Malicious Intent Research Articles

AI-based Cybersecurity Attacks and Countermeasures in IoT Environment: A Survey

The Internet of Things (IoT) has been considered an innovative integrated solution that opened opportunities for intelligent appliances to communicate over the Internet. However, IoT suffers from various cybersecurity threats because of its openness and the constraints of the used devices that share substantial amounts of classified information. Artificial intelligence (AI) is an effective key technology in solving cybersecurity issues. Cybercriminals are adopting AI techniques to conduct sophisticated attacks on IoT environments. Precise and elusive attacks across various applications have exemplified the deliberate employment of artificial intelligence for malicious intentions. Threat actors are constantly changing and improving their attack strategies with particular emphasis on applying AI-driven techniques in the attack process, called AI-based cybersecurity attacks, which can be used with conventional attack techniques to cause considerable damage. Moreover, AI techniques can be utilized in cyber-attack countermeasures and have proven effective. Unfortunately, these types of attacks and countermeasures have not been investigated enough in the existing literature. This paper surveys the current studies of AI-based cybersecurity attacks and countermeasures in the IoT environment. The aspects captured by this paper highlight future research in this field and will assist in recognizing the power and impact of using AI in cybersecurity, especially in the IoT environment.

Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models

Recent research has found that neural networks for computer vision are vulnerable to several types of external attacks that modify the input of the model, with the malicious intent of producing a misclassification. With the increase in the number of feasible attacks, many defence approaches have been proposed to mitigate the effect of these attacks and protect the models. Mainly, the research on both attack and defence has focused on RGB images, while other domains, such as the infrared domain, are currently underexplored. In this paper, we propose two attacks, and we evaluate them on multiple datasets and neural network models, showing that the results outperform others established attacks, on both RGB as well as infrared domains. In addition, we show that our proposal can be used in an adversarial training protocol to produce more robust models, with respect to both adversarial attacks and natural perturbations that can be applied to input images. Lastly, we study if a successful attack in a domain can be transferred to an aligned image in another domain, without any further tuning. The code, containing all the files and the configurations used to run the experiments, is available <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jaryP/IR-RGB-domain-attackonline</uri> .

Intentional noncompliance: influencing employees' compliance decision in healthcare services

Ensuring service execution compliance with the requisites of day-today operational tasks continues to be a major managerial challenge for service systems, particularly in the healthcare context where patients' safety is at stake. In this qualitative inquiry, we use the data collected from a nursing care organisation to report on one underexplored category of employee failures: intentional noncompliance at the service execution stage. This specific category of failures happens when employees knowingly choose to deviate from the standards of the planned care, yet they have no malicious intention for sabotaging the organisation and/or its stakeholders. Based on our findings, preventing employees' intentional noncompliance requires designing compliance enablers that dampen the negative impact of socio-psychological inhibitors, which manifest in the form of personal and interpersonal traits and legitimise the employees' choice of deviation from the requirements.

Interpreting the Intentional Inaccessibility of the Early Modern Roman Catholic Church

Many may wonder why, in the modern day, the Roman Catholic Church continues to incorporate Latin, a now barely extant language, into its canonic religious rituals and public services. However, to understand whether there is a latently malicious intent lurking beneath this esoteric tradition, we must look back to the 1545 Council of Trent, in which these traditions were first canonized. This choice on the part of the Catholic Church helped incense the Protestant Reformation’s criticisms against the Church for exclusivizing the very religion its followers were practicing. This paper investigates the origins of the Catholic Church’s suppressive practices against the religious knowledge of the masses and how this tactic backfired on their intentions.

Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature燬election

As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and energy burnout during transmission and propagation of energy are TLs. NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft, along with tampering with AMI for bill reduction by fraudulent customers. This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile. In our proposed methodology, a hybrid Genetic Algorithm and Support Vector Machine (GA-SVM) model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London, UK, for theft detection. A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%, compared to studies conducted on small and limited datasets.

Phish Block: A Blockchain Framework for Phish Detection in Cloud

The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy. But, deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes. In Spite of much technological advancement, phishing acts as the first step in a series of attacks. With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks. The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular domain names, using reserved characters for re directions and multi-chain phishing. Such phishing URLs can be stored as a part of the document and uploaded in the cloud, providing a nudge to hackers in cloud storage. The cloud servers are becoming the trusted tool for executing these attacks. The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end (browser). At the same time, the avalanche effect and immutability of block-chain proves to be a strong source of security. Considering these trends in technology, a block-chain based filtering implementation for preserving the integrity of user data stored in the cloud is proposed. The proposed Phish Block detects the homographic phishing URLs with accuracy of 91% which assures the security in cloud storage.

