Secure Routing Research Articles

Enhancing Road Safety through Crime Hotspot Detection and Secure Route Planning

In the light of increasing criminal activities, it is become important that everyone should concern about individual safety. It has been seen those criminal activities near criminal hot spot happening frequently. Road safety is of paramount significance, as people frequently need to travel often through unfamiliar areas via various modes of transport, such as walking, public transport, private vehicles, auto rickshaws, or cabs. It is observed this incident are more often when victims are likely to be individual. This paper aims to enhance the safety of people. It analyzed criminal records happened recently and aims to take individual feedback regarding certain incident in particular area. This System intended to take geographical locations as input, to identify the safest route between a given source and destination at a particular time. The approach uses optimized A algorithm to determine optimized route considering crime rate severity and proximity. Open-Source Routing Machine (OSRM) to handle the real-time mapping and routing data. OSRM provides efficient and accurate routing, allowing the system to dynamically calculate the shortest and safest routes based on real world road networks. The system offers three types of routes fastest, safest, and optimized using a modified A algorithm that balances travel efficiency with safety.

A Study on Zone-Based Secure Multicast Protocol Technique to Improve Security Performance and Stability in Mobile Ad-Hoc Network

Since MANET consists of only nodes and is in wireless communication with limited resources, cooperation between nodes is very important. Multicasting technology supports various applications that allow data to be immediately transmitted to target nodes. In environments such as MANET, where nodes are constantly moving, finding an efficient path from the source to the destination is a critical challenge. Providing integrity for the data transmitted from the source to the target node set is also an important part. Maintaining the state of neighbor nodes not only increases the communication and processing overhead but also requires more memory. In this paper, we propose a zone-based secure routing algorithm to improve the performance of routing protocols. We will also prove efficient management of node groups in an area and better scalability, performance, and security despite frequent topology changes by using group keys. To evaluate the proposed technique, detailed and extensive simulation performance with PAST-DM and MSZRP are evaluated. The simulation results show that the proposed technique, compared to other routing protocols, can achieve scalability by maintaining the routing load even if the speed of nodes, the number of sources, the number of group members, and the size of the network increase.

Secure Route: Enhancing Route Safety Through Real-Time Updates and User Feedback

The increasing prevalence of crime in urban areas necessitates innovative solutions to enhance public safety during navigation. This study presents the "Secure Route" system, a community-driven web application designed to provide real-time safe route navigation and crime reporting capabilities. Drawing insights from existing frameworks, the application integrates user feedback, crowdsourced data, and geolocation-based updates to assess route safety. Leveraging techniques such as risk assessment, heatmap visualization, and collaborative data filtering, the system enables users to identify safer routes while avoiding crime-prone areas. This research highlights the importance of community engagement and real-time data in creating a responsive and adaptive safety navigation platform. SecureWalk offers a novel approach to urban safety by fostering collaboration between users and leveraging GIS technologies for informed decision-making. The integration of AI- driven models for analyzing crime patterns and predicting future risks further enhances the accuracy and reliability of the system’s recommendations. By combining collaborative filtering, user feedback, and advanced geospatial analytics, SecureWalk aims to improve both individual safety and community awareness, fostering a safer environment for urban commuters. Keywords: Secure Route, Community-driven, Real- time navigation, Crime reporting Geolocation-based updates, Heatmap visualization

Secure Vehicle-to-Vehicle Communication using Routing Protocol based on Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Enhancing Road Safety through Crime Hotspot Detection and Secure Route Planning

Enhancing Road Safety through Crime Hotspot Detection and Secure Route Planning

An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network

AbstractThis paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.

