A review of SolarWinds attack on Orion platform using persistent threat agents anf techniques for gaining unauthorized access

This paper of work examines the SolarWinds attack, designed on Orion Platform security incident. It analyses the persistent threats agents and potential technical attack techniques to gain unauthorized access. In 2020 SolarWinds attack indicates an initial breach disclosure on Orion Platform software by malware distribution on IT and government organizations such as Homeland Security, Microsoft and Intel associated with supply chains leaks consequences from small loopholes in security systems. Hackers increased the number of infected company and businesses networks during the supply-chain attack, hackers were capable to propagate the attack by using a VMware exploit. On the special way they started to target command injections, privilege escalations, and use after free platforms of VMware. In this way, they gained access to Virtual Machines and in the east way pivot other servers. This literature review aim to analyze the security gap regarding to SolarWinds incident on Orion Platform, the impact on industry and financial sectors involving the elements of incident response plan. Therefore, this research paper ensures specifications of proper solutions for possible defense security systems by analyzing a SolarWinds attack case study via system evaluation and monitoring. It concludes with necessary remediation actions on cyber hygiene countermeasures, common vulnerabilities and exposure analysis and solutions.Comment: 6 page

Representation Learning via Variational Bayesian Networks

We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is scarce. VBN provides better modeling for long-tail entities via two complementary mechanisms: First, VBN employs informative hierarchical priors that enable information propagation between entities sharing common ancestors. Additionally, VBN models explicit relations between entities that enforce complementary structure and consistency, guiding the learned representations towards a more meaningful arrangement in space. Second, VBN represents entities by densities (rather than vectors), hence modeling uncertainty that plays a complementary role in coping with data scarcity. Finally, we propose a scalable Variational Bayes optimization algorithm that enables fast approximate Bayesian inference. We evaluate the effectiveness of VBN on linguistic, recommendations, and medical inference tasks. Our findings show that VBN outperforms other existing methods across multiple datasets, and especially in the long-tail

Efficient Regeneration System for the Improvement of Kinnow mandarin (Citrus reticulata Blanco)

Kinnow mandarin (Citrus reticulata Blanco.) is a highly adaptable variety among citrus cultivars. An efficient system for in vitro regeneration by organogenesis starting from seed of (C. reticulata Blanco) was developed. Seeds were treated by Murashige and Skoog (MS) media supplemented with 2, 4-Dichlorophenoxyacetic acid (2, 4-D) to initiate callus induction. The best result (96%) were obtained when seeds were treated with MS basal media + 2,4-D (16.0) μM. The regeneration system tested allowed the attainment of highest shoots (90 %) with BA 13.0 μM. An average of 7.8 well-differentiated shoots per explant was obtained. Highest rooting (85%) was achieved in culture medium with 10.0 μM IBA. The well-developed plantlets were transferred to potting mixture. Of the rooted plant, 95% adapted well to soil conditions. Keywords: C. reticulata Blanco, In vitro, Callus induction, Shoot formation, Explant, Rooting. Abbreviations: μM = Micromolar, BA = Benzyl adenine, IBA = Indole-3-butyric acid, TSS = Total soluble solids, NAA

IoMT-Blockchain based Secured Remote Patient Monitoring Framework for Neuro-Stimulation Device

Biomedical Engineering's Internet of Medical Things (IoMT) is helping to improve the accuracy, dependability, and productivity of electronic equipment in the healthcare business. Real-time sensory data from patients may be delivered and subsequently analyzed through rapid development of wearable IoMT devices, such as neuro-stimulation devices with a range of functions. Data from the Internet of Things is gathered, analyzed, and stored in a single location. However, single-point failure, data manipulation, privacy difficulties, and other challenges might arise as a result of centralization. Due to its decentralized nature, blockchain (BC) can alleviate these issues. The viability of establishing a non-invasive remote neurostimulation system employing IoMT-based transcranial Direct Current Stimulation is investigated in this work (tDCS). A hardware-based prototype tDCS device has been developed that can be operated over the internet using an android application. Our suggested framework addresses the problems of IoMTBC-based systems, meets the criteria of real-time remote patient monitoring systems, and incorporates literature best practices in the relevant fields.Comment: 8 Figures and 2 Table

Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques

The rise of fake news has made the need for effective detection methods, including in languages other than English, increasingly important. The study aims to address the challenges of Bangla which is considered a less important language. To this end, a complete dataset containing about 50,000 news items is proposed. Several deep learning models have been tested on this dataset, including the bidirectional gated recurrent unit (GRU), the long short-term memory (LSTM), the 1D convolutional neural network (CNN), and hybrid architectures. For this research, we assessed the efficacy of the model utilizing a range of useful measures, including recall, precision, F1 score, and accuracy. This was done by employing a big application. We carry out comprehensive trials to show the effectiveness of these models in identifying bogus news in Bangla, with the Bidirectional GRU model having a stunning accuracy of 99.16%. Our analysis highlights the importance of dataset balance and the need for continual improvement efforts to a substantial degree. This study makes a major contribution to the creation of Bangla fake news detecting systems with limited resources, thereby setting the stage for future improvements in the detection process.Comment: Accepted for publication in the 7th International Conference on Networking, Intelligent Systems & Security. The conference Proceedings will be published by ACM International Conference Proceeding Series (ICPS) ISBN N{\deg}: 979-8-4007-0019-4. 8 pages, 11 figure

Eco-friendly management of seed borne fungi for sustainable crop production

A total of seven seed-borne fungi were detected from forty rice (Oryzae sativa) seed samples (cv. BR11 and BRRI dhan28) collected from two upazilas (Narshingdi Sadar and Shibpur) of Narshingdi district in Bangladesh. The identified species were Bipolaris oryzae, Alternaria padwickii, Sarocladium oryzae, Curvularia lunata, Aspergillus niger and Fusarium spp. The seed samples were composed of apparently healthy seed, spotted seed, discoloured seed, deformed seed, varietal mixture and chaffy grain. Prevalence of fungi and seed germination varied significantly with respect to variety and seed source. Seeds of rice variety BRRI dhan28 carried the lower infection of all the seed-borne fungi than the variety BR11. Seeds collected from Shibpur had higher seed-borne infection. An attempt has been made to control the seed-borne fungi by different plant extracts and chemicals. Garlic extract (1:1) dilution found best which successfully reduced seed-borne infection (80.3%) and also increased seed germination by 10.69% over control. Neem, allamanda and bishkatali extracts also increased seed germination 8.99%, 7.10% and 5.84%, respectively. Seed treating fungicides viz. Vitavax-200, Bavistin 50 WP and Captan were also tested to control seed-borne fungi. Seed treatment with Vitavax-200 @ 0.3% of seed weight eliminated all the seed-borne fungi and increased seed germination by 25.70% over control. Another chemical Bavistin also reduced seed-borne infection (88%) successfully and increased seed germination by 24.67% over control. Considering the high cost and deleterious effect of chemicals on environment, plant extracts may be recommended for controlling seed-borne fungal pathogens of rice as they are cheap, safe and eco-friendly