Privacy-Aware Adversarial Network in Human Mobility Prediction

As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).

Metasurface Manipulation Attacks: Potential Security Threats of RIS-Aided 6G Communications

The physical layer security (PLS) of reconfigurable intelligent surfaces (RIS) is critical for providing secure and reliable communications in sixth generation (6G) wireless systems. This article contributes to a debate on many aspects of unique vulnerabilities that can arise in the PLS of RIS with malicious attacks on signal processing (SP) in 6G networks. We address and categorize future security threats in RIS-aided networks considering PLS aspects. Metasurface manipulation attacks (MSMA) are based on manipulating metasurface behavior for malicious purposes. In this study, we present the potential hostile activities of MSMA considering the impact of electromagnetic and time-frequency deterioration. In addition, we evaluate various interference-based MSMA and eavesdropping booster-based MSMA (EaB-MSMA), which are novel potential PLS attacks that involve manipulating phase shifting activity with SP on malicious RIS. We also compare performance degradation in the presence of hostile interference-based MSMA through the manipulation of maliciously configured RIS. Additionally, in the EaB-MSMA case, the loss of secrecy capacity is shown to be considerably greater in passive eavesdropping scenarios.

An Analysis of the Privacy and Security Issues Affecting the Usage of Social Media

Social media have become extremely popular in recent years, and their widespread adoption has led to the presence of huge volumes of users’ personal information on the Internet. The ever-increasing number of social media users on one hand and the massive amount of information being shared daily on the other hand have encouraged attackers to develop and use different techniques to collect and analyze such information for a number of malicious purposes, including spamming, attacking through viruses, phishing, spear-phishing attacks and identity theft among others. Clearly, this trend represents a significant challenge affecting both users and administrators, and likewise create a privacy and security issues for social media users. In this paper, common security and privacy issues were examined along with recommendations to social media users to protect themselves from these issues whenever they use social media.

The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices.

Ethical Hacking for a Good Cause: Finding Missing People using Crowdsourcing and Open-Source Intelligence (OSINT) Tools

Over 600,000 people go missing every year in the US alone. Despite the extensive resources allocated to investigating these cases, the high volume of missing person cases constitutes one of the biggest challenges for law enforcement agencies. One approach to tackle this challenge is using crowdsourcing. That is, volunteers use freely available tools and techniques to aid the existing efforts to investigate missing person cases. Open-Source Intelligence (OSINT) refers to gathering information from publicly available sources and analyzing it through a comprehensive set of open-source tools to produce meaningful and actionable intelligence. OSINT has been applied to address various societal challenges and crimes, including environmental abuse, human rights violations, child exploitation, domestic violence, disasters, and locating missing people. Building on this premise, this case examines a crowdsourced initiative called Trace Labs that aims to assist law enforcement agencies in solving missing person cases using OSINT tools. The case emphasizes socio-technical aspects of cybersecurity, highlighting both the bright and dark sides of technology. It demonstrates the potential of information systems to serve the public good by examining topics such as open-source software, crowdsourcing, and intelligence gathering, while acknowledging that the very same underlying technology can be used for malicious purposes.

Radar 2: Passive Spy Radar Detection and Localization Using COTS mmWave Radar

Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user’s daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radar</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frequency Component Detection</i> method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector’s observations at multiple anchor points. Not only does <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radar</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radar</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in various settings. Our evaluation results show that the radar detection rate is above 96% and the localization error is within 0.3m. The results also reveal that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radar</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is robust against various environmental factors (e.g., room layout and human activities).

Digital Dystopia in the context of 21st century with special Reference to Cyber world, Sublimity, and artificial Intelligence.

Once Albert Einstein said "I fear the day that technology will surpass our human interaction. The world will have a generation of idiots". This saying is very much relevant so far the 21st century is concerned. The present era is an age of the three M's (Men, Machines and Media). The technological innovations have created such a hyper real society, that gradually people start to forget about their humanism. In order to sublimate their intelligence and ideas for the sake of worldwide development, they give huge importance to digital media instead of their own carnal labour. To execute this purpose, the mechainenary systems as well as social media act like partners in crime with the human brain. Various social media platforms are basically the updated platform of the cyber world, which is very much responsible for manipulating and motivating the psychology of humans. These influences are so high that people do not even think twice before indulging themselves in several crimes through these media platforms. Cyber crimes, robotic invention, uncontrollable dependence on the internet with some malicious purpose - these are enough to establish a tyrannical totalitarian society where everything is under the control of virtual platforms. These actually transform the humans into degenerate beings. Social media is simply a tyrant in disguise. The virtual mode, which helps people to get easily connected with each other, is nothing but a place of utter destruction, better to say a dystopia. Dystopia is the very opposite of an ideal world which involves less crime. In the present 21st century, digital dystopia denotes the artificial, ahistorical digital society where there is a dominance of technology over public and private ongoings of human activities as well as society at large; which yields desolation. The developed applications used in windows, Android, desktops are solely responsible for such chaotic structure of the world. Moreover, there are some underworld people who misuse those applications to distort the syntactic structure of the society. They use to dwell in a metaphorical panopticon ( a kind of cell ) area of society from where every aspect of humans can be keenly observed; but ironically people neither can see them nor can see their way of controlling people. In one word, this present digital dystopian universe acts like the character of Big Brother in George Orwell's dystopian fiction Nineteen Eighty-Four which hovers over this infernal world just like a wild vulture. Literally, people have transformed into idiots at the hands of technology.

