This paper presents a novel Collaborative Cyberattack Detection (CCD) system aimed at enhancing the security of blockchain-based data-sharing networks by addressing the complex challenges associated with noise addition in federated learning models. Leveraging the theoretical principles of differential privacy, our approach strategically integrates noise into trained sub-models before reconstructing the global model through transmission. We systematically explore the effects of various noise types, i.e., Gaussian, Laplace, and Moment Accountant, on key performance metrics, including attack detection accuracy, deep learning model convergence time, and the overall runtime of global model generation. Our findings reveal the intricate trade-offs between ensuring data privacy and maintaining system performance, offering valuable insights into optimizing these parameters for diverse CCD environments. Through extensive simulations, we provide actionable recommendations for achieving an optimal balance between data protection and system efficiency, contributing to the advancement of secure and reliable blockchain networks.

Ensuring meal safety in hospital settings is of paramount importance, given the specialized dietary needs and the vulnerability of patients. This paper addresses the critical need for accurate food component identification in hospital food service systems to prevent adverse reactions due to allergens or dietary non-compliance. We propose a novel approach to enhance automated meal recognition systems, focusing on the recognition of allergenic food components with priority. We integrate two novel methods within Convolutional Neural Networks: Adaptive Priority Dual Loss Function Strategy and False Negative Weighting with Hyper-parameter Calibration. The results show an improvement in the average recall rate for priority food categories, thereby enhancing meal safety. We compared our method with traditional techniques using benchmark neural network architectures. The findings reveal that our approach significantly improves the recognition rates of priority classes, which is crucial in a hospital setting where accurate food identification can have profound health implications. This research contributes to the field by ofering an innovative solution to a pressing food safety challenge, paving the way for safer and more efficient food service systems in hospitals.

Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link between principles of persuasion and social engineering attacks, particularly in phishing email attacks, is an important topic in cyber security as they are the common and effective method used by cybercriminals to obtain sensitive information or access computer systems. This survey paper concluded that spear phishing, a targeted form of phishing, has been found to be specifically effective as attackers can tailor their messages to the specific characteristics, interests, and vulnerabilities of their targets. Understanding the uses of the principles of persuasion in spear phishing is key to the effective defence against it and eventually its elimination. This survey paper systematically summarizes and presents the current state of the art in understanding the use of principles of persuasion in phishing. Through a systematic review of the existing literature, this survey paper identifies a significant gap in the understanding of the impact of principles of persuasion as a social engineering strategy in phishing attacks and highlights the need for further research in this area.

We investigate the problem of extrapolation of band-limited signals on the 2-sphere in the presence of noise. Specifically, given incomplete or spatially limited measurements subject to noise, find the unique extrapolation to the complete 2-sphere. We present an analytic solution to the extrapolation problem based on the expansion of a signal in Slepian basis corresponding to an orthogonal set of eigenfunctions of an associated energy concentration problem. An alternative equivalent iterative algorithm is also developed for practical implementation and guidelines are proposed to choose the parameters of the iterative algorithm. The capability of the proposed extrapolation is compared and demonstrated with the help of an illustration example.

Smallcell is emerging as a cost-effective solution for satisfying the huge demands of mobile data. It can be deployed at any place where mobile traffic is required without the need for cell planning. However, coexistence of many uncontrolled smallcells using the same licensed frequency band can result in serious interference problems. In order to utilize smallcell efficiently, it is highly desirable that the smallcell can self-organize the network and mitigate interference automatically. In this paper, we propose a dynamic fractional frequency reuse (FFR) method for reducing the intercell interference automatically and improving the spectral efficiency. Key features of the proposed method are sub-band optimization with a central manner and sub-band size adjustment with a distributed manner. The proposed method has a low complexity and can be implemented as a feature of a self-organizing network (SON) in smallcell. Simulation results verify the effectiveness of the proposed method.

In this paper, we investigate the performance of compressive sampling (CS) for ECG compression in telecardiology, when the signal acquisition is noisy and unavoidable body movements lead to varying heartbeat rate and sparsity of the signal. We show analytically that CS recovery noise does not scale linearly with the input noise. Hence, it is not easy to reduce the adverse impact of noise in CS. Additionally, any variation in the heartbeat rate changes the sparsity and can adversely affect compression. We compare the performance of CS with thresholding discrete wavelet transform (TH-DWT), which is the best technique for real-time ECG compression. We show that CS is quite sensitive to sparsity and compression ratio, while the reconstruction quality of TH-DWT is quite stable. Our results suggest that while CS is an attractive option for telecardiology due to its encoder simplicity, caution should be exercised in applying it for ECG signal compression.