A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

The increasing dependence on data analytics and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity. There exists a consensus among scholars and experts that the identification and mitigation of Multi-step attacks pose significant challenges due to the intricate nature of the diverse approaches utilized. This study aims to address the issue of imbalanced datasets within the domain of Multi-step attack detection. To achieve this objective, the research explores three distinct re-sampling strategies, namely over-sampling, under-sampling, and hybrid re-sampling techniques. The study offers a comprehensive assessment of several re-sampling techniques utilized in the detection of Multi-step attacks on deep learning (DL) models. The efficacy of the solution is evaluated using a Multi-step cyber attack dataset that emulates attacks across six attack classes. Furthermore, the performance of several re-sampling approaches with numerous traditional machine learning (ML) and deep learning (DL) models are compared, based on performance metrics such as accuracy, precision, recall, F-1 score, and G-mean. In contrast to preliminary studies, the research focuses on Multi-step attack detection. The results indicate that the combination of Convolutional Neural Networks (CNN) with Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) provides optimal results as compared to standalone ML/DL models. Moreover, the results also depict that SMOTEENN, a hybrid re-sampling technique, demonstrates superior effectiveness in enhancing detection performance across various models and evaluation metrics. The findings indicate the significance of appropriate re-sampling techniques to improve the efficacy of Multi-step attack detection on DL models

Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain’s immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient

Innovative Cybersecurity for Enhanced Data Protection: An Extended Bit-Plane Extraction and Chaotic Permutation-Diffusion Approach in Information Security

In the era of big data, protecting digital images from cyberattacks during network transmission is of utmost importance. While various image encryption algorithms have been developed, some remain vulnerable to specific cyber threats. This paper presents an enhanced version of the image encryption algorithm based on bit-plane extraction (BPCPD) to address its vulnerability to chosen-plaintext attacks. The proposed cryptographic system encompasses three primary phases. The initial phase involves bit-plane extraction from the plaintext image and the generation of random sequences and a random image using multiple chaotic maps, such as the chaotic Arnold map and the chaotic CAT map. The second phase is dedicated to permutation operations, which comprise three sub-phases: multi-layer permutation, multi-round permutation, and recursive permutation. In the third phase, diffusion is introduced to the permuted image through pixel substitution, coupled with XOR operations performed on the respective bit-planes of the random image. To gauge the efficiency of the proposed encryption scheme, a range of experimental analyses are conducted, including histogram analysis, contrast assessment, entropy measurement, correlation analysis, encryption quality assessment, and investigations into noise attacks and occlusion attacks. The results of these experimental analyses, in comparison to an existing encryption scheme, demonstrate that the proposed framework exceeds both BPCPD and other existing encryption schemes in various aspects of performance

Knowledge of Primary School Teachers Regarding Dental Trauma Management in Hail Region, Saudi Arabia

Objective:To evaluate the knowledge of elementary school teachers about the management of dental trauma. Material and Methods:An observational study, with the cross-sectional design, was conducted among primary school teachers in Hail, Saudi Arabia during January 2017. The questionnaire distributed among 400 primary school teachers from 18 different schools using convenient sampling. Data were gathered and analyzed using SPSS version 20. Results:378 (94.5%) respondents to the questionnaire. It was found that only 37.8% of the primary school teachers were able to distinguish between the primary and permanent teeth. Only 59.5% reported starting the management of a child with trauma immediately. Merely 38.4% believed that it is important to search for the missing tooth or the broken pieces, whereas 31% would re-implant the permanent tooth into the socket by themselves. Regarding the storage media, only 16.6% respond correctly. According to school teachers, the best way of learning the management of dental trauma at school is through videos (36.2%) and phone application (33.9%). Conclusion:School teachers lack knowledge regarding the management of dental trauma. We strongly recommend planning for dental trauma educational based on the teacher's perception after pilot testing its effectiveness