Review on Malware, Types, and its Analysis

Abstract: Malicious entities utilize malware as a tool to carry out their nefarious plans. Malicious software, sometimes known as malware or mal-ware, is a type of program with malicious intents. Malware and spam both pose severe hazards to the security of systems, raising complex system issues. Malware that compromises computers through a coordinated attack includes worms, trojan horses, botnets, and rootkits. It is challenging to compile convincing evidence because inadequate tools are available, and the system is still in its infancy. This study examined the challenges faced by investigators in their search for a missing person. The author emphasized the need for a novel approach to malware detection that focuses on a robust framework and offered a solution based on a thorough literature evaluation and market research analysis. While market research was carried out to ascertain the precise nature of the current problem, the literature review concentrated on various malware detection tests and methodologies to establish the parameters for developing a solution design

Application of Statute of Limitation in Private Mediation in India

Throughout its long history, the Indian judiciary has functioned as a contemporary legal system, alongside regular Alternative Dispute Resolution (ADR) processes. Arbitration and conciliation programs were institutionalized through the enactment of the Arbitration and Conciliation Act, 1996. Additionally, the 1999 Amendment to the Code of Civil Procedure officially recognized ADR as a supplement to the legal system, allowing judges to refer cases to these processes. However, it was only in 2021 that the Indian Parliament proposed the Mediation Bill, 2021 with a view to officially recognize and institutionalize mediation processes in India. The purpose of this paper is to review the Indian Mediation Bill, 2021, with a focus on identifying gaps in the mediation process in this Bill, in relation to current practice. In particular, the paper notes the lack of recognition for private mediation processes, although private mediation exists in India. It searches for possible solutions to the problem of identifying the statute of limitation due to the individualized nature of the private mediation process. Currently, even if the private mediation is not court-ordered or court-annexed, the settlement from the process can be reviewed by a judge, and it can be enforced in the same manner as a decree of a civil court. This omission means there is scope for abuse or misuse of private mediation. One cause for this is that the current Mediation Bill, 2021 is silent on the application of the Limitation Act, 1963. Due to the lack of proper regulation of the mediation process, the multifarious mediation programs have not been harmonized within the entire dispute resolution process. Since private mediation is not directly connected with court proceedings, it will be difficult to identify the initial filing date. Therefore, a party with malicious intent might induce the other to miss the filing date of litigation. If a good-faith party, who does not file a litigation case in the court, sincerely focuses on private mediation to resolve a dispute, they might later be disadvantaged as they were unaware of the effect of the statute of limitation. The approach of the Mediation Bill can be contrasted with Section 43 of the Arbitration and Conciliation Act, 1996 which simply adopted the application of Limitation Act, allowing all participants to easily identify the effective date of application of the statute. Furthermore, it is easy for the disputing parties to identify the filing date of the arbitral proceedings because the arbitration programs are already institutionalized. In summary, this paper proposes that the Mediation Bill should contain a similar provision to Section 43 of Arbitration Act, to clarify the application of the Limitation Act. Also, we recommend that the Bill should mandate stronger professional ethics for private mediators, to ensure that all parties are clear about the application and impact of the Limitation Act.

APPlied stalking: What the next generation of stalking victims consider to be ‘stalking’ and why victims report their experiences to the police

Research suggests that offenders are finding new ways to exploit the conveniences of commonplace technologies for malicious purposes. What is less clear is whether targets of the malicious conduct consider emerging forms of technology-facilitated (TF) abuse as ‘stalking’ and whether such tactics influence decisions to involve the police. Using a national sample of emerging adults (ages 18–24) meeting legal criteria for stalking (N = 194), the current study investigated whether victim decision-making was related to pursuit type (physical only, TF-tactics only, both), the number of different tactics, and/or the frequency of specific pursuit behaviors. Net of victim and situational considerations, stalking acknowledgement was solely related to the frequency of being followed. Victims who were frequently followed were more likely to identify their experience as stalking while relentless threats and harassment via Snapchat produced a 12% decrease in the odds of acknowledgement. The findings suggest the need for adapting stalking prevention initiatives to discuss technology-facilitated pursuit tactics while increasing the capacity of first responders to identify and collect digital evidence.

