In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

The development of the Internet has led to the complexity of network encrypted traffic. Identifying the specific classes of network encryption traffic is an important part of maintaining information security. The traditional traffic classification based on machine learning largely requires expert experience. As an end-to-end model, deep neural networks can minimize human intervention. This paper proposes the CLD-Net model, which can effectively distinguish network encrypted traffic. By segmenting and recombining the packet payload of the raw flow, it can automatically extract the features related to the packet payload, and by changing the expression of the packet interval, it integrates the packet interval information into the model. We use the ability of Convolutional Neural Network (CNN) to distinguish image classes, learn and classify the grayscale images that the raw flow has been preprocessed into, and then use the effectiveness of Long Short-Term Memory (LSTM) network on time series data to further enhance the model’s ability to classify. Finally, through feature reduction, the high-dimensional features learned by the neural network are converted into 8 dimensions to distinguish 8 different classes of network encrypted traffic. In order to verify the effectiveness of the CLD-Net model, we use the ISCX public dataset to conduct experiments. The results show that our proposed model can distinguish whether the unknown network traffic uses Virtual Private Network (VPN) with an accuracy of 98% and can accurately identify the specific traffic (chats, audio, or file) of Facebook and Skype applications with an accuracy of 92.89%.

/pdf/cld-net-a-network-combining-cnn-and-lstm-for-internet-126ivoc888.pdf

CLD-Net: A Network Combining CNN and LSTM for Internet Encrypted Traffic Classification

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

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Smartphone App Usage Analysis: Datasets, Methods, and Applications

The proliferation of mobile devices over recent years has led to a dramatic increase in mobile traffic. Demand for enabling accurate mobile app identification is coming as it is an essential step to improve a multitude of network services: accounting, security monitoring, traffic forecasting, and quality-of-service. However, traditional traffic classification techniques do not work well for mobile traffic. Besides, multiple machine learning solutions developed in this field are severely restricted by their handcrafted features as well as unreliable datasets. In this paper, we propose a framework for real network traffic collection and labeling in a scalable way. A dedicated Android traffic capture tool is developed to build datasets with perfect ground truth. Using our established dataset, we make an empirical exploration on deep learning methods for the task of mobile app identification, which can automate the feature engineering process in an end-to-end fashion. We introduce three of the most representative deep learning models and design and evaluate our dedicated classifiers, namely, a SDAE, a 1D CNN, and a bidirectional LSTM network, respectively. In comparison with two other baseline solutions, our CNN and RNN models with raw traffic inputs are capable of achieving state-of-the-art results regardless of TLS encryption. Specifically, the 1D CNN classifier obtains the best performance with an accuracy of 91.8% and macroaverage F-measure of 90.1%. To further understand the trained model, sample-specific interpretations are performed, showing how it can automatically learn important and advanced features from the uppermost bytes of an app’s raw flows.

/pdf/real-network-traffic-collection-and-deep-learning-for-mobile-8lj3dj28su.pdf

Real Network Traffic Collection and Deep Learning for Mobile App Identification

The proliferation of handheld devices has led to an explosive growth of mobile traffic volumes on the Internet. Identifying mobile apps from network traffic has become a crucial task for mobile network management and security. Traditionally, the design of accurate identifiers relies on the deep packet inspection (DPI) techniques. However, such approaches have become less effective with the raising adoption of encrypted protocols in mobile applications (mostly TLS). To address the problem, various machine learning methods have been studied and used. Most of them use linear classifiers on top of hand-engineered features, which are unreliable due to the complexity of mobile traffic. In this article we propose App-Net, an end-to-end hybrid neural network for mobile app identification from encrypted TLS traffic. App-Net is designed by combining RNN and CNN in a parallel way and can automatically learn effective features from raw TLS flows. With coordinated fusion and optimized training, the hybrid and multimodal architecture is able to characterize both flow sequence patterns and app signatures to learn a joint flow-app embedding. We evaluate App-Net on a real-world dataset covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

Automatic Mobile App Identification From Encrypted Traffic With Hybrid Neural Networks

In this paper, we propose a low-complexity group alternate iterative list (GAIL) detection algorithm for MIMO systems. By utilizing the recursive interference suppression and successive interference cancellation techniques, the symbol vector can be partitioned into many subgroups. Subsequently, symbols in each subgroup are detected in terms of the K-best detector. The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols for the detection result. We provide the performance-complexity tradeoff based on different feasible parameter settings. The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity. In addition, the running speed of the GAIL algorithm can be dramatically increased using parallel processing in real-time communication systems.

Low-Complexity Group Alternate Iterative List Detection for MIMO Systems

In this paper, we investigate a two-way relay network consisting of two sources, multiple cooperative relays and an eavesdropper. To enhance secure communications, a new relay chatting based on transmission scheme is proposed. Specifically, the proposed scheme selects a best relay that maximize the sum mutual information among the sources to forward the sources’ signals using an amplify-and-forward protocol, and the remaining relays transmit interference signals to confuse the eavesdropper via distributed beam forming. It can be found that the proposed scheme with relay chatting does not require the knowledge of the eavesdropper’s channel, and outperforms the joint relay and jammer selection scheme, which introduces the interference into the sources. Numerical results show that the secrecy outage probability of the proposed scheme converges to zero as the transmit power increases.

/pdf/secure-communications-for-two-way-relay-networks-via-relay-3tuqww4v0z.pdf

Xin Wang

Papers

Real Network Traffic Collection and Deep Learning for Mobile App Identification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

Automatic Mobile App Identification From Encrypted Traffic With Hybrid Neural Networks

Low-Complexity Group Alternate Iterative List Detection for MIMO Systems

Secure Communications for Two-Way Relay Networks Via Relay Chatting