Situation Awareness Model Research Articles

Application of machine learning algorithm and data evaluation in computer network security situation awareness technology

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Network virus propagation and security situation awareness based on Hidden Markov Model

The security situation of the network is determined by the observation state and hidden state of a complex virus system. To analyze the relationship between the two states and evaluate the trends in the security situation of the system, we construct a new model for security situation awareness based on Hidden Markov Model (HMM). Firstly, the paper examines the impact of virus propagation on network security and proposes a new SIR (Susceptible–Infected–Recovered) virus propagation model on scale-free networks. Secondly, we construct a Hidden Markov Model for network security, and this model designs and utilizes the Forward Algorithm and Viterbi Algorithm to assess the security status of epidemic networks. Finally, we ascertain the effectiveness of the model and algorithms through model examples and simulation experiments. The results show that the proposed algorithm is highly efficient in analyzing the security of virus systems and has great application value in predicting the security trends of networks.

Safety effect of tunnel environment self-explaining design based on situation awareness

The aim of this study was to investigate the safety effect of environment self-explaining design from drivers’ situation awareness. First, a situation awareness model was developed based on drivers’ situation awareness status and the schema proposed in this study. Subsequently, simulation experiment environments were developed using 3D Max software. A total of 30 driver volunteers were recruited to perform driving simulation experiments under different tunnel environment self-explaining design scenes. Notably, the information on drivers’ eye movement and driving behaviors was collected using eye trackers and a driving simulator. Last, the situation awareness level of drivers was estimated utilizing the situation awareness model. The results indicated that drivers’ situation awareness varied with tunnel environment self-explaining design scenes. The visual illusion marking colored pavement and light-colored decorated sidewall in tunnels provided greater self-explaining performance and significantly enhanced driver cognition of the driving environment. Moreover, the optimal outcomes were achieved, including the scheme of inclined slope pattern, longitudinal marking colored pavement, and blue-white decorated sidewall in tunnels. As revealed by the findings of this study, tunnel environment self-explaining design takes on a critical significance in improving tunnel driving environment and safety.

RL-HAT: A New Framework for Understanding Human-Agent Teaming

This paper presents a novel framework for human-agent teaming grounded in the principles of Reinforcement Learning (RL). Recognizing the need for a unified language across various disciplines, we utilize RL concepts to provide a standard for the understanding and evaluation of diverse teaming strategies. Our framework extends beyond traditional RL constructs, integrating aspects such as belief states, prior knowledge, social considerations, situational awareness, and mental models. A particular focus is placed on the role of ethics and trust in effective teaming. Additionally, we discuss how sensor data, perception models, and actuator modules can be incorporated, emphasizing the adaptability of our framework to a broad range of tasks and environments. We believe this work forms a substantial contribution to the field of human-agent teaming, establishing a solid foundation for future research and application.

How much situation awareness does the driver have when driving autonomously? A study based on driver attention allocation

During the operation of the L3 automated driving system, since there is no need to supervise the vehicle at all times, the driver is often disengaged from the driving task and engages in a variety of non-driving-related tasks (NDRTs). When the autonomous driving system (ADS) encounters an unexpected situation and issues a takeover request (TOR), whether the driver can recover the situation awareness (SA) in time is the key to ensure the safety of the takeover. In this study, the theory of attention resource allocation is introduced to more accurately model the dynamic process of the SA recovery. Moreover, the attention allocation model is further developed, and the affecting factors of attention allocation are quantified. An experiment with 90 participants using driving simulator was conducted for different road scenarios to verify the proposed SA model. The model proposed in this study can accurately predict the SA value of the driver under different road scenarios and the time required for the driver to recover to the maximum SA value, which provides a reference for the scientific design of dynamic takeover lead time. The results also show that as the radius of curvature of the road decreased, the level of SA recovery would become progressively worse.

Synthesized Framework of Decision Making for the Army Aviation Context

This paper presents the results of a project to articulate a theory–based framework for characterizing the decision making and situation awareness (SA) of Army Aviators. This was the first step toward developing strategies for evaluating the impact of emerging technologies on cognitive performance. The team conducted a literature review of decision making and SA models along with a review of Army Aviation cognitive requirements and identified key decision making concepts. The team synthesized a framework of decision making based on key concepts from the literature tailored to Army Aviation. The framework guided a literature review of evaluation methods and measures. Evaluation literature review findings included the use of scenario-based evaluation with a range of complexities, and multiple, complementary measures. The synthesized framework served as a useful tool to describe aviator decision making and how technology can influence decision making and SA, and may be useful beyond the Army Aviation context.

Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver’s situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects’ SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving.

Could Spatial Awareness Affect Situation Awareness: A Conceptual Examination.

The purpose of this study was to explore the relationship between situation awareness (SA) and the physical environment in patient rooms through building a conceptual model. Nurses work in very complicated and dynamic environments where having high levels of SA could be critical for their performance. Studies have also shown that nurses' awareness of the physical environment and patient room is a part of their awareness of dynamic situations in which nurses' spatial awareness may play a role in SA. Despite literature outlining the importance of SA, there is a lack of studies exploring the relationship between the two. A literature review was conducted for the study from nursing and psychology databases. Thirty-three articles, books, and dissertations from a scoping review were included for in-depth review. An in-depth review of the harvested literature indicated that there is in fact a relationship between these two phenomena. Founded on Endsley's model of SA, the literature review in this study offers a conceptual model that articulates a plausible causal pathway between the physical environment and SA. There are a lot of studies focusing on SA and various aspects of it related to nursing, but almost none mention the physical environment and its impact on SA. The current inquiry suggests that spatial awareness plays a prominent role in SA.

National and international maritime situational awareness model examples and the effects of North Stream Pipelines sabotage

While many countries and international organizations with maritime security interests and rights at sea have developed new security strategies or policies in the wake of the 9/11 terrorist attacks in the United States of America, they have accordingly changed or created new maritime security strategies or doctrines with appropriate Maritime Situational Awareness (MSA) models as well. Maritime deterioration, climate change, cyberattacks, serious and organized crime, epidemics, and state-made threats are just some of the new and growing concerns affecting maritime security. The sabotage of the Nord Stream gas pipelines in the Baltic Sea has given maritime security doctrines and frameworks a new dimension. In this article, the current maritime security approaches and maritime domain or situational awareness (MDA/MSA) model examples of some countries and international organizations from different geographic regions and also the ones that are located in the maritime choke point regions where global maritime trade routes are located and also the effects of the Nord Stream Pipelines sabotages on these are examined in light of the new threats and risks. The principle result reached in this study is that countries and international structures should have a cross governmental maritime security strategy, or at least a doctrine, in order to guide their own maritime situational awareness models and identify information sharing architectures. The most important result of the sabotages on Nord Stream Pipelines for MSA models in this study is that the fastest and most cost-effective method for protecting critical infrastructure under the seas is the concept of systems such as Mothership controlled autonomous and unmanned underwater vehicles, extra large unmanned undersea vehicles and Synthetic-aperture radar (SAR) satellites.

A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.

Repeatable effects of synchronizing perceptual tasks with heartbeat on perception-driven situation awareness

The paper presents repeatable effects of synchronizing visual and auditory alarms with heartbeats on the availability of cognitive resources. A perception-driven situation awareness model is proposed and studied by implementing two distinct experimental protocols with different groups of participants. Results of a first study with a single-screen configuration are repeated by those of a second one on a multiple-screen context. Both experimental protocols rely on manipulating a between-subjects factor to compare two conditions - one with alarms activated synchronously with heart rate and one with alarms non-synchronized with heart rate - and a within-subjects factor to compare the impact of workload by increasing the level of task difficulty. Results about mono-screen and multi-screen configurations are homogenous. The synchronous condition makes people produce significantly more errors and fewer visual scans of the alarm display area. This degradation of perceptual abilities is non-conscious and is correlated with workload. Main people are not aware about their actual performance and this is confirmed by the evolution of subjective performance and frustration regarding task difficulty, display configuration and alarm activation condition. Such discrepancies between what it is looked at with what it is actually perceived and between actual and perceived indicators like performance are perceptual dissonances that are relevant for perception-driven situation awareness. The application of synchronizing dynamic events with heartbeats will be studied for different individual and collective work contexts in order to extend the proposed perception-driven situation awareness model based on perceptual dissonance management and on human capability parameters.

UAV Swarm Cooperative Situation Awareness Theory Model Construction and Scenario Analysis

The Unmanned Aerial Vehicle (UAV) swarm collaborative operations is one of the disruptive technologies in future information warfare. Considering the characteristics of UAV swarm collaborative operation, we propose two UAV swarm cooperative situation awareness (SA) theory models based on the classical ones and the composition of UAV swarm. Firstly, the improved Endsley three-level SA model is proposed, in which the traditional mental model is changed into human machine intelligent model on the individual level. Then the homogeneous swarm and heterogeneous swarm cooperative SA model are separately built based on the improved team SA model and Distributed SA (DSA) model. Meanwhile, a comparison between the multi-UAV and UAV swarm is carried. Finally, a scenario analysis of swarm cooperative SA in combat situation is presented.

Designing augmented reality for future commercial aviation: a user-requirement analysis with commercial aviation pilots

Augmented reality (AR) capable head-mounted displays (HMDs) have been proposed as technological enablers of several complex future flight concepts, which will bring accompanying pilot situation awareness (SA) and operational safety enhancements. However, relevant aviation design guidance concerning the implementation of modern HMD technologies and AR symbology is sparse. Consequently, the current study describes an SA grounded user-requirements analysis of operational applications for HMD technologies and AR symbology, with the intention of providing inputs for future designs of commercial aviation systems. In addition, insights from the study are relevant for AR design more generally. Endsley’s three-level SA model (1988) was applied as a framework to focus group discussions with eleven aviation subject matter experts. Thematic analysis highlighted multiple operational scenarios where HMD technology and AR may enhance SA, along with the requirements of the technologies to provide these relevant advantages. In future, more detailed user-centred design recommendations should be sought for the specific applications identified within the current study.

Establishment of nonlinear network security situational awareness model based on random forest under the background of big data

Abstract In order to explore the establishment of a nonlinear network security situational awareness model based on random forest in the context of big data, a multi-level network security knowledge system evaluation model based on random forest is proposed. This article proposes a multi-level CSSA analysis system and then uses random memory algorithm to create a CSSA evaluation model. Also, it proposes a CSSA multi-level analysis framework and then uses random forest algorithm to build a CSSA evaluation model. A random vector distribution of the same values is used for all forest trees. In this article, the interval [0,1] is used to quantitatively describe the weight of the security level. The training sample ratio of test samples is 110:40, in order to predict the security of the network, the prediction of knowledge is closer to the true value, and the complexity of multi-level security is predicted. Use unusual forests. The tree returns the most recommended part, which is a more realistic assessment of network security. The experimental results show that considering the network security situation, the prediction performance of this method is closer to the actual value, and the performance is better than the other two methods. Therefore, perception of multi-level security situations can be effectively predicted using random access memory. It is proved that random forest is faster and more efficient in network security.

Exploration of a network security situational awareness model based on multisource data fusion

Exploration of a network security situational awareness model based on multisource data fusion

Research on the Optimization and Evaluation of Excavator Human-Machine Interface Layout Based on Situation Awareness

In order to solve the problem of a large number of types and complex layouts of control devices on the operator interface of mining excavators, and a large variety and number of control elements on the control panel, the layout optimization design is carried out by applying the theoretical model of situational awareness and the influencing factors to the layout design of the human-machine interface. The modularity of the interface and the hierarchy of the original components are divided, and the importance evaluation indicators established according to situational awareness are weighted according to the entropy weighting method, and then the results are combined with the VIKOR method to analyse the importance of the interface components and the control panel module respectively before the interface is optimised and designed. The results show that the application of situational awareness theory to HMI design can play an important role in enhancing the level of situational awareness of operators and meeting their needs.

Multi-Scale Traffic Aware Cybersecurity Situational Awareness Online Model for Intelligent Power Substation Communication Network

Substation communication network (SCN) provides real-time, high-speed, and reliable data transmissions for the advanced monitoring and control functionalities, which are facing increasing cyberspace threats and attacks. Efficient threat perception and cyber situational awareness are essential to enhance secure and reliable SCN operations. This article explores multiscale SCN traffic pattern characteristics with holistic network traffic, separated network traffic for included devices (especially IoT devices) and separated network traffic of certain types of protocol. The proposed online traffic-oriented SCN traffic anomaly detection and cyber situational awareness models are designed for the network anomalies and cyber-attacks that could cause network traffic pattern variations. We leverage a fractional autoregressive integration moving average (FARIMA)-based dynamic threshold model to detect abnormal traffic patterns without sophisticated computations or deep packet inspection. The SCN real-time operation conditions are timely quantified through the statistical methods with the alliance of SCN topology and protocols. The cyber situational awareness model is further carried out to evaluate the most affected protocol and security risks of various devices in SCN using Grubbs’ test. The experiment results are carried out based on a real 110-kV intelligent power substation. The numerical results confirm the comparative low mean square error (MSE) and low complexity of the online traffic characterization when forecasting holistic network traffic and separated network traffics. Furthermore, the timely and quantified cybersecurity risk analysis is conducted based on the SCN traffic with varying scales to detect cyberspace threats and identify the high-risk SCN devices and the most affected protocol.

