Perimeter Defense Research Articles

Non-linear programming model proposal for mutual support distance of air defence systems

With the advancements in technology, the deployment of air defence systems required for the defence of a country after procurement is considered as an important problem in terms of defence effectiveness. It is seen that criteria such as coverage, strike effectiveness and logistics network are taken into account while deploying. In addition, principles such as remote countermeasures, defence in depth and all-round defence are also taken into account. In this study, two different deployment models are proposed for point and area air defence. In this model, the optimum deployment distance at which other systems can support each other in addition to a deployed air defence system is determined. In the nonlinear target programming model proposed for point air defence, the number of missiles is considered to be reasonably balanced for perimeter defence, while for area defence, the nonlinear optimization problem is addressed by calculating the equally important coverage and strike effectiveness criteria values. In this study, it is observed that the approach based on mutual support distance has been used for the first time in the defence literature. According to the results obtained, it is observed that this proposed model for determining the mutual support distance solves the deployment problem optimally.

Research on fiber OTDR signal recognition and classification based on the ViT model

In the field of event recognition for phase-sensitive optical time-domain reflectometry (-OTDR), convolutional neural networks (CNNs) have been the mainstream tool. However, Transformer models, with their self-attention mechanism, have provided a new perspective for image recognition tasks. This paper proposes a -OTDR event recognition method based on the Transformer model and experimentally demonstrates its significant advantages over CNNs. The method utilizes an attention-based feature extraction approach to recognize distributed fiber optic sensing signals in a phase-sensitive optical time-domain reflectometer (-OTDR). In perimeter defense applications, various interferences and noise severely affect the recognition of -OTDR sensing signals, leading to false alarms, making accurate signal recognition both challenging and necessary. Extracting signal features and using machine learning classification models to recognize signals has been a research hotspot in recent years. The effectiveness of feature extraction is crucial to classification performance. This project introduces a temporal-spatial dual attention mechanism into the feature extraction of fiber optic sensing signals to improve the accuracy and robustness of signal recognition.

Radar Based Security System Alert

Abstract: Radar-based security systems are now a reliable option for both intruder detection and perimeter defence. This research presents a unique approach to integrate state-of-the-art signal processing methods to improve the performance of such systems. The suggested system tracks and detects moving objects inside an established region using radar technology. The system utilizes advanced processing of signals methods, such as fuzzy logic, neural networks, and wavelet analysis, to differentiate between everyday activities and possible security risks. The network of radar sensors that are placed strategically around the perimeter of the secured region makes up the radar-based security system. Every radar sensor keeps an eye on the environment and detects any moving objects that come within range. Wavelet analysis is used to analyze the collected signals in real-time and extract important properties, such as object velocity, direction, and size.

The role of heterogeneity in autonomous perimeter defense problems

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.

The Impact of Network Design Interventions on the Security of Interdependent Systems

We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an orthogonal set of defense strategies in the form of network design interventions in the CPS to protect it against the attacker. We first propose an algorithm to simplify the CPS attack graph to an equivalent form which reduces the computational requirements for characterizing the defender's optimal security investments. We then evaluate four types of design interventions in the network in the form of adding nodes in the attack graph, interpreted as introducing additional safeguards, introducing structural redundancies, introducing functional redundancies, and introducing new functionalities. We identify scenarios in which interventions that strengthen internal components of the CPS may be more beneficial than traditional approaches such as perimeter defense. We showcase our proposed approach in two practical use cases: a remote attack on an industrial CPS and a remote attack on an automotive system. We highlight how our results closely match recommendations made by security organizations and discuss the implications of our findings for CPS design.

Randomized Competitive Perimeter Defense on a Line

Randomized Competitive Perimeter Defense on a Line

Multivehicle Perimeter Defense in Conical Environments

Multivehicle Perimeter Defense in Conical Environments

An intelligent zero trust secure framework for software defined networking

Software-defined networking (SDN) faces many of the same security threats as traditional networks. The separation of the SDN control plane and data plane makes the controller more vulnerable to cyber attacks. The conventional “perimeter defense” network security model cannot prevent lateral movement attacks caused by malicious insider users or hardware and software vulnerabilities. The “zero trust architecture” has become a new security network model to protect enterprise network security. In this article, we propose an intelligent zero-trust security framework IZTSDN for the software-defined networking by integrating deep learning and zero-trust architecture, which adopts zero-trust architecture to protect every resource and network connection in the network. IZTSDN uses a traffic anomaly detection mode CALSeq2Seql based on a deep learning algorithm to analyze users’ network behavior in real-time and achieve continuous tracking and analysis of users, restrict malicious users from accessing network resources, and realize the dynamic authorization process. Finally, the Mininet simulation platform is extended to build the simulation platform MiniIZTA supporting zero-trust architecture and the proposed security framework IZTSDN is experimentally analyzed. The experimental results show that the IZTSDN security framework can provide about 80.5% of throughput when the network is attacked. The accuracy of abnormal traffic detection reaches 99.56% on the SDN dataset, which verifies that the reliability and availability of the IZTSDN security framework are verified.

Doomed to Repeat with IPv6? Characterization of NAT-centric Security in SOHO Routers

With the transition to IPv6, addressing constraints that necessitated a common security architecture under network address translation (NAT) are no longer present. Instead, manufacturers are now able to choose between an open model design, where devices are end-to-end reachable, or a more familiar closed model, where the home gateway may continue to serve as a perimeter security device. The potential for further nuance, such as differences in default access control policies, filtering behaviors, and IPv6 specific requirements, present an environment defined by ambiguity. For the consumer, the potential impact of these changes are unclear. To address this uncertainty, we taxonomize the present NAT-centric model of consumer gateway security through a survey of over 300 common vulnerabilities and exposures surrounding NAT and hole punching protocols. From this survey, we contextualize the limited security NAT has provided while serving as the primary perimeter defense mechanism in home networks. We further define how this baseline security model for consumer gateways is reflected in IPv6 through an assessment of ten commonly deployed consumer gateways. Our conclusion is that familiarity of a NAT-centric design is no longer assured for IPv6, requiring an active involvement by users to limit exposures within their home networks.

Competitive perimeter defense with a turret and a mobile vehicle

We consider perimeter defense problem in a planar conical environment with two cooperative heterogeneous defenders, i.e., a turret and a mobile vehicle, that seek to defend a concentric perimeter against mobile intruders. Arbitrary numbers of intruders are released at the circumference of the environment at arbitrary time instants and locations. Upon release, they move radially inwards with fixed speed towards the perimeter. The defenders are heterogeneous in terms of their motion and capture capabilities. Specifically, the turret has a finite engagement range and can only turn (clockwise or anti-clockwise) in the environment with fixed angular rate whereas, the vehicle has a finite capture radius and can move in any direction with unit speed. We present a competitive analysis approach to this perimeter defense problem by measuring the performance of multiple cooperative online algorithms for the defenders against arbitrary inputs, relative to an optimal offline algorithm that has information about the entire input sequence in advance. Specifically, we establish necessary conditions on the parameter space to guarantee finite competitiveness of any online algorithm. We then design and analyze four cooperative online algorithms and characterize parameter regimes in which they have finite competitive ratios. In particular, our first two algorithms are 1-competitive in specific parameter regimes, our third algorithm exhibits different competitive ratios in different regimes of problem parameters, and our fourth algorithm is 1.5-competitive in specific parameter regimes. Finally, we provide multiple numerical plots in the parameter space to reveal additional insights into the relative performance of our algorithms.

Graph neural networks for decentralized multi-agent perimeter defense

In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders. One major challenge for practical implementations is to make perimeter defense strategies scalable for large-scale problem instances. To this end, we leverage graph neural networks (GNNs) to develop an imitation learning framework that learns a mapping from defenders’ local perceptions and their communication graph to their actions. The proposed GNN-based learning network is trained by imitating a centralized expert algorithm such that the learned actions are close to that generated by the expert algorithm. We demonstrate that our proposed network performs closer to the expert algorithm and is superior to other baseline algorithms by capturing more intruders. Our GNN-based network is trained at a small scale and can be generalized to large-scale cases. We run perimeter defense games in scenarios with different team sizes and configurations to demonstrate the performance of the learned network.

“Perimeter defence” and “Zero Trust”

“Perimeter defence” and “Zero Trust”

Classification of Adversarial Attacks Using Ensemble Clustering Approach

As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that is applicable to different network applications. In contrary to the wrapper method introduced with the benchmark dataset, three new filter models are proposed to transform the feature space without knowledge of class labels. These ECT (ensemble clustering based transformation) techniques, i.e., ECT-Subspace, ECT-Noise and ECT-Combined, are developed using the concept of ensemble clustering and three different ensemble generation strategies, i.e., random feature subspace, feature noise injection and their combinations. Based on the empirical study with published dataset and four classification algorithms, new models usually outperform that original wrapper and other filter alternatives found in the literature. This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks, and the second that focuses on recognizing obfuscated intrusions. In addition, analysis of algorithmic parameters, i.e., ensemble size and level of noise, is provided as a guideline for a practical use.

A Decentralized Multirobot Spatiotemporal Multitask Assignment Approach for Perimeter Defense

This article provides a new decentralized approach to solve a perimeter defense problem (PDP). In a typical PDP, many intruders try to enter a territory, and a group of defenders operating both inside and on the perimeter protects the territory by capturing the intruders on the perimeter. The objective of the defenders is to detect and capture the intruders before they enter the territory. Defenders sense the intruders independently and compute their trajectories to capture all the intruders in a cooperative way. Each intruder is estimated to reach a specific location on the perimeter at a specific time, and this is considered as a spatiotemporal task to be handled by a defender. At any given time, the PDP is converted to a decentralized multirobot spatiotemporal multitask assignment (DMRST-MTA) problem. The cost of executing a task for a defender is defined by a composite cost function that includes both the spatial and temporal cost components. In this article, a modified decentralized consensus-based bundle algorithm is presented to solve the above spatiotemporal multitask assignment problem. The performance evaluation of the proposed approach is presented based on Monte Carlo studies, and the results show the effectiveness of the proposed approach under different scenarios. The robustness studies also show that the proposed approach is robust against uncertainties in the heading angles of the intruders. The performance comparison with the decentralized adaptive partitioning approach for the various arrival distributions of intruders clearly shows that DMRST-MTA is efficient.

Adopting Zero Trust Architecture in Data Engineering: Implementing Secure, Trustless Systems for Modern Data Security

The ever-evolving landscape of data engineering necessitates robust security measures to protect sensitive information in distributed environments. Traditional security paradigms, which rely heavily on perimeter defenses, are increasingly inadequate against sophisticated cyber threats. Zero Trust Architecture (ZTA) emerges as a pivotal strategy, advocating the principle of "never trust, always verify." This paper delves into the implementation of ZTA within data engineering, outlining its core principles, significance in modern data security, and practical applications to safeguard data integrity and confidentiality. Through detailed methodologies, algorithms, and visual representations, we explore how ZTA can transform data security practices, ensuring resilient protection against a myriad of cyber threats.

Dynamic Resource Allocation With Decentralized Multi-Task Assignment Approach for Perimeter Defense Problem

In this article, a dynamic resource allocation with decentralized multi-task assignment (DREAM) approach using a dynamic sized team of defenders is presented to protect a perimeter of a high-security area from heterogeneous intruders. In the DREAM approach, the spatio-temporal problem of neutralizing the heterogeneous intruders is converted into a decentralized time-ordered multi-task assignment problem. A single-step dynamic resource allocation algorithm based on the computed assignments determines an optimal number of defenders required. The DREAM approach adds more defenders to the team from the reserve stations or removes the excess defenders from the team such that it always utilizes minimal resources for protecting convex territory. A trajectory computation algorithm converts the optimal multi-task assignments to trajectories for the defenders. The working of the DREAM approach for a typical perimeter defense problem is illustrated using a simulated convex territory protection problem. Based on the results from the ablation study, it is seen that one needs to deploy a higher number of defenders in the team for a better success rate to handle highly maneuvering intruders. Furthermore, a higher defender-to-intruder speed ratio helps in better resource utilization. These results clearly indicate that the DREAM approach can protect any convex territory efficiently with minimal resources.

Signal recognition method based on Mel frequency cepstral coefficients and fast dynamic time warping for optical fiber perimeter defense systems.

To improve the accuracy of signal recognition in optical fiber perimeter defense systems, a method based on Mel frequency cepstral coefficients (MFCCs) and a fast dynamic time warping (FastDTW) algorithm is proposed. Four kinds of sensing signals are acquired by employing an in-line Sagnac interferometer system. MFCC features of the signals are extracted. The FastDTW algorithm is utilized to calculate distances of the optimal warping paths between a test sample and all reference templates. According to the nearest neighbor criterion, signal recognition results are obtained. The average accuracy is over 96%. This proposed identification method is highly efficient and easy to implement.

Competitive Perimeter Defense in Linear Environments

Competitive Perimeter Defense in Linear Environments

System Analysis of Counter-Unmanned Aerial Systems Kill Chain in an Operational Environment

The proliferation of Unmanned Aerial System (UAS) capabilities in the commercial sector is posing potentially significant threats to the traditional perimeter defense of civilian and military facilities. Commercial Off-The-Shelf (COTS) UAS are small, cheap, and come with multiple types of functions which have growing interest among hobbyists. This has prompted the need for facility commanders to have a methodology to conduct quick evaluation and analysis of the facility and the existing Counter-Unmanned Aerial System (CUAS)’s effectiveness. This research proposes a methodology that follows a systems engineering perspective to provide a step-by-step process in conducting evaluation and analysis by employing Model-Based Systems Engineering (MBSE) tools to understand the CUAS’s effectiveness and limitations. The methodology analyzes the CUAS’s operating environment and effects of the dominant factors and impacts that CUAS may pose to other stakeholders (e.g., adjacent allied forces, civilians, etc.) within the area of operation. We then identify configuration candidates for optimizing the CUAS’s performance to meet the requirements of the stakeholders. A case study of a hypothetical airport with existing CUAS is presented to demonstrate the usability of the methodology, explore the candidates, and justify the implementation of a candidate that fits the facility and the stakeholders’ requirements.

Pattern Recognition of Grating Perimeter Intrusion Behavior in Deep Learning Method

An intrusion behavior recognition method based on deep learning is proposed in this paper in order to improve the recognition accuracy of raster perimeter intrusion behavior. The Mach–Zehnder fiber optic interferometer was used to collect the external vibration signal sensing unit, capture the external vibration signal, use the cross-correlation characteristic method to obtain the minimum frame length of the fiber vibration signal, and preprocess the intrusion signal according to the signal strength. The intrusion signals were superimposed and several sections of signals were intercepted by fixed window length; the spectrum information is obtained by Fourier transform of the intercepted stationary signals. The convolution neural network was introduced into the pattern recognition of the intrusion signals in the optical fiber perimeter defense zone, and the different characteristics of the intrusion signals were extracted, so as to realize the accurate identification of different intrusion signals. Experimental results showed that this method was highly sensitive to intrusion events, could effectively reduce the false alarm rate of intrusion signals, and could improve the accuracy and efficiency of intrusion signal recognition.