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Present network monitoring systems need to cope with the ever-increasing amount of traffic in modern high-speed networks. These systems often perform sophisticated deep packet inspection (DPI) for anomaly detection, denial-of-service attacks detection and mitigation, intrusion detection and prevention, etc. Since DPI is resource-intensive, the monitoring devices are often not able to analyze all incoming traffic at link speeds. Consequently, sampling is employed to reduce the traffic volume and thus limit packet losses caused by resource exhaustion. Classical sampling methods select packets based on a fixed limiting parameter, regardless of the computational resource utilization of the monitoring device. This paper proposes a novel sampling approach for network traffic security monitoring that is based on an analytical model of the monitoring device. The model allows for testing adaptive sampling strategies that adjust the instantaneous sampling rate according to the input queue occupancy. The queue occupancy is used to drive the adaptation as it indicates the current relationship between available computational resources and the input traffic volume. Consequently, our approach maximizes the DPI ratio while simultaneously ensuring that the probability of packet loss due to resource exhaustion remains negligible. Analytical and simulation results are presented to demonstrate the impact of the proposed method on system parameters, along with a comparative studies.

In today’s digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study.

In today's digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study. [ABSTRACT FROM AUTHOR]

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This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner's dilemma, covert channels, qualitative metrics, and existing steganography techniques in computer communications. An architecture was developed to assess the effectiveness of this solution, and experiments were conducted, with their results presented. This contribution leverages established steganographic principles and seamlessly integrates with widely adopted IPsec protocols, offering a solution to improve covert communication within computer networks. [ABSTRACT FROM AUTHOR]

In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flows and which telemetry data are collected, the network-wide visibility and the performance of monitoring applications may decrease. In this paper, we present the in-band network telemetry problem and extend the existing mathematical optimization models of the problem by proposing a new model that computes the routing of network flows. Results show that the new model outperforms existing models in term of network coverage and monitoring applications performance. The results of this work can be useful for network managers and enterprises to gain real-time insights into network performance. [ABSTRACT FROM AUTHOR]

The work presents the possibility to use Kali Linux in the process of power electronic devices research, which has not been applied before. Several of the built-in tools of Kali Linux have been used for the purpose of the research. Nmap has been used for vulnerability testing - scanning for open ports and finding out specific, well-known security vulnerabilities by using specific scripts. Wireshark and Burp Suite have been used to find out if the exchanged information is secure or not. hping3 has been used to scan for open ports and generating different TCP DoS attacks, thus studying what is the response of the power electronic device when it is subjected to different TCP DoS attacks - can it be accessed or not, has it experienced any performance violation, etc. Based on the results of this research, it can be argued that Kali Linux is applicable and can be used during the process of power electronic device research. [ABSTRACT FROM AUTHOR]

Middle-high level managers of operators of telecommunications have to follow many key parameters to increase the profitability along with strategy of company. Therefore, easily readable data are being provided to managers by experts. With large screens in rooms, these data are monitored momentarily and ensured to take necessary actions without creating customer dissatisfaction. Especially for operators, it is one of the key parameters to manage the network in a quality and healthy way without creating complaints. It is important to monitor network health with appointed criteria and methods. In this context, there have been weights of province-based network health by processing geolocation-based big data and using the VIKOR method. Thus, there will have been presented the network monitoring opportunity from instant real user information to managers. Although there is a lot of criteria and methods for network monitoring, it will have been one of the priority studies that will enable the analysis of geo location data with a MCDM method in this area.

In-band network telemetry (INT) is one of the promising technologies for achieving fine-grained and real-time visibility into the network, however, it brings non-negligible traffic overhead due to the increased packet length. Several sampling methods have been introduced to reduce the INT overhead; although, the provision of satisfactory network visibility was not well investigated. In this paper, we introduce a utility-based INT sampling rate allocation scheme, namely uINT, that aims to reduce data redundancy while improving network visibility. We conducted experiments using the software switches and demonstrated that uINT reduces redundantly collected INT data by up to 15% compared to sINT.

Network monitoring is essential for IT infrastructure health, enabling proactive threat detection, bandwidth optimization, and data analysis. Federated Learning facilitates collaboration and expands threat detection capabilities by allowing multiple clients to train models while preserving privacy and security without compromising sensitive information. This work explores the application of Federated Learning for secure network monitoring by investigating its use in various data partitioning settings to train Deep Learning models across multiple data partitions, incorporating secure aggregation to enhance data privacy. We propose an approach for multiparty training of deep neural networks for time series-based network load forecasting with secure aggregation. Our approach shows that a collaboratively trained model, on horizontal partitions, performs 11%-14% better than model training only on a single partition of that type. The model trained on vertical partitions achieved performance comparable to that of the models trained on a complete data set. Finally, examining the proposed approach on horizontal and vertical partitions proved its viability in both aggregation settings, regular and secure. These contributions demonstrate the feasibility of Federated Learning to improve interoperability between multiple organizations while addressing privacy and security concerns.

Network monitoring is essential for operators to review and optimize network behaviour, and to troubleshoot any issues that arise. With the adoption of unlicensed band and spectrum-sharing technologies, conventional optimization techniques are no longer feasible. As cellular networks grow in complexity, data-driven solutions are becoming increasingly central to ensuring optimal network performance, reliability, and user experience. Recent cellular research has focused on data-driven analysis and optimization to ensure high Quality of Service in a cellular network. However, the biggest challenge for such work is data collection at scale from sophisticated and reliable network monitoring tools. This work bridges a gap in cellular network literature with a thorough review of cellular network monitoring tools. We rigorously test and review eleven applications that are popularly used to collect information on cellular networks. We understand, analyze, and critique their architecture to assess their reliability. We share insights on their performance and ease of use. Most importantly, we analyze the ability of applications to collect accurate cellular network data at scale. We also review recent literature on cellular network monitoring and analysis after carefully selecting 32 papers relevant to the topic.

Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as fast as possible. As this is getting more complex in modern enterprise networks, Artificial Intelligence (AI) and Machine Learning (ML) have gained substantial popularity in research. However, their adoption into real-world IDS solutions remains poor. Academic research often overlooks the interconnection of users and technical aspects. This leads to less explainable AI/ML models that hinder trust among AI/ML non-experts. Additionally, research often neglects secondary concerns such as usability and privacy. If IDS approaches conflict with current regulations or if administrators cannot deal with attacks more effectively, enterprises will not adopt the IDS in practice. To identify those problems systematically, our literature survey takes a user-centric approach; we examine IDS research from the perspective of stakeholders by applying the concept of personas. Further, we investigate multiple factors limiting the adoption of AI/ML in security and suggest technical, non-technical, and user-related considerations to enhance the adoption in practice. Our key contributions are threefold. (i) We derive personas from realistic enterprise scenarios, (ii) we provide a set of relevant hypotheses in the form of a review template, and (iii), based on our reviews, we derive design guidelines for practical implementations. To the best of our knowledge, this is the first paper that analyzes practical adoption barriers of AI/ML-based intrusion detection solutions concerning appropriateness of data, reproducibility, explainability, practicability, usability, and privacy. Our guidelines may help researchers to holistically evaluate their AI/ML-based IDS approaches to increase practical adoption.

The increasing need for high-precision monitoring and network-wide observability has spurred significant interest in In-Band Network Telemetry (INT), which empowers the collection of monitoring data directly from the dataplane, thus, alleviating the various shortcomings of passive monitoring techniques. INT, in some of its initial forms (e.g., P4-INT), tends to accumulate per-hop telemetry values within each packet, introducing substantial transmission overhead. Recent efforts to alleviate this shortcoming spread per-hop telemetry values among multiple packets, but eventually cause other implications, such as INT data redundancy or collisions during state lookup, stemming from the probabilistic data structures that they employ. Along these lines, we stress on the need for a lightweight INT approach with deterministic behavior. To this end, we present a new INT approach (Controller-Assisted Lightweight In-Band Network Telemetry – CLINT) that relies on a telemetry controller for the coordination of INT nodes, through the update of telemetry states, which indicate at each instance the telemetry action that should be applied to each incoming packet. We discuss in detail the operation of CLINT, as well as its prototype implementation using P4 BMv2 and the P4Runtime API. Our evaluation results indicate that CLINT outperforms two state-of-the-art lightweight INT techniques (i.e., PINT and DLINT) in terms of monitoring efficiency and network update detection, while maintaining an insignificant control communication overhead.

Efficient real-time top-k flows measurement plays a pivotal role in enhancing both network performance and security, including tasks such as timely traffic scheduling, optimizing network latency and identifying potential security threats. However, traditional methods for detecting top-k flows suffer from decreased accuracy and high memory overhead. Furthermore, many existing methods overlook finer-grained measurements, such as the detection within the latest short time intervals. With the increasing expansion scale and link speed of the network, an accurate real-time top-k flows identified method is required. This paper proposes wSketch, a novel sketch-based method for real-time top-k flows detection. The innovations of wSketch are that it combines with the sliding window model and circular queue model, and introduces a novel probabilistic update solution. The probabilistic update mechanism gives the larger flow a greater chance of retention, the sliding window model focuses on the latest flow in the last $\mathbb {W}$ time units, and the circular queue reduces memory consumption. Therefore, wSketch provides insights into the current network situation and does well in anticipating future trends. The experimental results showcase wSketch’s superior performance, achieving over 96% accuracy with a small memory size of 20KB.

The importance of stored streaming video for current Internet traffic is undeniable, even in the context of satellite communications (SATCOM). Therefore, Internet service providers aim to deliver the highest quality of experience to their end users, although they are not able to assess it directly. Some machine learning techniques proposed in the literature have demonstrated their ability to predict the quality of experience based on traffic data analysis. However, these models cannot be directly applied in a SATCOM context without considering the specific characteristics of satellite links. Furthermore, some of them may not be suitable for real-time use. In this study, we monitored over 2,400 YouTube video sessions over an emulated satellite network to develop models capable of predicting the initial delay, played resolution, and stalling events. The collected dataset is available as an open source to the research community. The primary objective of this research is to provide a functional model for real-time applications. To achieve this, we reduced the required feature set to minimize computation time and resources, enabling a practical real-time implementation of the model while assessing its feasibility. We show that we successfully achieved a substantial reduction in the number of features while also observing a relative improvement in prediction. Our results yield prediction performance comparable to that of other studies on terrestrial networks. Using the reduced feature set, we present a real-time implementation and confirm the real-time viability of our work.

This work demonstrates the feasibility of using Kali Linux in the process of power electronic device research. The novelty in this work is the use of Kali Linux in the process of power electronic device research. This operating system is mainly used for the penetration testing of various communication devices but not for power electronic device research. The aim of this work is to study the level of network security (the type of security vulnerabilities that a power electronic device has) and whether the data exchange between the power electronic device and the monitoring and control center is secure. Additionally, penetration testing has been carried out. Kali Linux was used to implement these tasks. Penetration testing was performed to verify how the studied power electronic device reacted to various TCP DoS attacks—could it be accessed, was it blocked, etc. Kali Linux and some of the tools built into the operating system—Nmap, hping3, Wireshark, Burp Suite Community Edition—were used for this study. During the penetration tests, a characterization of the traffic being processed/generated by the studied power electronic device was carried out to evaluate and analyze what impact each TCP DoS attack had on the device's performance. In order to conduct the study, an experimental setup was designed. This experimental network was not connected to other networks, so the cyber attacks were controlled and confined within the experimental network. The research carried out validated the use of Kali Linux for the study of power electronic devices. From the obtained results, it is found that the studied power electronic device provides a certain level of network security, but the data exchange is insecure. [ABSTRACT FROM AUTHOR]

Safe and reliable data transport over optical networks is essential for a high-speed Internet. Optical fibres are the backbone of the Internet, allowing billions of people around the world to connect and exchange data. However, these cables can be compromised by both deliberate attacks and accidental damage. The disruption caused by these abnormalities may result in significant monetary and data losses, undermine the security of optical networks by enabling unauthorised parties to view the sent data, or progressively lower network performance. Based on an examination of data from optical networks, this research proposes an original strategy for hybrid machine learning-based network monitoring and security improvement. Here, an anomaly detection method called convolutional principal component network is utilised to keep an eye on the optical infrastructure. Then, we apply hybrid cloud vector Bayesian graph networks to fortify the system. Anomaly detection rate, accuracy, precision rate, and quality of service are all experimentally evaluated across a variety of security analysis datasets. We validate our research using data from operational networks and simulated data created in a realistic-looking simulator. The findings verify the efficacy of our approach for optimising routing decisions in real time and designing networks for the future. [ABSTRACT FROM AUTHOR]

With the application of public and private network technology in heavy haul freight vehicle-ground communication lines, monitoring the public and private networks of heavy haul railways has become an important technical means to ensure network security and stable operation. This paper briefly discusses the LTE public and private network architectures currently applied in heavy haul freight lines, and discusses the structure, functions, interfaces, and key technologies of the public and private network monitoring system. The research focuses on monitoring technology for the entire chain, including technology for the onboard side, the ground side, and even the center side, and ultimately provides prospects for the future promotion of heavy haul railway public and private network monitoring systems. [ABSTRACT FROM AUTHOR]

To strengthen networks’ security performance, here pose suggests a fused full convolutional approach to monitoring computer networks. This paper first analyzes the various performances of the full convolutional model for error problems rate, error reporting, and monitoring effects on various attack categories then proposes a network monitoring scheme for the full convolutional model and introduces the workflow of the full convolutional model in computer network security protection. In terms of accuracy, the full convolutional error rate of the blueprint meets the requirements rate of 96.8%, which is better than the classical network models of Lenet-5 and AlexNet, with 86.2% and 91.6%. The false alarm rate is only 2.37%, which is lower than the 5.74% MLP algorithm and 4.23% SVM algorithm. By comparison, the full convolutional calculation method is more efficient than other calculation methods in the detection rate of attack types such as Dos, Probe, U2R, and R2L. Therefore, the calculation method here is well adapted to computer network security protection requirements.

With the ever-increasing complexity and scale of optical networks, efficient network monitoring and robust security analysis have become paramount. In this study, we propose a novel approach that combines optical network modeling with data analytics, leveraging the power of hybrid computing models. Aim of this research is to propose novel technique in optical network modelling based on data analysis in network monitoring with security enhancement using hybrid computing model with deep learning techniques. Here the optical network monitoring and anomaly detection is carried out using fuzzy density clustering based markov K-means attention recurrent neural network (FDCM-KARNN). By continuously monitoring network performance and traffic patterns, our system can proactively detect and respond to network aberrations, minimizing downtime and mitigating potential risks. Then by hybrid computing based on edge and cloud network the vast amounts of network data and provide accurate predictions has been enabled. The experimental analysis is carried out for various monitored optical network dataset based on network security analysis and data analysis in terms of training accuracy, throughput, packet delivery ratio, data integrity, precision. The proposed technique attained training accuracy of 99%, precision of 98%, packet delivery ratio of 97%, data integrity of 85%, throughput of 96%. [ABSTRACT FROM AUTHOR]

In-band Network Telemetry (INT) is a framework that collects and reports network status and can be used in SDN dataplanes such as P4. On the INT, a network switch adds monitoring information for network monitoring in the packet header and transmits it. This behavior has a problem in that monitoring data transmission overhead occurs as the number of transmission hops and packets increases. To solve this problem, in this paper proposes an algorithm that reduces transmission overhead based on the start, change, end etc. events of a transport flow. In addition, the proposed algorithm can trace the data transmission path originally provided by INT even if the path changes of the flows. In the simulation, it can be confirmed that the monitoring transmission overhead is reduced, and it is possible to trace the data transmission path according to the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner’s dilemma, covert channels, qualitative metrics, and existing steganography techniques in computer communications. An architecture was developed to assess the effectiveness of this solution, and experiments were conducted, with their results presented. This contribution leverages established steganographic principles and seamlessly integrates with widely adopted IPsec protocols, offering a solution to improve covert communication within computer networks.

The data described herein pertains to the Robotic Systems Security domain. This data in brief presents the attributes of the ROSIDS23 dataset and its collection process in detail. This dataset comprises Robot Operating System (ROS)-based cyber-attacks to address the emerging need in high fidelity data for robotic system security research. The data was gathered from the IFARLab-DIH environment. IFARLab-DIH is a robotic and factory-level laboratory that includes a ROS-based network and is used to conduct studies up to TRL 5 on robotic systems. ROSIDS23 dataset contains benign and various attack traffic collected from the ROS middleware using the tcpdump network protocol analyser. Then the eighty-two traffic features were extracted from the captured pcap files and converted into CSV format using the CICFlowMeter tool. This dataset can serve as a valuable resource for developing and improving security countermeasures in robotic systems and can help the evolution of resilient robotics infrastructure.

One of the essential components in LoRaWAN is managing the server with all existing components, including Device Management, Data forwarding, Network security, and real-time network monitoring. It is necessary to manage the uplink and downlink of the LoRaWAN server and maintain the quality of data transmission from the LoRa end node to the LoRaWAN server. Depending on the service provider, the LoRaWAN Server provides space for payload, uplink, and downlink data. How many times in a day, week, month, or even year? This discussion depends on the LoRaWAN server provider. Some are open sources, and some provide free uplink and downlink services. Settings on the LoRaWAN Server will determine how much data will enter the server or the network size. Security is also an essential parameter, e.g., encryption or authentication, supported by the feature requirements used on the server, which will determine the type of communication that LoRa will apply, for example, multi-communication or analysis data. Centralized management, Security, scalability, and cost-effectiveness are essential parameters if LoRaWAN is managed well. [ABSTRACT FROM AUTHOR]

Deep learning is a growing technique used to solve complex artificial intelligence (AI) problems. Large-scale deep learning has become a significant issue as a result of the expansion of datasets and the complexity of deep learning models. For training large-scale models, the cloud can be used as a distributed HPC (high-performance computing) tool with benefits in cost and flexibility. However, one of the major performance barriers in distributed deep learning in a distributed HPC environment is the network. The performance is often limited by heavy traffic like many stochastic gradient descent transfers for distributed communication. There are many network studies in distributed deep learning to solve these problems, but most research only focuses on improving communication performance and applying new methods or algorithms like overlapping parameter synchronization to minimize communication delay rather than considering the actual network. In this paper, we are focusing on the actual network, especially in a distributed HPC environment. In such an environment, if cluster nodes are assigned to different zones/regions which means a set of an appropriate number of distributed HPC nodes when performing distributed deep learning tasks, performance degradation due to network delay may occur. The proposed network optimization algorithm ensures that distributed work is placed in the same zone as much as possible to reduce network delay. Furthermore, scoring using network monitoring tools like loss, delay, and throughput is applied to select the optimal node within the zone. Our proposal has been validated on the Kubernetes platform, an open source orchestrator for the automatic management and deployment of micro-services. The performance of distributed deep learning is improved through the proposed scheduler. [ABSTRACT FROM AUTHOR]

The detection of recurring behavioral patterns in time series data, also called motif discovery, is a crucial step for mining insights in complex time series data, especially in complex environments where manual monitoring is not feasible. However, current state-of-the-art algorithms fall short in their applicability in production environments (due to static motif length, lots of user defined parameters, only providing the best motif pair, etc.). In this paper, a variable length motif discovery method is proposed based on the Matrix Profile which focuses on industrial applicability. It works in noisy and periodic environments, returns only unique motifs (meaning same shape motifs are grouped together as one) and only requires one distance matrix calculation. The method was benchmarked on synthetic data as well as publicly available real world key performance indicator (KPI) data from telecom providers and shows adequate accuracy in finding both short and long motifs in the same time series.

The aim of DNS over HTTPS (DoH) is to enhance users’ privacy by encrypting DNS. However, it also enables adversaries to bypass security mechanisms that rely on inspecting unencrypted DNS. Therefore in some networks, it is crucial to detect and block DoH to maintain security. Unfortunately, DoH is particularly challenging to detect, because it is designed to blend into regular HTTPS traffic. So far, there have been numerous proposals for DoH detection; however, they rely on specialized flow monitoring software that can export complex features that cannot be often computed on the running sequence or suffer from low accuracy. These properties significantly limit their mass deployment into real-world environments. Therefore this study proposes a novel DoH detector that uses IP-based, machine learning, and active probing techniques to detect DoH effectively with standard flow monitoring software. The use of classical flow features also enables its deployment in any network infrastructure with flow-monitoring appliances such as intelligent switches, firewalls, or routers. The proposed approach was tested using lab-created and real-world ISP-based network data and achieved a high classification accuracy of 0.999 and an F1 score of 0.998 with no false positives.

The need for monitoring the state of a traffic network versus the costly installation and maintenance of roadside sensors constitutes the tough sensor placement problem in designing transportation networks. Placement problems naturally lie in the category of subset selection problems, which are known to be inherently combinatorial, and therefore, finding their exact solution is intractable for large problems. Due to this intractability, numerous heuristics have been proposed in the literature for approximately solving placement problems for traffic networks. Among these approaches, it has been observed that greedy algorithms normally outperform other heuristics. In this paper, we show the mathematics of why greedy algorithms are appropriate proxies for solving these subset selection problems; similar to placement problems for linear systems, placement problems for traffic networks also normally have a submodular structure. In this work, we analyze the problem of road sensor placement for transportation networks under different information structures available: when no vehicle routing information is available, when vehicles' routings are known, and when it is necessary to maximize the number of origin–destination (O/D) traffic flows that are monitored with a set of sensors. We show that in all these cases, the placement problem has a submodular monotone structure. It is well known that the submodularity and monotonicity of discrete optimization problems can be leveraged to derive greedy algorithms that approximate the optimal solution. Consequently, our result is of great practical importance since by exploiting the submodularity and monotonicity of a problem, we show that it is possible to use polynomial-time greedy algorithms to approximate the combinatorial optimization problem with guaranteed optimality bounds for large problems, which are intractable to solve otherwise. Our results shed light upon the success of heuristic greedy algorithms that have been developed in some of the literature for solving placement problems at scale. To demonstrate the applicability of submodular optimization for solving placement problems, we first compare the performance of our polynomial-time approximation algorithm with the true optimum in an example traffic network which is small enough for finding the exact optimal solution with enumerating all possible subsets. Then, we investigate and validate our submodular approach in a case study involving a large-scale traffic network in Berkeley, California, where finding the exact optimal solution is intractable. Submodularity of the placement problem in these scenarios provides a powerful computational tool which can be further extended to other placement problem formulations that can become a reference for solving similar problems in the transportation literature. • Sensor placement problems on large scale networks are intractable. • Three different sensor placement problems are shown to share the submodular feature. • Submodular optimization problems have efficient solutions. • The submodular optimization algorithm is validated on both small and large networks. [ABSTRACT FROM AUTHOR]