Energy-efficient and secure routing strategy for opportunistic data transmission in WSNs

ABSTRACT Driven by the critical importance of routing in Wireless Sensor Networks (WSNs) and the security vulnerabilities present in existing protocols, this research aims to address the key challenges in securing WSNs. Many current routing protocols focus on computational efficiency but fall short of providing strong security measures, leaving them vulnerable to malicious attacks. Reactive protocols, often preferred for their reduced bandwidth usage, heighten security concerns due to their limited resources for maintaining network routes, while proactive alternatives require more resources. Additionally, the ad hoc nature and energy limitations of WSNs make traditional security models, designed for wired and wireless networks, impractical. To overcome these limitations, this paper introduces the Secured Energy-Efficient Opportunistic Routing Scheme for sustainable WSNs. The proposed protocol is designed to enhance security by continuously updating neighbor information and verifying the validity of routing parameters, while also being power-aware, a critical factor given the energy constraints of WSNs. The protocol has been evaluated through simulation experiments, measuring key performance indicators such as throughput, average end-to-end delay (E2 delay), energy consumption, and network lifetime. The results demonstrate that the proposed protocol effectively strengthens WSN security while addressing the unique operational constraints of these networks.

Mitigating blackhole attacks in wireless body area network

<p>In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.</p>

Resilient and Adaptive Secure Routing Protocol for Wireless Sensor Networks Using a Grey Wolf Optimizer and Lightning Search Algorithm

The ability of Wireless Sensor Networks (WSNs) to efficiently monitor and collect data from difficult and distant places has made them a notable technical solution for a variety of applications. It is crucial to optimize energy usage and secure data integrity in order to put these technologies to reality with the Internet of Things (IoT). Maintaining a balance between energy economy, flexibility to changing network circumstances, and security is a common difficulty for contemporary routing systems. Because of this, the performance of these protocols drops and they become vulnerable. Specifically, for WSNs, this research presents HGL-ASRP, a state-of-the-art Resilient and Adaptive Secure Routing Protocol. By integrating the Quantum Assisted Grey Wolf Optimizer (GWO) with the Lightning Search Algorithm (LSA), this protocol overcomes the challenges faced by previous routing protocols in the Internet of Things (IoT) setting. Hybrid GWO takes cues from the social behavior of grey wolves and uses them to solve optimization problems very well. As a result of this hybrid GWO and hypercube sampling, grey wolves can only go to a better area to live. With hypercube sampling, the HGL-ASRP method performs better in a number of areas, including speed (90.9 kbps), end-to-end delay (88.8 ms), packet delivery ratio (91.2%), energy efficiency (92.09%), network lifetime (163.63 hours), and security (87.59%). The HGL-ASRP system ensures continuous data transmission even in demanding environments by successfully managing node failures and changing topologies

The Application of Machine Learning in the Field of Network Security

Nowadays, as the technology of AI is becoming more and applying in more fields, there rises some doubt about the safety of using AI. This paper explores the application of machine learning in the field of network security. The introduction provides an overview of machine learning and its importance in ensuring the security of networks. It also highlights the challenges and opportunities when implementing machine learning techniques in network security. The second section delves into the various machine learning techniques used in network security. Supervised learning is discussed concerning intrusion detection, while unsupervised learning is explored for anomaly detection. Reinforcement learning is also examined for secure routing purposes. The third section focuses on the applications of machine learning in intrusion detection. Deep learning, natural language processing, and graph-based machine learning are explored as methods for detecting intrusions. In conclusion, this paper summarizes the key findings of the research conducted. It highlights the implications for future research and provides recommendations for practitioners in the field of network security. Overall, machine learning has proven to be a valuable tool in enhancing the security of networks, but further research and development are needed to address the challenges and fully exploit its potential.

Hybrid RBFN-GWO Approach for Energy-Efficient and Secure Routing in Intelligent IoT Networks for Precision Farming

This paper introduces a hybrid approach combining Radial Basis Function Networks (RBFN) and Grey Wolf Optimization (GWO) to address the challenges of energy efficiency and security in Intelligent IoT networks for precision farming. Our proposed method utilizes RBFN for feature extraction and classification of network states, while GWO optimizes the routing parameters to achieve an optimal balance between energy conservation and security. The hybrid RBFN-GWO algorithm adapts to dynamic network conditions and evolving security threats in real-time. Extensive simulations using real-world precision farming data demonstrate that our approach outperforms existing protocols, achieving a 20% increase in network lifetime and a 15% improvement in intrusion detection accuracy. This research contributes to the development of more efficient and secure IoT infrastructures for precision agriculture applications.