Digital information channels during the COVID-19 global health emergency through a situational awareness lens: a study of Pakistani citizens

Purpose The main aim of this study was to know the role of digital information and communication channels in developing citizens’ understanding regarding COVID-19 with reference to situation awareness. Furthermore, the impact of gender, age, qualification and area of respondents on citizens' perception and comprehension of COVID-19 was also investigated. Design/methodology/approach This quantitative study used an online survey and received 377 usable responses. The instrument was developed with the help of literature, and it was based on two constructs (perception of the element and comprehension of the current situation) of the situational awareness (SA) model. Data were collected from Pakistani citizens through a convenient sampling technique. Findings It was observed that the majority of citizens used electronic media, Facebook and WhatsApp for accessing COVID-19-related information. World Health Organization website played an important role in developing citizens’ understanding. Alert messages through mobile phone and apps played role in developing citizen’s understanding regarding COVID-19 situation. They perceived that media was effective in providing social distancing techniques. Moreover, they believed that government campaigns were helpful. Findings revealed that there was no significant impact of gender, age, qualification and area on citizens' perceptions and comprehension of COVID-19 through media. Originality/value This study is helpful for authorities in decision-making regarding COVID-19 and also filled the literature gap as, to the best of the authors’ knowledge, no comprehensive study has been conducted regarding the SA of COVID-19.

DROID: Driver-Centric Risk Object IDentification.

Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition. In this work, we approach the problem from the perspective of subjective risk. We operationalize subjective risk assessment by predicting driver behavior changes and identifying the cause of changes. To this end, we introduce a new task called driver-centric risk object identification (DROID), which uses egocentric video to identify object(s) influencing a driver's behavior, given only the driver's response as the supervision signal. We formulate the task as a cause-effect problem and present a novel two-stage DROID framework, taking inspiration from models of situation awareness and causal inference. A subset of data constructed from the Honda Research Institute Driving Dataset (HDD) is used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even compared with strong baseline models using this dataset. Additionally, we conduct extensive ablative studies to justify our design choices. Moreover, we demonstrate the applicability of DROID for risk assessment.

Data De-identification Framework

As technology develops, the amount of information being used has increased a lot. Every company learns big data to provide customized services with its customers. Accordingly, collecting and analyzing data of the data subject has become one of the core competencies of the companies. However, when collecting and using it, the authority of the data subject may be violated. The data often identifies its subject by itself, and even if it is not a personal information that infringes on an individual’s authority, the moment it is connected, it becomes important and sensitive personal information that we have never thought of. Therefore, recent privacy regulations such as GDPR (General Data Protection Regulation) are changing to guarantee more rights of the data subjects. To use data effectively without infringing on the rights of the data subject, the concept of de-identification has been created. Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research. De-identification/pseudonymization techniques have been studied a lot, but it is difficult for companies and researchers to know how to de-identify/pseudonymize data. It is difficult to clearly understand how and to what extent each organization should take de-identification measures. Currently, each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country. We solved this problem from the perspective of risk management. Several steps are required to secure the dataset starting from pre-processing to releasing the dataset. We can analyze the dataset, analyze the risk, evaluate the risk, and treat the risk appropriately. The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk. Then, we can release the dataset under its own purpose. These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/IEC (International Organization for Standardization/International Electrotechnical Commission) 20889, NIST IR (National Institute of Standards and Technology Interagency Reports) 8053, NIST SP (National Institute of Standards and Technology Special Publications) 800-188, and ITU-T (International Telecommunications Union-Telecommunication) X.1148. We propose an integrated framework based on situational awareness model and risk management model. We found that this framework can be specialized for multiple domains, and it is useful because it is based on a variety of case and utility-based ROI calculations.