Vulnerability Assessment for Applications Security Through Penetration Simulation and Testing

Cybersecurity threats and attacks are a critical concern for computing systems as general and specifically in web applications. There are many types and categories of cyberattacks on web applications. Many of these attacks are made possible due to existing vulnerabilities in the networking environments and platforms that host these web applications. So, the vulnerability assessment and attacks simulations on these networking platforms are of extreme importance to protect and secure the top web applications that play a prime role in our daily life. One of the widely used mechanisms to identify vulnerabilities and defend against different attacks on systems and networks is Penetration Testing. It allows us to simulate real-world attacks on a network or a single device to determine the susceptibility and impact of cybersecurity attacks. Pen testing aims to secure a system or network by performing a full-blown attack against it. Several techniques have been used for that, from port scanning, service, and operating system detection to network enumeration, creating specially crafted packets, and modifying software to exploit vulnerabilities. However, while it is used widely as a defensive technique, some attackers also employ it for malicious intentions utilizing available open-source penetration testing tools. Penetration testing on internal networks such as networks that connect IoT/sensors/web cameras, can be utilized to find vulnerabilities and fix them to secure the networks. In this research, we present a detailed discussion on penetration testing and its seven phases of action and provide a step-by-step procedure with instructions using various open-source tools to conduct penetration testing and vulnerability assessments of a network. We finally demonstrate the process and results of simulated attacks on our network within the testing environment. This research provides a comprehensive introduction to penetration testing and testbed through real-world attack simulation. The IT administrator or security enthusiast can utilize them to secure networks, devices, clients, servers, and applications while enhancing the overall organization’s security.

Стохастичний і динамічний підходи при моделюванні процесів поширення шкідливого програмного забезпечення автоматизованих енергетичних об'єктів та їх систем

The rapid development of information technologies in today's conditions makes it possible to control and automate processes and enterprises, institutions to ensure the correct and efficient operation of various energy systems. The mathematical apparatus used in the software for such objects and systems makes it possible to manage their states in various regular conditions. Sometimes unpredictable factors arise in the operation of energy facilities, which can lead to global catastrophes not only for a particular region, but also for all of humanity is the deliberate damage to the logic of the software that controls all the processes of the power system is one of these factors, for the purpose of terror or other malicious purposes. Such factors require the construction of models with which it is possible to predict the scale of risk and extent of damage, as well as to obtain a general estimate of the costs of protecting power system software against such malicious actions. An optimization mathematical model and a corresponding description of the implementation of a complex software tool for modeling the spread of malicious software (malware) in modern energy facilities and systems is the result of the work. The developed optimization mathematical model is based on the use of methods of optimization of functions and functionals with restrictions in the form of systems of ordinary differential equations with given corresponding initial conditions. To develop process simulation software modules based on the PSIDR mathematical model, stochastic population methods, models and algorithms were used to determine the control parameter at each time step. The use of such optimization methods and algorithms makes it possible to solve more complex tasks. It requires a procedure for predicting the spread of processes of various origins in general. The developed mathematical model consists in the minimization of costs for the purchase of antiviruses for the protection of relevant systems in energy facilities and systems. Keywords: malware, prediction, optimization, stochastic model, deterministic model, cellular automaton, energy objects

Pertimbangan Hakim Dalam Menjatuhkan Sanksi Tindak Pidana Pencurian Ringan Perspektif Hukum Pidana Islam

This article describes the judge's considerations in deciding the sanctions for minor theft from the perspective of Islamic Criminal Law. This type of research is normative. The legal materials of this research consist of primary and secondary legal materials in the form of Decision No: 8/Pid.C/TPR/2019/PN PSP, while secondary legal materials are in the form of books, journals and documents related to Islamic criminal law, data collection techniques with the case study method. In this study, the data analysis technique used is the legal hermeneutic analysis method. The results of this study indicate that in Islamic criminal law, theft can be referred to as ordinary theft when theft consists of four elements, namely the element of property taken secretly, the element of the item taken in the form of property, the element of the property being the property of another person and elements of malicious intent or against the law. While the decision No: 8/Pid.C/TPR/2019/PN PSP only consists of three elements that this crime is referred to as light theft, not ordinary theft. The punishment given is ta'zir punishment.

CNN Based Image Classification of Malicious UAVs

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads).