ABSTRACT
Companion bots such as chatbots in cyberspace or robots in real space gained popularity during the COVID-19 pandemic as a means of comforting humans and reducing their loneliness. These bots can also help enhance the lives of elderly people. In this paper, we present how to design and implement a quick prototype of companion bots for elderly people. A companion bot named "Hello Steve"that is able to send emails, open YouTube to provide entertainment, and remember the times an elderly person must take medicine and remind them is designed and implemented as a quick prototype. In addition, the bot combines the features of a mobile robot and a chatbot. The experimental results show the effectiveness of the design through its very high accuracy when navigating mobile-robot-like tasks and responding to chatbot-like tasks via voice commands. © 2023 MYU K.K.
ABSTRACT
The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject. © 2023 IEEE Computer Society. All rights reserved.
ABSTRACT
To provide the ease control and remote monitoring, Internet of Things (IoT) plays an important role in smart devices. The IoT system ranges from smart city to healthcare sector, and supply chain management. This extent of advancement generated a huge amount of data which may be the reason of malware threats of the IoT system. IoT Malware is a threat which may affects all sectors such as business, network, telecoms, media, military, etc. The recent report claimed the proliferation of global cost of malware estimated that till 2023 it would be around 8 trillion dollars annually which may double due to coronavirus outbreak. The analysis of IoT malware needs serious concern as now warfare and digital retaliation can cause serious damage than the war lead on ground. The major aim of this paper is performing the critical analysis of an IoT malware named Emotet. The IoT malware analysis can be categorized in two types such as static and dynamic malware analysis. Static analysis is the process of analyzing malware or binary without executing it. It is considered a more effective method when it comes to the diversity of processor architecture. While dynamic analysis is based on the detection of malware and its behavior with real-time execution. This paper focused on the testbed and Analysis of Emotet malware statically and dynamically using distinguished malware analysis tools. © 2023 IEEE.
ABSTRACT
The World Health Organization defines vaccine hesitancy as a delay in acceptance or refusal of vaccination despite the availability of vaccination services. Vaccine hesitancy contributes to lower rates of vaccination in a population and delayed vaccine coverage. A large number of COVID-19 vaccines have been administered worldwide against COVID-19. Due to concerns people have about COVID-19 vaccine adverse events, a significant proportion of people exhibit hesitancy towards the vaccines. These are often prompted by information and misinformation spread through social media conversation, which is not driven exclusively by genuine human-run accounts. Social bots have been shown to be very active during the pandemic participating in discussions about vaccines, including the spread of conflicting and misleading information. Using a novel ensemble technique, we sought to identify and describe the involvement of social bots in COVID-19 vaccination-related discussions on Twitter and how this could have influenced sentiments and hesitancies about COVID-19 vaccines. We included tweets from January to December 2021 to present a whole year's analysis in relation to the vaccines. Unique usernames from these posts were passed to Botometer and Tweetbotornot, programs that review Twitter accounts, to detect a broad range of social bots using a scoring system. A domain-oriented transfer learning technique is applied by finetuning the CT-BERT V2 model to detect the influence of social bots on COVID-19 vaccine sentiments. We computed the ratio of sentiment transmission from bots-to-human, human-to-human, human-to-bots, and bots-to-bots. BERTopic was used to extract the topics of discussion to identify the amplified or transferred hesitancies. Social bots' participation in online discussions noticeably influenced human sentiments and hesitancies about COVID-19 vaccination. A major portion of sentiments transferred from bot to human during the period of study appeared to amplify or transfer hesitancies regarding COVID-19 vaccination. © 2023 ACM.
ABSTRACT
Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats. DDoS attacks have evolved in quantity and volume to evade detection and increase damage. Changes during the COVID-19 pandemic have left traditional perimeter-based security measures vulnerable to attackers that have diversified their activities by targeting health services, e-commerce, and educational services. DDoS attack prediction searches for signals of attack preparation to warn about the imminence of the attack. Prediction is necessary to handle high-volumetric DDoS attacks and to increase the time to defend against them. This survey article presents the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction. It highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks. Further, this survey details each identified study and, finally, it emphasizes the research opportunities to evolve the DDoS attack prediction state-of-the-art.
ABSTRACT
When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. © 2022
ABSTRACT
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022
ABSTRACT
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively.
ABSTRACT
Corona Virus Disease 2019 (COVID-19) has led to an increase in attacks targeting widespread smart devices. A vulnerable device can join multiple botnets simultaneously or sequentially. When different attack patterns are mixed with attack records, the security analyst produces an inaccurate report. There are numerous studies on botnet detection, but there is no publicly available solution to classify attack patterns based on the control periods. To fill this gap, we propose a novel data-driven method based on an intuitive hypothesis: bots tend to show time-related attack patterns within the same botnet control period. We deploy 462 honeypots in 22 countries to capture real-world attack activities and propose an algorithm to identify control periods. Experiments have demonstrated our method's efficacy. Besides, we present eight interesting findings that will help the security community better understand and fight botnet attacks now and in the future.
