Nowadays, Web bots are used extensively for tasks like search engine indexing and security assessments, but they can also facilitate malicious activities such as ad fraud and data theft and many more. However, existing approaches are unable to detect more advanced bots, such as those driven by Selenium or OpenWPM, which can conceal their browser fingerprint and imitate human browsing behaviors. In this paper, we propose a novel technique for identifying advanced bots, specifically those using OpenWPM, through behavioral analysis. Our approach considers four browsing behaviors, including mouse movement, mouse click, keystroke, and scrolling. We employ an ensemble of lightweight classification models trained on behavioral features, which are augmented using unsupervised clustering in a novel way to enhance detection performance. The detection system is designed in a modular fashion, making it resilient to missing behavioral data and independent of platform-specific features or tasks, enabling generalizability across diverse web platforms. The proposed approach achieves an F1-score of 98.8%, presenting a promising solution for detecting OpenWPM bots and other human-mimicking bots with improved precision.

With the rapid growth of smart home IoT devices, users are increasingly exposed to various security risks, as evident from recent studies. While seeking answers to know more on those security concerns, users are mostly left with their own discretion while going through various sources, such as online blogs, and technical manuals; which may render higher complexity to the regular users to extract the necessary information from. This requirement does not go along with the common mindsets of smart home users and hence threatens the security of smart homes furthermore. In this paper, we aim to identify and address the major user-level security concerns in smart homes. Specifically, we develop a novel dataset of Q&A from public forums, capturing practical security challenges faced by smart home users. We extract major security concerns in smart homes from our dataset by leveraging the Latent Dirichlet Allocation (LDA). We fine-tune relatively “smaller” transformer models, such as T5 and Flan-T5, on this dataset to build a QA system tailored for smart home security. Unlike larger models like GPT and Gemini, which are powerful but often resource hungry, and requiring data sharing, smaller models are more feasible for deployment in resource-constrained or privacy-sensitive environments, like smart homes. The dataset is manually curated and supplemented with synthetic data to explore its potential impact on model performance. This approach significantly improves the system’s ability to deliver accurate and relevant answers, helping users address common security concerns with smart home IoT devices. Our experiments on real-world user concerns show that our work improves the performance of the base models.

Despite the significant benefits of the widespread adoption of smart home Internet of Things (IoT) devices, these devices are known to be vulnerable to active and passive attacks. Existing literature has demonstrated the ability to infer the activities of these devices by analyzing their network traffic. In this study, we introduce a packet-based signature generation and detection system that can identify specific events associated with IoT devices by extracting simple features from raw encrypted network traffic. Unlike existing techniques that depend on specific time windows, our approach automatically determines the optimal number of packets to generate unique signatures, making it more resilient to network jitters. We evaluate the effectiveness, uniqueness, and correctness of our signatures by training and testing our system using four public datasets and an emulated dataset with varying network delays, verifying known signatures and discovering new ones. Our system achieved an average recall and precision of 98-99% and 98-100%, respectively, demonstrating the effectiveness and feasibility of using packet-level signatures to detect IoT device activities.

Identifying vulnerabilities in complex 5G network protocols is a challenging task. Manual analysis is time-consuming and often inadequate. Modern ML and NLP methods, though effective, are resource-intensive and struggle to find implicit vulnerabilities. In this research, we utilize GPT-4’s advanced language understanding to detect vulnerabilities directly from 5G specifications. To assess GPT-4’s fundamental capabilities in this domain, we first adopt a zero-shot approach that relies solely on the specification text without external guidance. For detecting more sophisticated vulnerabilities that require deep contextual understanding, we introduce a novel domain-aware strategy, where we explicitly teach GPT-4 about security properties and hazard indicators from related works using few-shot learning. We further employ chain-of-thought prompting to guide the model through structured reasoning steps to identify violations or exploitations that may lead to vulnerabilities. A two-tier filtering process ensures that only promising test-cases are retained. Our method has identified 47 potential vulnerabilities in 5G mobility management procedures, including 27 previously unreported issues, and generated corresponding test-cases. Simulating 14 of them, we have found 9 vulnerabilities, five of which are new. The zero-shot approach is effective in detecting procedural and validation flaws, while the domain-aware method excels in finding protocol violations and advanced attack scenarios. These findings validate our methodology and demonstrate its strength in discovering both known and novel vulnerabilities in 5G protocols.

Nowadays VANETs are being used to ensure road safety and reduce traffic congestion. Vehicles exchange real time position with one another through basic safety messages and these periodic updates must be authenticated for safety purpose. Malicious vehicles can broadcast spoofed position and such an insider attack cannot be dealt with cryptography and digital signature. However, previous machine learning-based approaches that used distributed data-centric misbehaviour detection schemes could detect such attacks in most of the scenarios. However, in case of smarter attacks in a sparse network (where the attacker vehicle pretend to be stand still even though it is on the move), previous solutions could not always detect such attacks. We propose a novel detection scheme which outperforms previously published methods in this specific scenario. For evaluating the performance of this approach, we have used VeReMi dataset, a public repository for the malicious node detection in VANETs. We combine three consecutive basic safety messages and surpass currently existing methods in detecting eventual stop attacks, especially in sparse networks. Our proposed method can help in securing VANET, thereby preventing fatal accidents.

Containerization using tools such as Docker has transformed the way applications are deployed and managed in various organizations. However, the use of containers also presents new security challenges due to the potential vulnerabilities that may be present in the container images. To address this, various vulnerability detection tools that use static analysis have been developed that focus more on OS packages, and fail to detect known package vulnerabilities. In this paper, we propose a pre-deployment methodology that detects vulnerabilities in container images targeting both the OS and application packages using a layered approach, where static tools generally fail to detect vulnerabilities in those images. Our solution offers a high degree of customizability and control over its performance in detecting vulnerabilities in images. Users can choose a specific scan profile that is tailored to their particular needs, enabling the detection of vulnerabilities that are either more common, more unique, or a balance of the two. This adaptability makes our solution more flexible and better suited to meet the specific needs of our users. We evaluate our proposed framework against 111 both official and community images collected from Docker Hub to demonstrate its effectiveness compared to other popular static analysis tools for containers.

Multi-Access Edge computing (MEC) and Fog computing provide services to subscribers at low latency. There is a need to form a federation among 3GPP MEC and fog to provide better coverage to 3GPP subscribers. This federation gives rise to two issues—third-party authentication and application mobility—for continuous service during handover from 3GPP MEC to fog without re-authentication. In this paper, we propose: 1) a proxy-based state transfer and third-party authentication (PS3A) that uses a transparent proxy to transfer the authentication and application state information, and 2) a token-based state transfer and proxy-based third-party authentication (TSP3A) that uses the proxy to transfer the authentication information and tokens to transfer the application state from 3GPP MEC to the fog. The proxy is kept transparent with virtual counterparts, to avoid any changes to the existing 3GPP MEC and fog architectures. We implemented these solutions on a testbed and results show that PS3A and TSP3A provide authentication within 0.345–2.858s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility while taking 40–52% less time than PS3A using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloudbased service via tokens and prefetching.

The security and privacy of the Android system is an important research area due to the widespread use of Android devices. Most of the existing researches on this area focus on the mobile and wireless networks. Security breach through the speaker remains relatively less explored. In this work, we focus on potential security breaches in the Android system using ultrasonic channel in the speaker. We have proposed a dynamic vocabulary selection strategy to facilitate faster data transmission in ultrasonic side-channel attacks. We also propose a novel piecewise linear amplitude reduction technique that helps the system to achieve a higher bit-rate and higher amplitude for a long time. Our proposed system improves the bit-rate of the modulated audio by 27.5% and maximum attack distance by 20%. The improvement for the data transfer rate is 155% for numeric data, 70% for alpha-numeric data and 27.5% for any ASCII character. We also propose a detection mechanism for similar attacks using Android log analysis.

Vehicle-to-everything (V2X) is one of the generic services provided by emerging 5G wireless technology. Data traffic in V2X demands both communication and computation which is a challenge to ensure better user experience where communication and computation offloading are an effective scheme. Earlier works have only focused on communication offloading policies and algorithms. However, there have been a few analytical studies, especially on computation offloading and the role of Road-Side Units (RSUs). In this work, we propose an analytical model for communication and computation offloading to RSUs and gNB to minimize average packet delay by finding an optimal offloading probability through a sub-gradient based algorithm. Extensive simulations have been carried out to validate our model. Our results show that deploying RSUs with gNB increases efficiency by as much as 19% than deploying only gNB. Sensitivity analysis also shows that RSU service rate has 10 times more impact on reducing average packet delay than gNB service rate caused by relatively lower communication bandwidth of gNB.

Malware is one of the biggest threats for the privacy and security of the smart-phone users. Android is currently the most popular operating system for smart-phones; consequently, many malwares are directed toward Android devices. Existing techniques for malware detection tend to compromise between accuracy and computational complexity. In this paper, we have proposed a novel technique to monitor the behavior of both malwares and benign applications using system calls and have developed a mathematical model that can detect mobile mal-wares. We have extracted features by sequencing the system calls of these applications. We have proposed a novel way of feature reduction using Gaussian dissimilarity and compared our feature selection technique with existing methods. Using the extracted set of features, we have implemented a machine learning classifier, namely Gaussian Bayes classifier, on two different malware data-sets (obtained from Malware Genome Project and Android Malware Dataset by Arguslab) and on non-malware samples (obtained from Google Play Store). We have found that our model is quite lightweight yet powerful to detect malwares with significant accuracy of 98%.

Software Defined Networking (SDN) decouples the control plane from the data plane, thereby enhancing flexibility in network management. Earlier works on SDN modeling only focused on packet-level arrivals without considering flow-level arrivals. However, a model without considering flow-level arrivals cannot correctly reflect the probability of sending packets to the controller. In this paper, we propose an analytical model of SDN considering flow-level (TCP connection) arrivals and packet-level arrivals simultaneously. We use an analytical method termed as 4D state model, which uses four-dimensional states. We have derived the state transition rates of the model and also the packet delay and packet loss probability. We have conducted numerical analysis and extensive simulations. Our results show good matches, which verify the suitability and correctness of our analysis. Error ratios of the analytical results for 4D state model and M/M/1 model against simulation results are also given. Results show that for data packet delays, 4D state can achieve error ratios of 1.16-3.30%, which is much better than 8.57-35.7% attained by M/M/1 model, which only considers packet-level arrivals.

The next generation ubiquitous wireless networks will comprise of mobile users moving between heterogeneous networks with terminals having multiple access interfaces and services. Vertical handover techniques deal with the scenario where a user seamlessly moves between different network access technologies. The most crucial issue in such environment is seamless connectivity. Earlier works demand for infrastructural modification with cumbersome implementations. Here, we propose an automated, transparent, seamless, hybrid decision model based easy-to-implement vertical handover mechanism between WiFi and cellular network. The implementation of the mechanism does not supplant the current infrastructure. We carry out experiments on a testbed to demonstrate the efficacy of our proposed scheme. Experimental results show that a handover-held communication slightly differs from a handoverless communication in terms of diversified network metrics.

NEtwork MObility (NEMO) is a host-based mobility management protocol, which efficiently manages Internet connectivity of multiple nodes on the move. Proxy Mobile IPv6 (PMIPv6), a network-based mobility management protocol, tries to offload low-power mobile devices though managing mobility signaling by the infrastructure devices. To support network mobility in PMIPv6-domain, several schemes have been proposed: NEMO-enabled PMIPv6, PMIPv6-based NEMO and Network-based NEMO. However, there is no work which performs entity-wise cost analysis of PMIPv6 domain though the mobility management entities have limited resources. In this paper, we have developed an analytical framework to perform entity-wise cost evaluation of the above three NEMO schemes in PMIPv6 domain. The performance is evaluated with respect to location update cost, session continuity cost, packet delivery cost and handover cost. Our results show that Network-based NEMO performs better as it distributes its overall load among different network entities. The cost analysis framework presented in this paper will help in choosing the best PMIPv6-based NEMO scheme that imposes least load on the network infrastructure.

Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al. [1] and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.

The usage of Unmanned Aerial Vehicles (UAVs) is increasing day by day.In recent years, UAVs are being used in increasing number of civil applications, such as policing, firefighting, etc in addition to military applications.Instead of using one large UAV, multiple UAVs are nowadays used for higher coverage area and accuracy.Therefore, networking models are required to allow two or more UAV nodes to communicate directly or via relay node(s).Flying Ad-Hoc Networks (FANETs) are formed which is basically an ad hoc network for UAVs.This is relatively a new technology in network family where requirements vary largely from traditional networking model, such as Mobile Ad-hoc Networks and Vehicular Ad-hoc Networks.In this paper, Flying Ad-Hoc Networks are surveyed along with its challenges compared to traditional ad hoc networks.The existing routing protocols for FANETs are then classified into six major categories which are critically analyzed and compared based on various performance criteria.Our comparative analysis will help network engineers in choosing appropriate routing protocols based on the specific scenario where the FANET will be deployed.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. On the contrary, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, there are several proposed scheduling algorithms in the literature. However, these scheduling algorithms are limited because they do not consider different types of customers. Our objective is to satisfy performance expectations of customers by proposing an efficient Dynamic Dedicated Server Scheduling (DDSS) while considering different types of customers. Results show that the customer drop rate and throughput can be significantly improved by DDSS. Our proposed scheduling and related analysis will help cloud service providers build efficient cloud computing service architectures through considering different types of priority class performances such as, drop rate, throughput, and utilization.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. However, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, several scheduling algorithms have been proposed in the literature. However, these scheduling algorithms do not consider heterogeneous servers or different priority classes of customers. Our objective is to satisfy performance expectations of customers by proposing a Heterogeneous Dynamic Dedicated Server Scheduling (h-DDSS) while considering heterogeneous servers and different priority classes of customers. Results show that h-DDSS architecture can provide improved customer throughput and drop rate over homogeneous DDSS in cloud computing. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient cloud service architectures which are adaptable to homogeneous and heterogeneous environments by considering different types of priority class performances, such as, drop rate, throughput, and utilization.

