Identification Of Attacks Research Articles

Security Issues on Industrial Internet of Things: Overview and Challenges

The Industrial Internet of Things (IIoT), where numerous smart devices associated with sensors, actuators, computers, and people communicate with shared networks, has gained advantages in many fields, such as smart manufacturing, intelligent transportation, and smart grids. However, security is becoming increasingly challenging due to the vulnerability of the IIoT to various malicious attacks. In this paper, the security issues of the IIoT are reviewed from the following three aspects: (1) security threats and their attack mechanisms are presented to illustrate the vulnerability of the IIoT; (2) the intrusion detection methods are listed from the attack identification perspectives; and (3) some defense strategies are comprehensively summarized. Several concluding remarks and promising future directions are provided at the end of this paper.

Identification and Percentage of Disease Pathogen Attacks on Primary Palm Oil Crops

The aim of this research is to identify and determine the percentage of diseases caused by fungi in oil palm seedlings. The purposive sampling method and the objects observed were fungi that cause disease in oil palm seedlings microscopically at the Jambi Class 1 Agricultural Quarantine Laboratory. The results of the research found that Curvularia sp disease had a percentage of attacks in nursery one (f?) of 25%, while the intensity of attacks in nursery one (f?) was 5%, in nursery two (f?) the percentage of attacks was 25% while the intensity of attacks in nursery two (f?) 6%, and in the third nursery (f?) the attack percentage was 10%, while the attack intensity in the third nursery (f?) was 2%. The percentage of attacks by Pestalotiopsis sp in nursery one (f?) is 20%, while the intensity of attacks in nursery one (f?) is 4%, in nursery two (f?) the percentage of attacks is 5%, while the intensity of attacks in nursery two (f?) is 1%, and in nursery three (f?) attack percentage was 20% while the attack intensity in the three (f?) nursery was 4%. From this research, it was concluded that two diseases were found in oil palm seedlings, namely Curvularia sp leaf spot and Pestalotiopsis sp leaf spot.

Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach.

Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines. This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits. Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, "vaccinate" and "Pfizer") and mandates (eg, "forced" and "mandating"). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10% sample of the filtered key terms was used for the final analysis. Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001). This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19.

Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods

AbstractSoftware‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%.

Deep Forest-Based E-Commerce Recommendation Attack Detection Model

With the advancement of Internet, recommendation systems have become an indispensable component for every e-commerce platform, playing an increasingly pivotal role in product recommendations. However, due to the recommendation mechanisms of these systems, numerous new attack patterns have emerged. A novel attack pattern targeting e-commerce recommendation systems, termed the “Ride Item’s Coattails” attack, fabricates false click information to deceitfully establish associations between popular products and low-quality products, intending to mislead the e-commerce platform’s recommendation system and promote the sales of substandard products. This article presents a recommendation system attack detection method based on the Deep Forest algorithm to address the challenges of these novel recommendation system attacks. Random forests are used for feature selection, aiming to filter crucial features and reduce feature redundancy. To tackle the issue of extreme class imbalance, a symmetric sampling technique based on k-means centroids is introduced. This approach addresses the incomplete noise filtering and sampling data comprehensiveness issues commonly found in undersampling algorithms. Considering the potential for even more imbalanced data in real-world scenarios, a combined strategy of undersampling and SMOTE resampling is used to handle imbalanced data. The proposed algorithm was trained on e-commerce “Ride Item’s Coattails” attack identification data from Alibaba Cloud’s Tianchi, which originates from genuine recommendation system attack data. The proposed method was compared with various deep learning and machine learning algorithms, such as DLMP and deep neural networks (DNN), for extensive validation. Experiments demonstrate that the proposed technique effectively meets the demands for attack detection.

Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model

In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While the utilization of the internet amongst consumers is increasing on a daily basis, the significance of security and privacy preservation of system alerts, due to malicious actions, is also increasing. IDS is a widely executed system that protects computer networks from attacks. For the identification of unknown attacks and anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, in real-world applications, the classification performances of these approaches are fluctuant with distinct databases. The major reason for this drawback is the presence of some ineffective or redundant features. So, the current study proposes the Network Intrusion Detection System using a Lion Optimization Feature Selection with a Deep Learning (NIDS-LOFSDL) approach to remedy the aforementioned issue. The NIDS-LOFSDL technique follows the concept of FS with a hyperparameter-tuned DL model for the recognition of intrusions. For the purpose of FS, the NIDS-LOFSDL method uses the LOFS technique, which helps in improving the classification results. Furthermore, the attention-based bi-directional long short-term memory (ABiLSTM) system is applied for intrusion detection. In order to enhance the intrusion detection performance of the ABiLSTM algorithm, the gorilla troops optimizer (GTO) is deployed so as to perform hyperparameter tuning. Since trial-and-error manual hyperparameter tuning is a tedious process, the GTO-based hyperparameter tuning process is performed, which demonstrates the novelty of the work. In order to validate the enhanced solution of the NIDS-LOFSDL system in terms of intrusion detection, a comprehensive range of experiments was performed. The simulation values confirm the promising results of the NIDS-LOFSDL system compared to existing DL methodologies, with a maximum accuracy of 96.88% and 96.92% on UNSW-NB15 and AWID datasets, respectively.