ABSTRACT
With the growing popularity of Android smart devices, and especially with the recent advances brought on by the COVID-19 pandemic on digital adoption and transformation, the importance of protecting these devices has grown, as they carry very sensitive data. Malicious attacks are targeting Android since it is open source and has the highest adoption rate among mobile platforms. Botnet attacks are one of the most often forgotten types of attacks. In addition, there is a lack of review papers that can clarify the state of knowledge and indicate research gaps in detecting android botnets. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of Android Botnet detection. This study attempts to provide a comprehensive overview of the deployed AI apps for future academics interested in performing Android Botnet Detection studies. We focused on the applications of artificial intelligence and its two prominent subdomains, machine learning (ML) and deep learning (DL) techniques. The study presents available Android Botnet datasets suitable for detection using ML and DL algorithms. Moreover, this study provides an overview of the methodologies and tools utilized in APK analysis. The paper also serves as a comprehensive taxonomy of Android Botnet detection methods and highlights a number of challenges encountered while analyzing Android Botnet detection techniques. The research gaps indicated an absence of hybrid analysis research in the area, as well as a lack of an up-to-date dataset and a time-series dataset. The findings of this paper show valuable prospective directions for future research and development opportunities. Author
ABSTRACT
DDoS attacks are noticed from last many years but due to growing figure of such attacks in present time increases the awareness of them. Many researchers proposed useful detection and mitigation methods for such DDoS attacks. DDoS attack is somewhat simple to perform, hard to safeguard against, and the aggressor is once in a while followed back. The assailant dispatches a DDoS assault utilizing a botnet to produce immense measure of traffic against a casualty's web worker. The casualty might be a business association, government, or basic framework. The wellspring of the attack can be any gadget associated with the web. During the last one and half year of covid-19 pandemic, the exponential growth of about 542% for such attacks is noticed. As all the organizations started working online, the security solutions that provide a safe and secure online working environment are required more. Software-Defined Networks solution is the better option for such requirements. It is a stage towards the foundation of a dynamic and unified nature of the organization. In this paper, we have reviewed that challenges and solutions for SDN networks. The study reveals important detection and mit-igation methods and strategies against DDoS attacks. © 2021 IEEE.
ABSTRACT
The Internet has become an essential part of life, especially after the COVID-19 pandemic. The increasing use of technology brings new challenges. Cyber security has emerged as a major threat during the pandemic. Distributed Denial of Service Attack (DDoS) attacks have become more refined than other cyber-attacks during the pandemic. The most important question comes into mind: What is the source of the DDoS attack? The answer is botnet which provides the platform for the attacker. A botnet has targeted the escalation of vulnerable systems. Therefore, real-life and accurate botnet detection and prevention techniques must be effectively designed. Due to this organized dataset, IoCs are required for a most dangerous botnet to prevent networks at an early stage. Various malware datasets have been published for the research work, but most are outdated. The author has proposed a new dataset of windows based botnets using different analysis techniques. This work provides the geolocation of the live malicious connection made by emotet. They have also presented the mechanism which calculates the IP reputation and detects botnet based on IoCs using snort Intrusion Detection. © 2022 Seventh Sense Research Group®
ABSTRACT
Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer. © 2022 Bharati Vidyapeeth, New Delhi.
ABSTRACT
In the global pandemic, social media platforms are the primary source of information exchange. Social bots are one of the main sources of misinformation in the COVID-19 pandemic but do social bots spread the fake and real news with the same ratio as human accounts on social media platforms? Can bot detection improve fake news detection on social media platforms? Who presents more fake news in the COVID-19 pandemic, Human or social bots? This work provides preliminary research results based on limited data to answer these questions, but it opens a new perspective on fake news detection and bot detection on online platforms. We use Bidirectional Encoder Representations from Transformers(BERT) to create a new model for fake news detection. We use the transfer learning model to detect bot accounts in the COVID-19 data set. Then apply new features to improve the new fake news detection model in the COVID-19 data set. © 2021 IEEE.
ABSTRACT
Botnets can simultaneously control millions of Internet-connected devices to launch damaging cyber-attacks that pose significant threats to the Internet. In a botnet, bot-masters communicate with the command and control server using various communication protocols. One of the widely used communication protocols is the 'Domain Name System' (DNS) service, an essential Internet service. Bot-masters utilise Domain Generation Algorithms (DGA) and fast-flux techniques to avoid static blacklists and reverse engineering while remaining flexible. However, botnet's DNS communication generates anomalous DNS traffic throughout the botnet life cycle, and such anomaly is considered an indicator of DNS-based botnets presence in the network. Despite several approaches proposed to detect botnets based on DNS traffic analysis; however, the problem still exists and is challenging due to several reasons, such as not considering significant features and rules that contribute to the detection of DNS-based botnet. Therefore, this paper examines the abnormality of DNS traffic during the botnet lifecycle to extract significant enriched features. These features are further analysed using two machine learning algorithms. The union of the output of two algorithms proposes a novel hybrid rule detection model approach. Two benchmark datasets are used to evaluate the performance of the proposed approach in terms of detection accuracy and false-positive rate. The experimental results show that the proposed approach has a 99.96% accuracy and a 1.6% false-positive rate, outperforming other state-of-the-art DNS-based botnet detection approaches.
ABSTRACT
Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.