B.3. Communication/Networking and Information Technology
Roya Morshedi; S. Mojtaba Matinkhah; Mohammad Taghi Sadeghi
Abstract
IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning ...
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IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning techniques and pristine data. We utilize the publicly available CICIDS2017 dataset, which enables comprehensive training and testing of intrusion detection models across various attack scenarios, such as Distributed Denial of Service (DDoS) attacks, port scans, botnet activity, and more. Our goal is to provide a more effective method than the previous methods. Our proposed deep learning model incorporates dense transition layers and LSTM architecture, designed to capture both spatial and temporal dependencies within the data. We employed rigorous evaluation metrics, including sparse categorical cross-entropy loss and accuracy, to assess model performance. The results of our approach show outstanding accuracy, reaching a peak of 0.997 on the test data. Our model demonstrates stability in loss and accuracy metrics, ensuring reliable intrusion detection capabilities. Comparative analysis with other machine learning models confirms the effectiveness of our approach. Moreover, our study assesses the model's resilience to Gaussian noise, revealing its capacity to maintain accuracy in challenging conditions. We provide detailed performance metrics for various attack types, offering insights into the model's effectiveness across diverse threat scenarios.
Amin Moradbeiky
Abstract
Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator ...
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Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator models or methods can be improved by correct application of data filtering or feature weighting techniques. Numerous models have also been proposed based on machine learning methods for data modeling. This study proposes a new model consisted of data filtering and feature weighting techniques to improve the estimation accuracy in the final step of data modeling. The model proposed in this study consists of three layers. Tools and techniques in the first and second layers of the proposed model select the most effective features and weight features with the help of LSA (Lightning Search Algorithm). By combining LSA and an artificial neural network in the third layer of the model, an estimator model is developed from the first and second layers, significantly improving the final estimation accuracy. The upper layers of this model filter out and analyze data of lower layers. This arrangement significantly increased the accuracy of final estimation. Three datasets of real projects were used to evaluate the accuracy of proposed model, and the results were compared with those obtained from different methods. The results were compared based on performance criteria, indicating that the proposed model effectively improved the estimation accuracy.
M. Rahimi; A. A. Taheri; H. Mashayekhi
Abstract
Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. ...
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Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. In the proposed framework, a set of heterogeneous classifiers are stacked to produce the first-level outputs. Then these outputs are augmented using several combination functions to construct the inputs of the second-level classifier. We conduct a set of extensive experiments on 121 datasets and compare the proposed method with other established and state-of-the-art heterogeneous methods. The results demonstrate that the proposed scheme outperforms many heterogeneous ensembles, and is superior compared to singly tuned classifiers. The proposed method is also compared to several homogeneous ensembles and performs notably better. Our findings suggest that the improvements are even more significant on larger datasets.
H.3. Artificial Intelligence
A.R. Hatamlou; M. Deljavan
Abstract
Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase ...
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Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the previous studies on gold price forecast. In terms of time span, the collected data were divided into three groups of daily, monthly and annually. The conducted tests using new factors indicate accuracy improvement up to 2% in neural networks methods, 7/3% in time series method and 5/6% in linear regression method.