OFIDS : Online Learning-Enabled and Fingerprint-Based Intrusion Detection System in Controller Area Networks

As a widely used industrial field bus, the controller area network (CAN) lacks security mechanisms (e.g., encryption and authentication). With the development of network connectivity and the increase in external communication, CAN networks are vulnerable to security attacks (e.g., masquerade). Attackers can intrude on CAN networks to control CAN devices, causing security threats. A fingerprint-based intrusion detection system (IDS) in CAN networks can detect masquerade attacks by scanning the unique clock signals of CAN devices. However, most state-of-the-art fingerprint-based IDSs commonly use an analog-to-digital converter module with a low frequency of 60 MHz to sample CAN signals, lowering the accuracy of the trained fingerprint-based IDS model. In addition, almost all fingerprint-based IDSs are trained offline and then detected online in CAN networks, ignoring that system clock signals of hardware change over time, resulting in degraded detection performance. An intrusion detection system based on physical fingerprints can achieve effective identification of masquerade attacks. This paper proposes an online learning-enabled and fingerprint-based IDS (OFIDS) in CAN networks to increase the sampling frequency, simplify the sampling circuit, shorten the delay time, improve the detection response time, and increase the detection accuracy. OFIDS uses a high-speed comparator (i.e., TLV3501) and field-programmable gate arrays (FPGA; i.e., Xilinx ZYNQ-7010) to sample the CAN_High signal, achieving a low sampling delay time of 4.5 ns and a high sampling frequency of 1 GHz. The self-adaptability of the backpropagation neural network is taken advantage of and used to train the OFIDS model with a detection accuracy of 99.9992%. The OFIDS model is deployed to a CAN network prototype with five CAN devices (i.e., two Arduino UNO boards and three STM32 microcontrollers), where online learning is conducted with a low training time of less than 2 min to update the OFIDS model. Experimental results show that OFIDS can achieve at least 99.99% detection accuracy within 0.18 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s in a CAN network prototype and can achieve 98% detection accuracy in a real vehicle.

Detection of DoS Attacks in Smart City Networks With Feature Distance Maps: A Statistical Approach

Smart cities are highly digitalized cities, with a high volume of data stored digitally, as well as a large number of physical objects and Internet of Things (IoT) devices. It is no surprise that security concerns increase as connected IoT devices become more prevalent. There has long been a perception that Denial of Service (DoS) attacks give rise to a notable menace to the security of smart city networks. A statistical method based on feature distance maps is suggested in this work for the identification of DoS attacks in smart city networks. This study aims to build a DoS attack detection model that has a low computational complexity and a low rate of false positives. The performance of the model is arbitrated using smart city network traffic dataset. In order to make processing easier, the features are normalized using Min-Max normalization. A subset of features suitable for attack detection can be evaluated with Pearson Correlation Coefficient. A feature distance map method is proposed for extracting correlations between features between records. Manhattan distance measures are used to generate the feature distance maps. The feature distance maps enhance the statistical analysis process. Hellinger distance measure is used to generate the normal traffic profile by calculating the degree of similarity between the feature distance map of each record and the normal traffic profile mean. On the basis of the proposed threshold, the Hellinger distance is used to evaluate whether an unknown traffic data belongs to normal or attack. Compared with Mahalanobis distance-based DoS detection, the Hellinger distance-based attack detection takes less time to execute. Based on the theoretical and experimental results, the proposed DoS attack detection system has a low computational complexity and a low false positive rate. Furthermore, the proposed approach outperforms the existing state-of-the-art feature ranking based DoS attack detection systems.

Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis

This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females.

Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles.

