URECA:Efficient Resource Location Middleware for Ubiquitous Environment

We describe an effective resource location framework for ubiquitous computing environments populated by a diverse set of networks, devices, services and computational entities. Our framework provides context adaptation with the aid of a middleware service to improve the quality of resource location. A resource location protocol suitable to each type of network locates resource effectively by means of dynamic reconfiguration to the current context. Our framework is also refined by support for interoperability between different types of resource location protocols occurring across a hybrid ubiquitous network. These characteristics also reduce the control overhead for resource location, saving resource, decreasing latency and permitting a considerable degree of scalability.

Resource Allocation for Physical Layer Security in Heterogeneous Network with Hidden Eavesdropper

The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Phishing detection method based on URL features

In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,etc.In general,these kinds of cyberattacks are made at users through phone calls,emails,or instant messages.The anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage content.In third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses.The tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification process.In this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)model.The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs.In addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance.Moreover,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of phishing.The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes.The performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

A Survey about Algorithms Utilized by Focused Web Crawler

Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter data analyzing or user querying. This paper demonstrates that the popular algorithms utilized at the process of focused web crawling, basically refer to webpage analyzing algorithms and crawling strategies （prioritize the uniform resource locator （URLs） in the queue）. Advantages and disadvantages of three crawling strategies are shown in the first experiment, which indicates that the best-first search with an appropriate heuristics is a smart choice for topic-oriented crawlingwhile the depth-first search is helpless in focused crawling. Besides, another experiment on comparison of improved ones （with a webpage analyzing algorithm added） is carried out to verify that crawling strategies alone are not quite efficient for focused crawling and in most cases their mutual efforts are taken into consideration. In light of the experiment results and recent researches, some points on the research tendency of focused crawler algorithms are suggested.

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.