Research on Clustering Detection Method for Security Attack Behaviors Based on Air Traffic Control Network

Air traffic control systems play a critical role in ensuring the sustainable and resilient flow of air traffic. The air traffic sector serves as a fundamental topological unit and is responsible for overseeing and maintaining the system’s sustainable operation. Examining the structural characteristics of the air traffic sector network is a useful approach to gaining an intuitive understanding of the system’s sustainability and resilience. In this paper, an air traffic sector network (ATSN) was established in mainland China using the complex network theory, and its motif characteristics were analyzed from a microscopic perspective. Additionally, subgraph resilience was defined in order to describe the network topology by analyzing changes in subgraph motif concentration and subgraph residual concentration. Our empirical findings indicated that motifs exhibit high connectivity, while anti-motifs are found in subgraph structures with low connectivity. The motif concentration of subgraphs can efficiently reflect the distribution of heterogeneous subgraph structures within a network. During the process of resilience evaluation, the subgraph motif concentration remains relatively stable but is sensitive to the transition state of the network from disturbance to recovery. The resilience of the system at the macroscopic scale is aligned with the resilience of each heterogeneous subgraph structure to some extent. Topological indicators have a more significant impact on the resilience of the ATSN than air traffic flow characteristics. This study has the outcome of uncovering the preference for connection among nodes and the rationality of sector structure delineation in ATSNs. Additionally, this research addresses the fundamental mechanism behind the network disturbance recovery process, and identifies the connection between network macro- and microstructure in the resilience process.

Computer network infrastructures constitute the critical backbone of every socio-economic ICT system. Consequently, they are becoming increasingly mission-critical in our society since they provide always-on services for many everyday applications (e.g., Cloud Data Centres), safety-critical operations (e.g., Air Traffic Control networks), critical manufacturing services (e.g., Utility networks and Industrial Control Systems), and critical real-time services (e.g., Financial Trading Systems). The resilience and ability of such systems to remain operational in the face of threats is therefore paramount; this needs to be done by taking remedial action and intelligently reshaping their resources. At the same time, current communication architectures do not allow for such informed and adaptive provisioning. In this paper, we introduce the concepts, principles and current research activities related to a new Situation Aware Information Infrastructure (\(SAI^2\)) framework being developed for next generation ICT environments.

The network of air traffic service (ATS) routes is the main component of the modern air transportation system. Configuration of ATS routes is responsible for effective airspace usage. However, a dense network with higher order and big size will be difficult to support air traffic management. Therefore, in most cases during airspace design, it is important to search effective combination of peak air traffic flow and network configuration to distribute nodes load. In the paper, we study ATS routes configuration to support traffic between local airports. We consider ATS route network as an undirected graph. Airports are endpoints of local connections. Graph of runways is connected to the graph of ATS routes by standard schemes of departure, arrival, and approach. We use the shortest path between any two airports to estimate the importance of each waypoint. The excessive value of betweenness centrality for a particular node may indicate its overloading during air traffic simulation with equal flow for different directions. As an example, we use the Ukrainian ATS route network to estimate its configuration and the possibility to support local air traffic.

Air Traffic Flow Network Rerouting Problem ATFNRP, which aims to alleviate the flight delays caused by the increasing traffic and extreme weather, has become more and more serious in air traffic flow management. This paper proposes a multi-objective general rerouting model considering both total delay cost and airlines fairness and adopts Multi-Objective Evolutionary Algorithm based on Decomposition MOEA/D for ATNFRP. Empirical studies using the real data of China airspace demonstrate that MOEA/D outperforms or performs similarly to three well-acknowledged Multi-Objective Evolutionary Algorithms MOEAs on ATFNRP.

The evolution of many networked systems, such as air transportation, can be modeled using a combination of the network topology and the resultant dynamics. In particular, time-varying networks can be represented by switching between candidate topologies. This paper models such systems as discrete-time, positive Markov Jump Linear Systems. Time-varying, periodic Markovian transition matrices and continuous state resets during discrete-mode transitions are also incorporated. Two notions of stability are considered: Mean Stability and Almost-Sure Stability, and appropriate conditions are derived for both of them. The analysis techniques are demonstrated using models determined from operational air traffic delay data. The results show that air traffic delay networks satisfy the proposed conditions for both mean stability and almost-sure stability, implying that delays tend to decay over time, even though several of the component discrete modes are unstable. Different nodes (airports) are also evaluated in terms of the persistence of delays and their susceptibility to network effects.

