Figure - available from: Wireless Communications and Mobile Computing
This content is subject to copyright. Terms and conditions apply.
Source publication
During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes an...
Citations
... Various studies have reported different variants of intrusive activities in the social network [8]. There are also accomplished studies and ongoing research toward strengthening social network security [9]. However, due to their publicly exposed contents, no tool or program has yet been identified or benchmarked as offering full-proof security in social networks. ...
... Introduction e world went into a state of emergency in December of 2019 when the COVID-19 virus (coronavirus) started spreading, causing a lot of hospitalizations and, in many cases, death (1,2). It is a virus that attacks the respiratory system of an infected person, causing shortness of breath and eventually inability to breathe (3). ...
The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.
Internet-based social engineering (SE) attacks are a major cyber threat. These attacks often serve as the first step in a sophisticated sequence of attacks that target, among other things, victims’ credentials and can cause financial losses. The problem has received mounting attention in recent years, with many publications proposing defenses against SE attacks. Despite this, the situation has not improved. In this article, we aim to understand and explain this phenomenon by investigating the root cause of the problem. To this end, we examine Internet-based SE attacks and defenses through a unique lens based on psychological factors (PFs) and psychological techniques (PTs). We find that there is a key discrepancy between attacks and defenses: SE attacks have deliberately exploited 46 PFs and 16 PTs in total, but existing defenses have only leveraged 16 PFs and seven PTs in total. This discrepancy may explain why existing defenses have achieved limited success and prompt us to propose a systematic roadmap for future research.
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffec-tive. Although prior writers have proposed numerous high-quality approaches, static and dy-namic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tai-lored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves ap-plying a newly proposed detection model to classify android apps; this model uses a neural net-work optimized with an improved version of HHO. Application of binary and multi-class classi-fication is used, with binary classification for benign and malware apps and multi-class classifica-tion for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classifi-cation. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.
The rapid development of cyber-physical systems results in vast amount of heterogeneous data generated every day. To deal with unstructured data and maintain its security and privacy in smart manufacturing, it is necessary to merge social space with cyber-physical systems to develop cyber-physical-social systems. Crowdsourcing and knowledge transfer can be effective approaches to solve problems of manufacturing and product development, such as inviting hackers to break the security bridge to test the efficacy of the measures and handling enormous data generated in cyber-physical-social system. Crowdsourcing is a novel computing paradigm that leverages human effort to tackle computationally difficult issues, whereas knowledge transfer helps complete the new assignment based on quick access to the existing knowledge. As a result, an enhanced annotation for the work may be done at a low cost via suitable knowledge transfer. This paper introduces cyber-physical-social system and highlights the challenges and issues arising from massive data generated over the internet by various sources, and discusses how pattern recognition techniques can be used to identify anomalies or attacks. It also defines the terms knowledge transfer and crowdsourcing and explains how they can be effectively used to solve a problem in cyber-physical-social systems.
