Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

Analysis of Network Security Protection Based on Digital Economy

Since the first half of 2020, the COVID-19 epidemic has continued to spread across the country. Based on this background, with the continuous promotion of a new round of technological innovation and industrial transformation, and the combined impact of the epidemic factors, the digital economy has become a new pillar of the steady development of China's macro economy. Emerging industries have provided good opportunities for the digital economy. Cyber security has risen to the height of national sovereignty, which is the direct embodiment of national competitiveness and the foundation for the healthy development of the digital economy. However, with the concentration of massive data and the progress of information technology, the data is easily and conveniently used, personal privacy security, corporate business secrets and even national security suffered serious damage, and network security protection has also become the bottleneck of the digital economy to a new level. It is particularly important to strengthen the network security governance capacity, improve the network security laws and regulations, and implement the hierarchical protection system. © 2023 SPIE.

Study of Zero Trust Architecture for Applications and Network Security

As a result of globalization, the COVID-19 pandemic and the migration of data to the cloud, the traditional security measures where an organization relies on a security perimeter and firewalls do not work. There is a shift to a concept whereby resources are not being trusted, and a zero-trust architecture (ZTA) based on a zero-trust principle is needed. Adapting zero trust principles to networks ensures that a single insecure Application Protocol Interface (API) does not become the weakest link comprising of Critical Data, Assets, Application and Services (DAAS). The purpose of this paper is to review the use of zero trust in the security of a network architecture instead of a traditional perimeter. Different software solutions for implementing secure access to applications and services for remote users using zero trust network access (ZTNA) is also summarized. A summary of the author's research on the qualitative study of 'Insecure Application Programming Interface in Zero Trust Networks' is also discussed. The study showed that there is an increased usage of zero trust in securing networks and protecting organizations from malicious cyber-attacks. The research also indicates that APIs are insecure in zero trust environments and most organization are not aware of their presence. © 2022 IEEE.

Network Security

Events that attract worldwide attention, such as the Olympic and Paralympic Games and international exhibitions, have become easy targets for cyber attacks, and it is no longer rare to hear of reports of damage from such attacks. The Olympic and Paralympic Games Tokyo 2020 was held in 2021 after a oneyear delay due to the novel coronavirus (COVID-19), and NTT, as a Gold Partner (Telecommunications Services), had the responsibility of managing the network infrastructure supporting the Tokyo 2020 Games, thus dealing with the threat of cyber attacks. This article describes how NTT-CERT (NTT Computer Security Incident Response and Readiness Coordination Team) of NTT Social Informatics Laboratories faced cyber attacks as the representative computer security incident response team of the NTT Group. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Genealogías discursivas sobre la pandemia de covid-19. Reflexiones sobre un mundo postpandemia

El objetivo de este trabajo es realizar una reflexión crítica sobre la idea de un mundo postpandemia, a partir de la deconstrucción de genealogías discursivas sobre la pandemia de la covid-19. Se utilizó como punto de partida la idea de Michel Foucault de historia del presente, en términos de la deconstrucción de los relatos que dan cuenta tanto lo novedoso, en esta caso de la pandemia de la covid-19, como de las inercias discursivas del pasado que perviven en el presente. Se deconstruyeron cinco genealogía discursivas sobre pandemia. En primer lugar, se abordó el problema de la propia definición de pandemia, a partir de la crisis de la gripe A, gripe porcina o H1N1. En segundo lugar, se reflexionó sobre el impacto que tuvo la gestión de la crisis del H1N1 en las representaciones y prácticas discursivas de la pandemia de covid-19. En tercer lugar, se discutieron los marcos interpretativos y epistemológicos del gobierno de las crisis pandémicas en las sociedades del Norte Global. Por su interés discursivo se analizaron, por una parte, la construcción discursiva del gobierno de las epidemias, considerando las ideas de confinamiento y vacunación y, por otra parte, el gobierno de las infraestructuras vitales, como origen de la utilización metáfora de la guerra para el gobierno de riesgos y amenazas. En cuarto lugar, se reflexionará sobre el discurso de la (in)seguridad y sus dificultades pragmáticas en el gobierno de este tipo de crisis. Se utilizará la idea de la disonancia pragmática para dar cuenta de los problemas del discurso de la seguridad. En quito lugar, se criticó el discurso de la salud global y sus implicaciones en esta crisis, tomando como referencia tres relatos o narrativas: el relato sobre la seguridad en salud global, el relato sobre el mercado de productos sensibles, como los equipos de protección personal (mascarillas) y el relato sobre la producción de vacunas. A partir de la deconstrucción de estas genealogías discursivas plantearemos, a manera de conclusión, la idea de la crónica de un fracaso global, en relación con el gobierno de la crisis de la covid-19, agravada por la irrupción de una nueva crisis, la guerra de Ucrania. Proponemos finalmente una reconstrucción del discurso virus-céntrico, a partir de la idea de una espacialidad territorial y simbólicamente constituida organizada, configurada y materializada por múltiples tecnologías de significación, vinculadas bajo la figura de una red de actores propuesta por Bruno Latour.Alternate abstract:The objective of this paper is to carry out a critical reflection on the idea of a post-pandemic world, based on the deconstruction of discursive genealogies on the Covid-19 pandemic. First of all, attention is drawn to the fact that the countries of the Global North, apparently better prepared to face this crisis, have experienced a severe impact, particularly in the so-called first wave. This fact becomes even more relevant if we consider that the different indices that predicted a better capacity of these countries to face this type of crisis were initially distorted by the cases of Italy and Spain and, later;by other Global North countries such as the United States.To carry out these discursive genealogies, Michel Foucault's idea of the history of the present was used as a starting point, in terms of the deconstruction of the stories that account for both the novelty, in this case of the Covid-19 pandemic, and the discursive inertias of the past that survive in the discourses on the representations and the government of this type of phenomena. Five discursive genealogies on the pandemic were deconstructed. In the first place, the problem of the definition of a pandemic was addressed, based on the crisis of influenza A, swine flu or H1N1 and the criticism made by the Council of Europe in 2010 of the declaration of a pandemic by the World Health Organization (WHO). Secondly, we reflected on the impact that the management of the H1N1 crisis had on the representations and discursive practices of the Covid-19 pandem c. The dissonance between the low impact of this crisis and the high spending by the countries of the Global North marked the initial management of the Covid-19 crisis, particularly in terms of reducing the perception of insecurity and the overvaluation of capacities. It became evident how the story of the impact of the crisis in Italy and Spain deeply marked the representations that were initially held about this crisis. Third, the interpretive and epistemological frameworks of the governance of pandemic crises in societies of the Global North were discussed. Due to its discursive interest, we analyzed, on the one hand, the discursive construction of the government of epidemics, considering the ideas of confinement and vaccination and, on the other hand, the government of vital infrastructures, such as the origin of the use of the metaphor of war to the governance of risks and threats in these societies. Fourth, we reflected on the discourse of (in)security and its pragmatic difficulties in governing this type of crisis.The idea of pragmatic dissonance is used to account for the problems of the security discourse. In fifth place, the global health discourse and its implications in this crisis were criticized.The survival of colonial and neocolonial narratives in global health, the weakening of the WHO due to the incorporation of interests of private actors such as multilateral agencies, banks linked to development discourses, multinational corporations and philanthropic companies were highlighted. The relevance of the biotechnological and biomedical discourse was also evident, based on the idea of the magic bullet. The critique of the global health discourse had three stories or narratives as its central reference: the story about global health security, the story about the market for sensitive products, such as personal protective equipment (masks), and the story about the production of vaccines. The problematization of the discursive genealogies related to the Covid-19 crisis made it possible to highlight the great difficulties we currently have in building a discourse that gives intelligibility to this type of crisis, especially from a global perspective. This difficulty allowed us to propose, by way of conclusion, the idea of the chronicle of a global failure (everything that could go wrong finally did go wrong), in relation to the government of the Covid-19 crisis, from the idea of the infelicity of the speech act proposed by Austin. This chronicle has been aggravated by the emergence of a new crisis, the war in Ukraine. We also propose the irruption of a disaster capitalism whose discursive performativity in relation to the pandemic was felicity, which is to say they achieved what they wanted: to significantly increase their profits. Finally, we propose as an alternative a reconstruction of the virus-centric discourse, which has permeated the discourse of experts, proposing the idea of a discourse based on territorial spatiality and symbolically constituted, organized, configured and materialized by multiple technologies of meaning, linked under the figure of a network of actors proposed by Bruno Latour. The virus is one more actor in this human and non-human network. What the virus does is expose the power relationships (knowledge/power) that account for the way this network is configured. More than the virus, it is these power relations that account for the vulnerabilities we experience due to the Covid-19 crisis. Everything seems to indicate that the new discursive practices in relation to this type of crisis should point in this direction.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