Covid-19, which was defined as a result of research conducted in a group of patients developing respiratory symptoms in late December 2019 in Wuhan province of China, spread to other countries in a very short time by infecting from people to people. On January 30, 2020, the World Health Organization declared the “International Public Health Emergency” due to the Covid-19 pandemic. Travel restrictions have been imposed by countries to prevent the pandemic. With these restrictions, air traffic has come to a halt, only health, humanitarian, military, repatriation and cargo flights have been carried out. Due to the Covid-19, more than 6 million traffic losses occurred in the European air traffic network. There were 0.2 million flight losses in the 9/11 attacks and 0.8 million flight losses in the great financial crisis. It is not known how long the recovery will take to reach traffic data in 2019. In this study, the impact of Covid-19 on the European and Turkey air traffic networks in 2020 was analyzed compared to the 2019 data. Within the scope of forecasts published by aviation authorities, assessments regarding the recovery process are also included. It is predicted that the impact of the Covid-19 pandemic on the air traffic networks will not be compensated for a long time in line with current data and predictions for the pandemic.

With the rapid development of society, the traffic problem has become increasingly severe, and the traditional methods can no longer effectively solve the current social traffic behaviour problems. Although studies on the dynamics of human traffic behaviour based on traffic modes can effectively reveal the anomalies in traffic behaviour, few studies integrate intelligent traffic behaviour with multiple traffic modes. Based on the numerous traffic data of bike-sharing and ride-hailing in a Chinese city, this paper reveals the dynamic characteristics of various traffic behaviours in the city by combining spatiotemporal characteristics index and urban spatial structure with human traffic behaviour patterns. The experimental results show that the traffic behaviour of the town presents a double logarithmic power-law distribution in time characteristics, and there is a close interdependent dynamic relationship with the city’s spatial structure. The research in this paper can reveal the relationship between bimodal power-law distribution and spatial characteristics in complex systems and help solve social traffic problems effectively in social reality. Further research results can provide practical planning guidance for the behavioural integration of multiple traffic in smart cities.

To develop methods to model the relationship between cancer detection and recall rates to inform professional standards. Annual screening programme information for each of the 80 English NHSBSP units (totalling 11.3 million screening tests) for the seven screening years from 1 April 2009 to 31 March 2016 and some Dutch screening programme information were used to produce linear and non-linear models. The non-linear models estimated the modelled maximum values (MMV) for cancers detected at different grades and estimated how rapidly the MMV was reached (the modelled 'slope' (MS)). Main outcomes include the detection rate for combined invasive/micro-invasive and high-grade DCIS (IHG) detection rate and the low/intermediate grade DCIS (LIG) detection rate. At prevalent screens for IHG cancers, 99% of the MMV was reached at a recall rate of 7.0%. The LIG detection rate had no discernible plateau, increasing linearly at a rate of 0.12 per 1000 for every 1% increase in recall rate. At incident screens, 99% of the MMV for IHG cancer detection was 4.0%. LIG DCIS increased linearly at a rate of 0.18 per 1000 per 1% increase in recall rate. Our models demonstrate the diminishing returns associated with increasing recall rates. The screening programme in England could use the models to set recall rate ranges, and other countries could explore similar methodology. • Question: How can we determine optimum recall rates in breast cancer screening? • Findings: In this large observational study, we show that increases in recall rates above defined levels are almost exclusively associated with false positive recalls and a very small increase in low/intermediate grade DCIS. • Meaning: High recall rates are not associated with increases in detection of life-threatening cancers. The models developed in this paper can be used to help set recall rate ranges that maximise benefit and minimise harm.

ABSTRACTWork zones for road maintenance in traffic networks can significantly impact the traffic distribution and route choice behaviour of travellers. This study proposes an approach to evaluate and predict the broad‐scale effects of work zones on large‐scale traffic networks. For the requirement of the efficient evaluation of the various impacts of work zones on traffic networks, this study defines the road maintenance sensitivity factor (RMSF) to represent the joint impact of work zones. A simulation‐based optimization method for calibrating the RMSF is formulated. The original objective function is replaced by an analytical metamodel that builds the approximate relationship between the RMSFs and traffic flow distribution with the effect of work zones. A derivative‐free trust‐region algorithm is used to obtain the optimal solution. Numerical experiments are conducted on a small test network and a large‐scale freeway network. The proposed method shows the accuracy and effectiveness with tight computational resources than the simultaneous perturbation stochastic approximation method in both experiments, giving the RMSF results and map the traffic redistribution of large‐scale networks with work zones accurately and efficiently, which can help traffic managers to optimize maintenance plans and traffic management measures with the assistance of the traffic management system.

A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method is employed to generate detectors, some classes of new detectors are created. Thereafter, detectors are supposed to be deployed in real network environments in order to achieve detection with good results for false positives and detection rates. Since the traffic behavior is quite different according to the user’s network activities over available services, restrictions and applications, it is supposed that behavioral-based detectors are not well suited to all kind of networks. This paper presents the differences of detection results between some network scenarios by applying traditional detectors that were calculated with artificial neural networks. The same detector is deployed in different scenarios to measure the efficiency or inefficiency of static training detectors.

Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

The objective of this paper is to present a network-based optimization method for identifying links in an air traffic network responsible for carrying infected passengers into previously unexposed regions. The required data include individual infection reports (i.e., when the disease was first reported in a region), travel pattern data, and other geographic properties. The network structure is defined by nodes and links, which represent regions (cities, states, countries) and travel routes, respectively. The proposed methodology is novel in its attempt to replicate an outbreak pattern atop a transportation network by exploiting regional infection data. The problem parallels a related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection.

Demand uncertainty is one critical form of uncertainty which has an adverse effect on Air traffic flow management (ATFM). This is mainly due to the deviation in departure time and aircraft speed from their scheduled values. This may lead to the arrival of aircraft to certain routes at unscheduled times, causing an unexpected demand on those routes. The uncertainty of demand creates several difficulties in air transportation systems, including higher workloads for air traffic controllers, higher delays, travel costs, as well as safety risk. In this study, we propose a robust flight routing and scheduling scheme while considering both departure and speed uncertainty present in the air traffic network. Following robust optimization, we ensure that the capacity violations are eliminated from the system. The ATFM problem is formulated as a Mixed Integer Quadratic Programming (MIQP) problem with the objective of minimizing expected total delay of the system while maintaining required in-trail separation between aircraft even under uncertainty. In addition, we use an optimal flight level assignment method and speed assignment strategy to minimize the system delay and to fully utilize the system capacity. Furthermore, a greedy strategy with parallel computation is presented with the problem decomposed into a set of maximum independent sets to reduce the computational complexity in solving large-scale ATFM problems. With the experimental results, we demonstrate the effectiveness of the model.

The existence of aviation subsidies can exacerbate congestion in airports that operate near their maximum threshold. One of the main operational issues in airports, that is, aircraft taxiing operations, could become more difficult and, therefore, produce negative effects on airport capacity management. Air carrier economies, touristic mobility (leisure travel), and the environment could also be negatively affected. Greenhouse gas emissions, passenger travel time, and air carriers’ fuel costs, for example, might rise due to aircrafts’ increased taxiing time. This paper seeks to analyse the impact of regional aviation subsidies for regional hub airports in the Canary Island air traffic network; specifically, the effect it has on airports’ capacity in terms of LTO (landing and taking off) cycle delays and its repercussions for passenger travel time, air carrier economics, and the environment. Therefore, to quantify the extent that the increased traffic from subsidies cause airport ground operations to delay flights, the disturbance between aircraft categories in landing and taking off operations must be considered. The disturbance between aircrafts will be evaluated by employing a simple model of landing intervals considering two aircraft categories (i.e., ATRs-72, ATRs-42, and B737/A320 aircraft families). The above-mentioned methodology provides an approximation of the average processing rate for take-off using the ultimate capacity concept for a variety of aircrafts landing on a single runway of the airport. As a result, it must be noted that there is a potential economic and environmental impact associated with the implementation of the subsidy increase for residents’ interisland travel and from the Canary Islands to the mainland. There is also a negative effect on runways for both airports due to the capacity constraints. These impacts, however, have to be balanced with the social and economic benefits that each regional inhabitant derives from the subsidy in terms of improvement of their mobility for any purpose.

Drawing on the examples of the seaport of Rotterdam and the International Airport Schiphol, Anton Kreukels, Professor of Urban and Regional Planning at the University of Utrecht, points out three main issues concerning the relationship of ports and city-regions: (1) Strategic decisions affecting seaports as well as airports are no longer dominated by local actors but related to national and international levels. (2) Increasingly the hub-and-spoke-system, a well-known phenomenon in air traffic networks, is also manifest with regard to seaports. This results in a network with other—often lower ranking—seaports by feeder transport, and in a network with inland terminals cooperating with international seaports like Rotterdam and enabling efficient and reliable container transshipment. (3) Spatial planners have not yet adjusted their planning procedures to this reality of national- and international-based decision-making and hierarchical-organized logistics infrastructures.