• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• Journal of Nutrition and Health
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• v.48 no.3
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• pp.277-288
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• 2015

Purpose: As the rate of senior citizens living alone increases in the current aging society, there is much concern regarding the health and nutritional intake of solitary senior citizens. Therefore, this study compared the nutritional intake of senior citizens according to their family type. Methods: In July and August of 2011, two senior citizen welfare centers in Seoul were visited to survey 267 elderly women. Excluding 54 subjects for which the data were incomplete, information from 213 subjects was analyzed. The subjects were divided into three family types, living alone (LA, n = 74), living with spouse (LS, n = 78), and living with children (LC, n = 61). Results: The mean age of the LA group was the highest, while the mean age of the LS group was the lowest (p < 0.001), and WHR of the LC group was the highest (p = 0.049). Income was the highest in the LS group (p < 0.001). Frequency of eating out was the lowest in the LA group (p = 0.031). By Duncan's multiple analysis, the amounts of energy intake, vegetable protein, fat, calcium, phosphorus, potassium, selenium, Vit D, Vit E, $Vit\;B_2$, niacin, $Vit\;B_6$, $Vit\;B_{12}$, and cholesterol were significantly higher in the LS group compared with the LA or LC group (p < 0.05). The intakes of calcium, Vit D, $Vit\;B_{12}$, and cholesterol were still significantly different among the three groups, even after adjustment for age and monthly income. The LA group ate less fruit and fish than the LS or LC group (p < 0.05). The LA group showed the lowest dietary diversity and the LS group showed the highest diversity (p = 0.014), however, the significance of dietary diversity score among the three groups disappeared after adjustment for age and monthly income. Conclusion: Elderly women living with spouse were receiving better nutrition than elderly women living alone or living with children. Therefore, solitary elderly women who do not live with their spouse or children should be offered greater opportunities to receive a balanced meal at a congregational kitchen or welfare center. To ensure their healthy diet, it is essential to provide continuous nutrition education with these groups in mind.