the optimal value was improved and was found to be energy efficient.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.41-47
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• 2012

MANET has vulnerable structure on security owing to structural characteristics as follows. MANET consisted of moving nodes is that every nodes have to perform function of router. Every node has to provide reliable routing service in cooperation each other. These properties are caused by expose to various attacks. But, it is difficult that position of environment intrusion detection system is established, information is collected, and particularly attack is detected because of moving of nodes in MANET environment. It is not easy that important profile is constructed also. In this paper, conformal predictor - support vector machine(CP-SVM) based intrusion detection technique was proposed in order to do more accurate and efficient intrusion detection. In this study, IDS-agents calculate p value from collected packet and transmit to cluster head, and then other all cluster head have same value and detect abnormal behavior using the value. Cluster form of hierarchical structure was used to reduce consumption of nodes also. Effectiveness of proposed method was confirmed through experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2171-2180
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• 2011

Security is a significant concern for many sensor network applications. Intrusion detection is one method of defending against attacks. However, standard intrusion detection techniques are not suitable for sensor networks with limited resources. In this paper, propose a new method for selecting and managing the detect nodes in IDS(intrusion detection system) for anomaly detection in sensor networks and the node scheduling technique for maximizing the IDS's lifetime. Using the genetic algorithm, developed the solutions for suggested optimization equation and verify the effectiveness of proposed methods by simulations.