AMDetector: Detecting Large-Scale and Novel Android Malware Traffic with Meta-learning

In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

GBLNet: Detecting Intrusion Traffic with Multi-granularity BiLSTM

Detecting and intercepting malicious requests are some of the most widely used ways against attacks in the network security, especially in the severe COVID-19 environment. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection method is required. In this paper, we formulate the problem of detecting malicious requests as a temporal sequence classification problem, and propose a novel deep learning model namely GBLNet, girdling bidirectional LSTM with multi-granularity CNNs. By connecting the shadow and deep feature maps of the convolutional layers, the malicious feature extracting ability is improved on more detailed functionality. Experimental results on HTTP dataset CSIC 2010 demonstrate that GBLNet can efficiently detect intrusion traffic with superior accuracy and evaluating speed, compared with the state-of-the-arts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design an Intrusion Detection System, Multiple Honeypot and Packet Analyzer Using Raspberry Pi 4 for Home Network

Increased crime and cyber-attacks make network security an essential prerequisite for organizations. However, organizations cannot guarantee this because the COVID-19 pandemic has forced organizations to suspend activities in the office and give employees the option to work from home. As a result, employees must always be connected to the home network to work. It can attract hackers to take advantage of the situation by launching various attacks. Therefore home network security must be updated, minimize vulnerabilities, and apply additional security. The number of IoT devices that can connect to the home network is also considered to increase security because the main entry point for hacking IoT is through the network. Raspberry Pi 4 can be used as a low-cost, power-efficient, and practical solution for home network security, including IDS Suricata, multiple honeypots (Cowrie & Dionaea), and Tshark packet analyzer. There are six types of attack simulations: port scanning, brute force, TCP flood attacks, smurf attacks, UDP flood attacks, and exploits on services/ports. Measurement of device performance is also carried out when running the system. Log data from the four sensors will be visualized with the ELK stack, making it easier to analyze attacks. ©2021 IEEE

Research on Intrusion Detection Based on Neural Network Optimized by Genetic Algorithm

With the development of 5G and the emergence of the COVID-19 epidemic, network traffic has surged, and network security has once again become a key concern. Intrusion detection system is an important means to protect network security. It can find abnormal conditions in the early stage of cyber attack. Intrusion detection is also a kind of abnormal detection in a broad sense. To improve the performance of the intrusion detection system, a cyber-attack detection method combining Borderline SMOTE and improved BP neural network (Back-Propagation neural network) is proposed. It mainly uses one-hot encoding, Borderline SMOTE data oversampling and other technologies to preprocess the data, and uses the BP neural network improved by genetic algorithm to predict cyber attacks. Finally, the model is compared with other traditional machine learning models through the core indicator recall and auxiliary indicators precision, roc curve, etc. The results show that the hybrid detection model proposed in this study has higher recall and lower running time, and performs better in intrusion detection. © 2022 IEEE.