Today, financial technology is a part of consumer needs. Mobile payments are used to support daily transactions, so a study of factors that can support behavioral intention and mobile money use is conducted. This stud...
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Automatic sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. However, how to...
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This article presents a bendable substrate MnxCa(0.90−x)Ni0.10Fe2O4 based metamaterial absorber for RF energy harvesting from Wifi frequency of 5 GHz to the achievement of the Sustainable Development Goals. The bendab...
Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagn...
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In this paper, the influence of the human body communication (HBC) channel conditions changes on Inductance Shift Keying (ISK-CC-HBC) is analyzed. In particular in ISK-CC-HBC, information is mapped by impedance change...
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ISBN:
(数字)9798350386394
ISBN:
(纸本)9798350386400
In this paper, the influence of the human body communication (HBC) channel conditions changes on Inductance Shift Keying (ISK-CC-HBC) is analyzed. In particular in ISK-CC-HBC, information is mapped by impedance change at the transmitter side through switching inductance between signal and ground electrode. To achieve maximum Euclidean distance between the constellation points, and consequently a low bit error rate (BER), correct inductance value is crucial. However, the state of the HBC channel varies depending on the person on which the communication takes place. Therefore, in this paper, the performance of the ISK-CC-HBC system was analyzed to determine the optimal inductance and BER for five persons of different ages and genders.
A human photoplethysmogram is a biological signal widely used for heart rate estimation, and it has a great potential for a variety of physiological and mental health monitoring applications. Photoplethysmogram is a h...
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Binary obfuscation is a very broad set of techniques widely employed in the context of code protection from piracy. However, it is also used for malicious goals, e.g. virus writers often employ obfuscation in order to...
Binary obfuscation is a very broad set of techniques widely employed in the context of code protection from piracy. However, it is also used for malicious goals, e.g. virus writers often employ obfuscation in order to evade signature-based antivirus detection. Thus, the ability to detect if an executable has been obfuscated is of paramount importance, as it allows to thwart the execution of potentially malicious code. The task of detection, however, is not easy, since many different obfuscating transformations exist and the alteration of an original code is not always easily detectable. In this paper, we want to shed light on the blurry task of obfuscation detection. We will look at this task through the brand new lenses of explainable artificial intelligence (XAI), in order to finally sharpen the obscure landscape of obfuscated software. Thanks to XAI we will be able to identify the relevant features altered by the transformating obfuscation as well as the invariant ones, that can be used for obfuscation-resistant malware signatures. We show our findings thanks to an evaluation with a dataset of obfuscated and non-obfuscated binaries, explaining the important features that lead to the detection of obfuscating transformations.
The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may...
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An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technologic...
An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technological developments. Despite the proliferation of security precautions, criminals have learned to circumvent them. We employ a Contingently Generative Adversarial Network in our research to detect harmful online behaviour (CC-GAN). Our proposed system employs CC-GAN, which can identify novel threats, to carry out intrusion detection based on anomalies. An Internet-of-Things layer, a fog layer, as well as a cloud layer make up the whole of our proposed fog-based infrastructure. The IoT layer consists of many connected devices, and it is here that the CC-GAN receives the training data it needs to learn to recognise potential security breaches. A convolutional neural network & encoder are trained on the cloud layer, where additional computing takes place. Adding a fog layer might help decrease the delay. Finally, the attack detection score is evaluated. For this exercise, we use the network simulator version 2.6. (NS 3.26). The simulation result shows that the proposed system achieves a higher detection rate than the state-of-the-art systems.
In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within di...
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