The Internet of Vehicles(IoV) employs vehicle-to-everything (V2X) technology to establish intricate interconnections among the Internet, the IoT network, and the Vehicle Networks (IVNs), forming a complex vehicle communication network. However, the vehicle communication network is very vulnerable to attacks. The implementation of an intrusion detection system (IDS) emerges as an essential requisite to ensure the security of in-vehicle/inter-vehicle communication in IoV. Within this context, the imbalanced nature of network traffic data and the diversity of network attacks stand as pivotal factors in IDS performance. On the one hand, network traffic data often heavily suffer from data imbalance, which impairs the detection performance. To address this issue, this paper employs a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) and RandomUnderSampler to achieve a balanced class distribution. On the other hand, the diversity of network attacks constitutes another significant factor contributing to poor intrusion detection model performance. Most current machine learning-based IDSs mainly perform binary classification, while poorly dealing with multiclass classification. This paper proposes an adaptive tree-based ensemble network as the intrusion detection engine for the IDS in IoV. This engine employs a deep-layer structure, wherein diverse ML models are stacked as layers and are interconnected in a cascading manner, which enables accurate and efficient multiclass classification, facilitating the precise identification of diverse network attacks. Moreover, a machine learning-based approach is used for feature selection to reduce feature dimensionality, substantially alleviating the computational overhead. Finally, we evaluate the proposed IDS performance on various cyber-attacks from the in-vehicle and external networks in IoV by using the network intrusion detection dataset CICIDS2017 and the vehicle security dataset Car-Hacking. The experimental results demonstrate remarkable performance, with an F1-score of 0.965 on the CICIDS2017 dataset and an F1-score of 0.9999 on the Car-Hacking dataset. These scores demonstrate that our IDS can achieve efficient and precise multiclass classification. This research provides a valuable reference for ensuring the cybersecurity of IoV.

Proposed Secure Hypertext Model in Web Engineering

Secure web application development is one of the prime challenges for the software industry. In the last decade, web applications have rapidly developed but web engineering methods have some limitations while designing web applications. The extensive literature survey explores various concepts like web engineering, hypertext modelling, web applications hypertext modelling methods, attacks on web applications, same origin policy (SOP) and cross origin resource sharing (CORS). The complexity of web pages is a major concern for security. The proposed secure hypertext model (SHM) provides hypertext modelling of web applications and helps in the identification of attacks on hypertext links. It provides security stereotypes and precisely specifies vulnerability defences in web application design. This standardized attack vector and defence mechanism will help developers to build more secure applications.

Spoofing Traffic Attack Recognition Algorithm for Wireless Communication Networks in a Smart City Based on Improved Machine Learning

Abstract It is difficult to find spoofing traffic attack information for a wireless communication network, which leads to poor performance of spoofing traffic attack identification. Therefore, a spoofing traffic attack recognition algorithm for wireless communication networks based on improved machine learning has been designed. The process of network traffic classification and several common network cheating traffic attacks are analyzed. A chaotic algorithm is used to search and collect wireless communication network data, and Min-Max and z-score are used to standardize the collected data. The risk assessment function of wireless communication network spoofing traffic attack is constructed, and the spoofing traffic attack is preliminarily determined according to the function. The convolutional neural network in machine learning is improved, and the preliminary judgment results are input into the improved convolutional neural network to identify the attack behavior. The experimental results show that the recall rate of this method for wireless communication network spoofing traffic attacks can reach 90.08 % at the highest level, and the identification process takes only 1,763 ms at the lowest level. It can control the false positive rate of attacks below 4.68 % and the false positive rate below 2.00 %, and the identification effect of spoofing traffic attacks is good.

Detection and defense of time‐varying formation for unmanned aerial vehicles against false data injection attacks and external disturbance

AbstractThis article investigates the detection and defense method of time‐varying formation for unmanned aerial vehicles (UAVs) against external disturbance and false data injection attacks (FDIAs). First, to detection attacks, a distributed attack identification filter is constructed for each UAV. Second, two distributed extended state observers are designed to separate and estimate external disturbances and FDIAs. Third, the distributed time‐varying formation tracking protocol and defense strategy for attacks is designed by utilizing the estimated information. The formation system is shown to asymptotically track the desired formation and trajectory and meanwhile reject the disturbance and attacks if the expected formation satisfies the feasible condition by stability analysis. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

Explainable deep learning for attack intelligence and combating cyber–physical attacks

Cyber–physical control loops comprising sensors, actuators and controllers pose the most valued and critical part of the industrial Internet of Things (IIoT) as it regulates the state of the physical process, such as water treatment or gas flow. Thus, any malicious activities could lead to physical damage, affecting human safety. Cyber–physical attacks against the physical process are difficult to detect using existing threats and attack intelligence due to the (1) lack of such intelligence for the physical process and operational technology systems and (2) such attacks affect the process parameters and states. Artificial Intelligence (AI)-based attack intelligence is required. This study proposes an attack intelligence framework for identifying cyber–physical attacks and extracting attack intelligence. We propose an attribution module for attack identification using various machine and deep learning algorithms. We also utilize Explainable AI (XAI) to improve the explainability of the attack attribution module and extract attack intelligence. Our proposed framework is evaluated and tested using a gas pipeline dataset as a use case. We demonstrate that the proposed framework improves the understanding of attacks and provides attack rules, assisting security analysts in securing critical physical processes.

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

Enhanced neural network-based attack investigation framework for network forensics: Identification, detection, and analysis of the attack

Network forensics aids in the identification of distinct network-based attacks through packet-level analysis of collected network traffic. It also unveils the attacker's intentions and operations. After identification, it is inevitable to design an efficient network attack detection model. Therefore, this work modifies the generic network forensic framework for attack investigation with two primary objectives i.e., Analysis and detection of attacks. In the proposed framework, a three-level analysis is performed. First, packet-level analysis is performed to study the attack behavior. Second, a graphical analysis is completed to determine both the attack flow and whether a node is an attacker or a victim. Moreover, it also assigns a score to the node indicating the severity of the attack. Finally, forensics exploratory data analysis (FEDA) is performed to distinguish the distribution of different features during attack and normal scenarios. For attack detection, the framework uses a convolution neural network (CNN-1D). CSE-CIC-IDS2018 (CIC2018), UNSW-NB15 and CIC-Darknet2020 datasets are used to test the performance of the proposed framework, wherein, it classifies distinct classes of attacks with an accuracy of 99.4%, 99.0%, and 90% on each dataset respectively. The results show that the proposed framework is more effective than previous works in attack detection and network traffic classification.

Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET) prevention technique with examined detection rate, memory consumption with minimum overhead are vital concerns. Node mobility and node energy are the two optimization problems in MANETs wherein nodes travel uncertainly in any direction, evolving in a continuous change of topology. With the proposed approach, a Centrality Coati Optimization Algorithm based Cluster Gradient for multi attack intrusion identification is devised. This study focuses on the problems of node mobility and energy to develop a clustering algorithm for cluster head selection in MANET that is incited by Dual Network Centrality. Compact cluster formation is carried out by Coati Optimization Algorithm (COA). The Multi-head Self-Attention based Gated Graph Convolutional Network (MSA-GCNN) with a hybrid type of IDS recognizes several attacks, including DoS and Zero-Day attacks. The proposed technique is implemented in NS-2 network simulator. The performance of proposed approach is examined under some parameters, like attack detection rate, memory consumption, computational time for detecting the intruder. The outcomes display that the proposed technique decreases the IDS traffic and entire consumption of memory and sustains a higher attack identification rate with less computational time. The proposed technique attains 4.299 %, 10.375 % and 6.935 % Accuracy, 5.262 %, 8.375 % and 7.945 % Precision, 7.282 %, 10.365 % and 5.935 % Recall, 9.272 %, 5.355 % and 8.965 % ROC is higher compared with the existing methods such as, Epsilon Swarm Optimized Cluster Gradient along deep belief classifier for multiple attack intrusion detection (ESOC-MA-ID-MANET), Intrusion Detection secure solution for intrusion detection in cloud computing utilizing hybrid deep learning approach called EOS-IDS and improved heap optimization (IHO-MA-ID-MANET) for induction detection technique respectively.

Data-Driven Attack Detection and Identification for Cyber-Physical Systems Under Sparse Sensor Attacks

This paper studies the issues of data-driven attack detection and identification for cyber-physical systems under sparse sensor attacks. First, based on the available input and output data sets, a data-driven monitor is formulated with two objectives: attack detection and attack identification. Then, with the subspace approach, a data-driven attack detection policy is presented, wherein the attack detector is designed directly by the process data. A subspace projection-based attack identification scheme is proposed via designing a bank of projection filters to determine the locations of attacked sensors. Moreover, the sparse recovery technique is adopted to decrease the combinatorial complexity of the subspace projection based identification method. The attack identification is recast into a block-sparse recovery problem. Finally, the proposed methods are verified by the simulations on a flight vehicle system.

Impolite Communication Practices as a Community-Building Tool in Sports News Comments

Conflict discourse studies provide a valuable insight into linguistic and sociocultural ideologies of online communities. They also may offer effective approaches to linguistic detoxification. This research featured online communication of sport fans. Their discourse is interdiscursive, possesses a certain netiquette, and reflects the social and ideological diversity within the community. The authors explored common speech strategies aimed at threatening the social identity of outsiders or at the acquisition of power. The material involved an array of comments posted on Sports.ru: each example was tested for speech aggression, and the conflict-marked comments were described from the strategy perspective. The sampling relied on the general intention and expressive vocabulary aimed at the addressee. The frequency distribution of impoliteness strategies revealed that users often tended to assert power through personal characterization of their opponents, e.g., agism, sarcasm, hypercorrect speech behavior, identity attacks via derogatory team nominations, etc. The analysis identified various conflict strategies employed to form derogatory nominations, as well as defined the socio-cultural characteristics that marked the addressee as a target of conflictladen speech behavior.

Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation

Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods.