Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the de...
Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the development of such systems has been impeded by the complexity of legal restrictions and physical constraints. While airspaces are often tightly shaped by various legal requirements, Unmanned Aerial Vehicles (UAV) must simultaneously consider, among others, energy demands, signal quality, and noise pollution. In this work, we address this challenge by presenting a novel architecture that integrates methods of Probabilistic Mission Design (ProMis) [1, 2] and Many-Objective Optimization [3] for UAV routing. Hereby, our framework facilitates compliance with legal requirements under uncertainty while producing effective paths that minimize various physical costs a UAV needs to consider when traversing human-inhabited spaces. To this end, we combine hybrid probabilistic first-order logic for spatial reasoning with mixed deterministic-stochastic route optimization, incorporating physical objectives such as energy consumption and radio interference with a logical, probabilistic model of legal requirements. We demonstrate the versatility and advantages of our system in a large-scale empirical evaluation over real-world, crowd-sourced data from a map extract from the city of Paris, France, showing how a network of effective and compliant paths can be formed.
The development of satirical and fake news on digital platforms has source of major concern about the spread of misinformation and its control on society. As part of the Arabic language, fake news detection (FND) show...
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The development of satirical and fake news on digital platforms has source of major concern about the spread of misinformation and its control on society. As part of the Arabic language, fake news detection (FND) shows particular problems because of language difficulties and the scarcity of labeled data. FND on Arabic corpus utilizing deep learning (DL) contains leveraging advanced neural network (NN) techniques and methods to automatically recognize and classify deceptive data in the Arabic language text. This procedure is vital in combating the spread of disinformation and misinformation, promoting media literacy, and make sure the credibility of data sources for the Arabic-speaking community. Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are common selections for FND because of their capability for learning hierarchical features and model sequential data from the text. In this view, this study develops a Mountain Gazelle Optimizer with Deep Learning-Driven Fake News Classification on Arabic Corpus (MGODL-FNCAC) technique. The presented MGODL-FNCAC approach aims to increase the performance of the fake news classification on the Arabic corpus. Primarily, the MGODL-FNCAC technique involves different stages of pre-processing to make the input data compatible for classification. For fake news detection, the MGODL-FNCAC technique applies the deep belief network (DBN) model. At last, the MGO approach can be used for the better hyperparameter tuning of the DBN approach, which supports in enhancing the overall training process and detection rate. The simulation outcomes of the MGODL-FNCAC technique can be examined on Arabic corpus data. The extensive outcomes exhibit the importance of the MGODL-FNCAC system over other methodologies with maximum accuracy of 97.68% and 95.14% on Covid19Fakes and Satirical dataset, respectively.
Despite significant effort put into research and development of defense mechanisms, new malware is continuously developed rapidly, making it still one of the major threats on the Internet. For malware to be successful...
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Despite significant effort put into research and development of defense mechanisms, new malware is continuously developed rapidly, making it still one of the major threats on the Internet. For malware to be successful, it is in the developer’s best interest to evade detection as long as possible. One method in achieving this is using Code Injection, where malicious code is injected into another benign process, making it do something it was not intended to *** detection and characterization of Code Injection is difficult. Many injection techniques depend solely on system calls that in isolation look benign and can easily be confused with other background system activity. There is therefore a need for models that can consider the context in which a single system event resides, such that relevant activity can be distinguished *** previous work, we conducted the first systematic study on code injection to gain more insights into the different techniques available to malware developers on the Windows platform. This paper extends this work by introducing and formalizing Behavior Nets: A novel, reusable, context-aware modeling language that expresses malicious software behavior in observable events and their general interdependence. This allows for matching on system calls, even if those system calls are typically used in a benign context. We evaluate Behavior Nets and experimentally confirm that introducing event context into behavioral signatures yields better results in characterizing malicious behavior than state-of-the-art. We conclude with valuable insights on how future malware research based on dynamic analysis should be conducted.
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