25 research outputs found
Models for the Generation of Heterogeneous Complex Networks
No full textComplex networks are composed of a large number of interacting nodes. Examples of complex networks include the topology of the Internet, connections between websites or web pages in the World Wide Web (WWW), and connections between participants in social networks.Due to their ubiquity, modeling complex networks is importantfor answering many research questions that cannot be answered without a mathematical model. For example, mathematical models of complex networks can be used to find the most vulnerable nodes to protect during a virus attack in theInternet, to predict connections between websites in the WWW, or to find members of different communities insocial networks. Researchers have analyzed complex networksand concluded that they are distinguished from other networks by four specific statistical properties. These four statistical properties are commonly known in this field as: (i) thesmall world effect,(ii) high average clustering coefficient, (iii) scale-free power law degree distribution, and (iv) emergence of community structure. These four statistical properties are further described later in this dissertation.
Mostmodels used to generate complex networks attempt to produce networks with these statistical properties. Additionally, most of these network models generate homogeneous complex networks where all the networknodes are considered to have the same properties. Homogenous complex networks neglect the heterogeneous nature ofthe nodes in many complexnetworks. Moreover, somemodels proposed for generating heterogeneous complexnetworks are not general as they make specific assumptions about the properties of the network.Including heterogeneity in the connection algorithm of a modelwould makeitmore suitable for generating the subset of complex networks that exhibit selective linking.Additionally, all modelsproposed, to date, for generating heterogeneous complex networks do not preserve all four of the statistical properties of complexnetworks stated above. Thus, formulation of a model for the generation of general heterogeneous complex networkswith characteristics that resemble as much as possible the statistical properties common to the real-world networks that have received attention from the research community is still an open research question.
In this work, we propose two new types of models to generate heterogeneous complex networks. First, we introduce the Integrated Attribute Similarity Model (IASM). IASM uses preferential attachment(PA) to connect nodes based on a similarity measure for node attributes combined with a node's structural popularity measure. IASM integrates the attribute similarity measure and a structural popularity measure in the computation of the connection function used to determine connectionsbetween each arriving (newly created) node and the existing(previously created or old) network nodes. IASM is also the first model known to assign an attribute vector having more than one element to each node, thus allowing different attributes per node in the generated complex network. Networks generated using IASM have a power law degree distribution and preserve the small world phenomenon. IASM models are enhanced to increase their clustering coefficient using a triad formation step (TFS). In a TFS, a node connects to the neighbor of the node to which it was previously connected through preferential attachment, thus forming a triad. The TFS increases the number of triads that are formed in the generated network which increases the network's average clustering coefficient.
We also introduce a second novel model,the Settling Node Adaptive Model (SNAM). SNAM reflects the heterogeneous nature of connectionstandard requirements for nodes. The connectionstandard requirements for a noderefers to the values of attribute similarity and/or structural popularityof old node ythat node new xwould find acceptable in order to connect to node y.SNAM is novel in that such a node connection criterion is not included in any previous model for the generation of complex networks. SNAM is shown to be successful in preserving the power law degree distribution, the small world phenomenon, and the high clustering coefficient of complex networks.
Next,we implement a modification to the IASM and SNAM models that results in the emergence of community structure.Nodes are classified into classes according to their attribute values. The connection algorithm is modified to include the class similarity values between network nodes. This community structure model preservesthe PL degree distribution, small world property, and does not affect average clustering coefficient values expected from both IASM and SNAM. Additionally, the model exhibits the presence of community structure having most of the connections made between nodes belonging to the same class with only a small percent of the connections made between nodes of different classes.
We perform a mathematical analysis of IASM and SNAM to study the degree distribution for networks generated by both models. This mathematical analysis shows that networks generated by both models have a power law degree distribution.
Finally, we completed a case study to illustrate the potential value of our research on the modeling of heterogeneous complex networks. This case study was performed on a Facebook dataset. The case study shows that SNAM, with some modifications to the connection algorithm, is capable of generating a network with almost the same characteristics as found for the original dataset. The case study providesinsight on how the flexibility of SNAM's connection algorithm can be an advantagethat makes SNAM capable of generating networks with different statistical properties.
Ideas for future research areas includestudyingthe effect of using eigenvector centrality, instead of degree centrality, on the emergence of community structure in IASM; usingthe nodeindex as an indication for its order of arrival to the network and distributing added connections fairly among networknodes along the life of the generated network; experimenting with the nature of attributesto generatea more comprehensive model; and usingtime sensitive attributes in the models, where the attribute can change its value with time,Ph. D
Evaluate the incidence, topography, management, and outcomes in patients with polytrauma in the Suez Canal and Sinai areas
No full textIntroduction: The global prevalence of trauma-related mortality ranges from 2% to 32%; however, In Egypt, it reaches 8%. Trauma chiefly affects people in the productive age group; seriously ill patients with multiple injuries present with various levels of polytrauma. Application of incorrect triage systems and improperly trained trauma teams increase mortality and morbidity rates in non-dedicated institutions; however, these rates can decrease with appropriate infrastructure. This study aimed to improve the quality of care for patients with polytrauma through improved knowledge of the different severity levels of polytrauma and defined databases, using a suitable triage trauma system, well-trained trauma team, and appropriate infrastructure. Methods: This observational cross-sectional study was conducted at the emergency department (ED), over a study period of 7 months, from August 10, 2019, to March 09, 2020. This study included 458 patients with polytrauma who had met the inclusion and exclusion criteria and attended the ED of Suez Canal University Hospital. Results: The incidence of trauma among all emergency cases in the ED was 5.3%. However, most multiple injuries are mild, accounting for 44.4%, while 27.3% of the cases had life-threatening injuries. Moreover, 41.9% of the patients were managed non-operatively, whereas 58.1% of the patients required surgical interventions. Concerning the outcome, 56% and 6.9% of patients with and without life-threatening injuries respectively, died. Conclusion: Facilities of the highest quality should be available for patients with polytrauma, especially those with life-threatening injuries. In addition, training emergency medical service staff for trauma triage is essential, and at least one tertiary hospital is required in every major city in the Suez Canal and Sinai areas to decrease trauma-related mortality
Development of immunization trials against Eimeria spp.
No full textAbstractCoccidiosis is a major intestinal disease affecting economically valuable livestock animals such as chickens and turkeys. Economic losses are associated with decreased productivity in afflicted animals. The different Eimeria spp. are the main etiologic agents for that virulent disease. The usefulness of prophylactic and therapeutic anticoccidial compounds has decreased in recent years due to the emergence of drug resistance in Eimeria, together with their possible toxic effect to the human consumers. Despite that, biosecurity and disinfection measures are the cornerstone to control the emergence of the pathogen, the immunization methods proved to be more practical and promising to prevent outbreaks due to coccidia. Since the early 1950s, several attempts were followed to formulate commercial immunotherapies, but up till now none proved to be sufficient. This review summarizes, classifies, and evaluates the trials performed to prevent avian coccidiosis, thereafter introduces an out of frame scientific strategy to find a solution for that emerging parasite
infusion protects oxygenation and lung mechanics in COPD patients during general anesthesia. A randomized clinical trial
No full textEffects of Integrating Light Emitting Diode (LED) on Different Fabrics Properties Used for Fashion Design
No full textAnalysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT
No full textThe IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification
Federated inception-multi-head attention models for cyber-attacks detection
No full textWith the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention
Deep Reinforcement Learning Approach for Cyberattack Detection
No full textRecently, there has been a growing concern regarding the detrimental effects of cyberattacks on both infrastructure and users. Conventional safety measures, such as encryption, firewalls, and intrusion detection, are inadequate to safeguard cyber systems against emerging and evolving threats. To address this issue, researchers have turned to reinforcement learning (RL) as a potential solution for complex decision-making problems in cybersecurity. However, the application of RL faces various obstacles, including a lack of suitable training data, dynamic attack scenarios, and challenges in modeling real-world complexities. This paper suggests applying deep reinforcement learning (DRL), a deep framework, to simulate malicious cyberattacks and enhance cybersecurity. Our framework utilizes an agent-based model that is capable of continuous learning and adaptation within a dynamic network security environment. The agent determines the most optimal course of action based on the network’s state and the corresponding rewards received for its decisions. We present the outcomes of our experimentation with the application of DRL on a specific model, double deep Q-network (DDQN), utilizing policy gradient (PG) on three distinct datasets: NSL-KDD, CIC-IDS-2018, and AWID. Our research demonstrates that DRL can effectively improve cyberattack detection outcomes through our model and specific parameter adjustments
A survey about deep learning and federated Learning in cyberse-curity
No full textAdvances in Artificial Intelligence (AI) technology have led to the strengthening of traditional systems\u27 cybersecurity capabilities in a variety of applications. However, these embedded machine learning models have exposed these systems to a new set of vulnerabilities known as AI assaults. These systems are now attractive targets for cyberattacks, jeopardizing the security and safety of bigger systems that include them. As a result, DL approaches are critical to transitioning network and system protection from providing safe communication between systems to intelligence systems in security. Federated learning (FL) is a new kind of AI based on heterogeneous datasets and decentralized training. FL is a unique research topic that is currently in its early phases. It has not yet gained wide acceptance in the community, owing mostly to privacy and security considerations. In this research, we first shed light on its privacy and security risks that must be discovered, analyzed, and recorded. FL is favored in scenarios where privacy and security are paramount issues. An extensive understanding of risk factors allows an FL adopter and implementer to construct a safe environment successfully while giving researchers a clear perspective of possible study domains. The survey in this paper intends to include an analysis of cybersecurity and DL approaches and modern advances to improve enhanced protection methods. It proposes a complete examination of FL\u27s security and privacy issues to assist in bridging the gap between the current level of federated AI and a future in which broad adoption is achievable. We also propose a range of cybersecurity datasets and the most recently used rating standards. 
A Hybrid-Transformer-Based Cyber-Attack Detection in IoT Networks
No full textThe concept of the Internet of Things (IoT) is significant in today’s world and opens up new opportunities for several organizations. IoT solutions are proliferating in fields such as self-driving cars, smart homes, transportation, and healthcare, and new services are constantly being created. Over the previous decade, society has seen a significant expansion in IoT connectivity. In reality, IoT connectivity will expand in a variety of domains over the next few years. Various problems must be overcome to permit effective and secure operations. However, growing connections increase the potential for cyber-attacks since attackers can exploit the broad network of linked devices. Artificial intelligence (AI) detects and prevents cyber assaults by constantly developing and adjusting to new threats and weaknesses. In this study, we offer a novel cyber-detection model for IoT networks based on convolutional neural networks (CNN) transformers. The study aims to enhance the system’s ability to identify and detect cyberattacks, new and sophisticated assaults, and its performance. The experimental study findings, using a new cybersecurity CICIoT2023 dataset, show that the CNN-Transformer model can detect IoT hazards with an overall accuracy of 99.49%. In identifying hazardous activity, MLP accuracy is 99.39%, while XGBoost-pipeline accuracy is 99.40%