Node Localization Based on Unsupervised Approach and Secure Routing for Healthcare Management in Delay‐Tolerant Network

ABSTRACTThe ability of the current global healthcare system to continue providing high‐quality medical care has been questioned. By using delay‐tolerant networks (DTNs) in healthcare management, people that spend a lot of time treating illnesses are exposed to more people while also putting their lives in danger. Unsupervised approach‐based node localization and secure routing in DTNs are developed in the proposed method. The proposed adaptive density‐based localization algorithm is constructed in two modules. The entire network is clustered depending on density utilizing density‐based spatial clustering of applications with noise (DBSCAN). An RSA‐based location estimation algorithm is employed to identify the unlocalized node within each cluster. After finding the location of each node, routing mechanism is carried out. An adaptive spray and wait routing strategy is utilized in the proposed method. When a node only has one copy of a message, the spray and wait, routing mechanism moves the relay node while it waits for the destination to be countered. Then modified DNA encryption algorithm is applied for secure data transmission. It is used for encrypting the information before storing it in the network. The trusted center will generate the private key needed for encrypting the data based on the sensitivity level of the information. This proposed algorithm is tested with several metrics which attain better performance, like 94% localization accuracy, 6% localization error, average residual energy of 93%, 5.7 Mbps throughput, 18% encryption time, and 14% decryption time. Thus, the techniques used in the proposed approach are the best choice for node localization, energy efficiency, and secure data transmission in DTN.

Development of Secure Routing Algorithms in Computer Networks

Development of Secure Routing Algorithms in Computer Networks

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

ABSTRACT Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques.

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Optimizing Multi-Tier Scheduling and Secure Routing in Edge-Assisted Software-Defined Wireless Sensor Network Environment Using Moving Target Defense and AI Techniques

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.

An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.

Uncovering Hidden Risks in IoT devices: A Post-Pandemic National Study of SOHO Wi-Fi Router Security

This study thoroughly analyzes the cybersecurity status of Small Office/Home Office (SOHO) Wi-Fi routers. These routers are crucial but frequently overlooked elements in network infrastructure, particularly in light of the impact of the COVID-19 pandemic on network security. The pandemic has led to shifts in network usage patterns, blurring traditional security perimeters and extending them into private residences, creating additional points of vulnerability in urban environments. Our nationwide research evaluated an extensive dataset of router brands and models currently used at scale. We measured the prevalence of known vulnerabilities, assessed the currency of userspace and kernel software versions, and compared the security robustness of proprietary firmware against open-source alternatives. Our findings reveal a concerning landscape of widespread vulnerabilities and outdated software components, posing latent risks to end-users. The results indicate a predominance of Linux on MIPS and ARM architectures, with an average delay of 5 to 10 years between the release of the kernel and the implementation of the most recent firmware versions. As a result, we observed an average of 1344 and 72 vulnerabilities in the kernel and applications. One significant discovery from our research is that replacing the manufacturer's original firmware with open-source alternatives, such as DD-WRT, OpenWrt, and Tomato, can substantially enhance the security of the software stack. This enhancement results in improvements of up to 97% in the case of binaries and 98.42% in the kernel. Our research helps increase cybersecurity awareness by pinpointing critical home network environment weaknesses and alerting the need for more rigorous security practices in producing and maintaining SOHO routers. This investigation also allowed the report of a new remote code execution vulnerability (disclosed in CVE-2022-46552).

TSRP: A Novel Trust-Based and Energy-Aware Secure Routing Protocol for Resource-Constrained Wireless Sensor Networks

TSRP: A Novel Trust-Based and Energy-Aware Secure Routing Protocol for Resource-Constrained Wireless Sensor Networks