Research of Wireless Sensor Network Intrusion Detection Based on Ant-Miner

2014 ◽  
Vol 989-994 ◽  
pp. 4832-4836
Author(s):  
Tao Liu ◽  
Shao Yu Liu ◽  
Dan Wei ◽  
Jie Cui
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Wireless Sensor ◽  
Network Intrusion Detection ◽  
Detection Scheme ◽  
Network Intrusion ◽  
Positive Rate

In this paper, we propose an intrusion detection program based on improved Ant-Miner (AM). The proposal needs to collecting out the node data, using intrusion detection module to test, compared with other wireless sensor network intrusion detection scheme, this scheme saves energy consumption of the sensor node effectively. Through the network simulation, this scheme proposed has a lower false positive rate and a higher true positive rate comparing with the current typical wireless sensor network testing program.

scholarly journals Research on Wireless Sensor Network Intrusion Detection Technology

2016 ◽  
Vol 44 ◽  
pp. 01053
Author(s):  
Qing Gang Fan ◽  
Li Wang ◽  
Yun Jie Zhu ◽  
Yan Ning Cai ◽  
Yong Qiang Li
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Detection Technology

scholarly journals Semi-supervised tri-Adaboost algorithm for network intrusion detection

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771984605 ◽  
Author(s):  
Yali Yuan ◽  
Liuwei Huo ◽  
Yachao Yuan ◽  
Zhixiao Wang
Keyword(s):  
Intrusion Detection ◽  
Categorical Data ◽  
False Positive Rate ◽  
Training Dataset ◽  
Network Intrusion Detection ◽  
Chi Square ◽  
Adaboost Algorithm ◽  
Numerical Studies ◽  
Network Intrusion ◽  
Positive Rate

Network intrusion detection is a relatively mature research topic, but one that remains challenging particular as technologies and threat landscape evolve. Here, a semi-supervised tri-Adaboost (STA) algorithm is proposed. In the algorithm, three different Adaboost algorithms are used as the weak classifiers (both for continuous and categorical data), constituting the decision stumps in the tri-training method. In addition, the chi-square method is used to reduce the dimension of feature and improve computational efficiency. We then conduct extensive numerical studies using different training and testing samples in the KDDcup99 dataset and discover the flows demonstrated that (1) high accuracy can be obtained using a training dataset which consists of a small number of labeled and a large number of unlabeled samples. (2) The algorithm proposed is reproducible and consistent over different runs. (3) The proposed algorithm outperforms other existing learning algorithms, even with only a small amount of labeled data in the training phase. (4) The proposed algorithm has a short execution time and a low false positive rate, while providing a desirable detection rate.

An Intrusion Detection Scheme for Home Wireless Sensor Networks

2011 ◽  
Vol 121-126 ◽  
pp. 3799-3804
Author(s):  
Min Wei ◽  
Kee Wook Rim ◽  
Kee Cheon Kim
Keyword(s):  
Intrusion Detection ◽  
Wireless Sensor ◽  
Network Intrusion Detection ◽  
Home Networks ◽  
Wireless Data ◽  
Detection Scheme ◽  
Security Enhancement ◽  
Network Intrusion ◽  
Wireless Home Networks ◽  
Multi Agents

In this paper, we propose an intrusion detection framework through multi-agents scheme for wireless home automation networks. Our mechanisms include the wireless sensor network intrusion detection architecture and an intrusion detection scheme for security enhancement. For the performance evaluation of our mechanism, we use the wireless data measured on the real wireless home networks. The simulation results show that the security manager detect the intrusion attack to improve the whole performance of the system, and can prolong the lifetime of the network.

scholarly journals Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Jin
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Short Term Memory ◽  
Wireless Sensor ◽  
Feature Subset ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Network Intrusion

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation-based intrusion detection model for long- and short-term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long- and short-term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning-based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two-way long- and short-term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long- and short-term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

scholarly journals LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xin Li ◽  
Peng Yi ◽  
Wei Wei ◽  
Yiming Jiang ◽  
Le Tian
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
False Positive Rate ◽  
Optimal Solution ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Krill Herd ◽  
Positive Rate

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

An active detection of compromised nodes based on en-route trap in wireless sensor network

2021 ◽  
Vol 17 (8) ◽  
pp. 155014772110403
Author(s):  
Jiang-Tao Wang ◽  
Zhi-Xiong Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
False Positive Rate ◽  
Trust Model ◽  
Wireless Sensor ◽  
Authentication Codes ◽  
Positive Rate ◽  
Forwarding Node ◽  
Active Detection ◽  
Compromised Nodes

With the development and wide use of wireless sensor network, security arises as an essential issue since sensors with restrict resources are deployed in wild areas in an unattended manner. Most of current en-route filtering schemes could filter false data effectively; however, the compromised nodes could take use of the filtering scheme to launch Fictitious False data Dropping attack, the detection of this attack is extremely difficult since the previous hop node is unable to distinguish whether the forwarding node dropt a false data report with incorrect Message Authentication Codes or a legitimate report. This is the first attempt to address the Fictitious False data Dropping attack; in this article, we propose an Active Detection of compromised nodes based on En-route Trap to trap compromised nodes in the scenario of a false data dropping. A trust model is used to evaluate trust level of neighbor nodes with respect to their authentication behaviors. A detecting algorithm of compromised node is used to detect compromised nodes. Simulation results showed that our scheme can address the Fictitious False data Dropping attack and detect 60% of compromised nodes with a low false positive rate; consequently, the packet accuracy of an Active Detection of compromised nodes based on En-route Trap increases rapidly and reaches to 86%.

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