To facilitate higher bandwidth for multimedia traffic, modern routers support simultaneous multi-band communication, leading to less interference, higher capacity and better reliability. However, there is lack of quantitative evaluation to judge whether multi-band is better than single band router in realistic scenarios. Our objective is to propose a scheduling algorithm for multi-band routers and compare single band and multi-band system with different allocation policies. We have used different scheduling algorithms for multi-band routers which transmit different classes of traffic through different frequency bands, thereby achieving improved performance. By comparing multi-band and single band mobile router performances, we have found out that one of them is not always better than the other although multi-band is expected to have better performance.

Recent trends in Internet usage have seen large amount of multimedia data due to increasingly large number of mobile users. To facilitate higher bandwidth, modern mobile routers are capable of supporting simultaneous multi-band, leading to less interference, higher capacity. However, there exists no previous work that attempts to maximize utilization through sharing of traffic among different frequency bands. In this paper, we have proposed a novel scheduling algorithm for multi-band mobile routers which transmits different classes of traffic through different frequency bands to achieve improved performance. We have developed an analytical model to perform queuing analysis of the proposed multi-band system and derived various performance metrics, validated by extensive simulations. Our results show that the proposed architecture can ensure maximum possible utilization through sharing of capacities among the bands. Our proposed scheduling algorithm and related analytical model can help network engineers build next generation mobile routers with higher throughput and utilization ensuring least packet loss for different classes of traffic.

Network mobility (NEMO) protocols are required to maintain connectivity of ongoing sessions for mobile networks that can be formed in bus, train, aircrafts with a wide variety of on-board IP-enabled devices. However, basic NEMO suffers from large delay and packet loss and fails to ensure seamless handover. Multi-homed network mobility solutions can facilitate seamless handover of mobile networks by making use of multiple network interfaces of NEMO. Previous works have not evaluated and compared the performance of multi-homed network mobility protocols that work in two different layers: network layer and transport layer. In this work, we compare the performance of two multi-homed network mobility protocols that exploits make-before-break strategy to ensure seamless handover for NEMO. Using experimental testbed, we have presented a detailed performance evaluation of these two multihomed NEMO protocols. Results demonstrate that transport layer-based mobility solution performs better than network layer-based protocol when the handing off between heterogeneous-capacity networks.

To facilitate seamless Internet connectivity to mobile nodes, we earlier proposed SIGMA, a transport layer mobility management protocol. However, frequent handoff of the mobile nodes produce excessive signaling overhead in the bandwidth-limited air interfaces and cause large handover delay, thereby degrading the performance. Hierarchical mobility management for SIGMA aims at resolving this problem by introducing micro-mobility agents in the hierarchy. In this paper, we perform a comprehensive cost and efficiency analysis of the key mobility management entities of Hierarchical SIGMA taking into account all possible costs. We present numerical results comparing Hierarchical SIGMA with standard hierarchical mobility protocol. Our results demonstrate that Hierarchical SIGMA requires less cost and yields better performance. Our analysis can be used as a framework to compare performance and signaling overheads of mobility protocols for next generation networks.

Mobile networks can be formed in vehicles, ships, satellites with a wide variety of on-board IP-enable devices. In spite of the fact that the connection configuration of the components inside the mobile network can influence its total cost and performance regarding mobility management, there exists no standard topological configuration of the mobile network. In this paper, we have performed a cost analysis of the key components of the nested mobile network considering all possible costs. We have chosen several topological configurations to evaluate their cost and performance. Our analysis will help in selecting a mobile network configuration that minimizes the total costs while maximizing efficiency for data communication.

Mobile networks can be formed in bus, train, aircrafts, satellites with a wide variety of on-board IP-enabled devices and Network Mobility (NEMO) protocols are required to support uninterrupted services to ongoing sessions. Earlier works have not demonstrated seamless handover for NEMO architecture. In this work, we proposed a handover scheme for NEMO that exploits the multi-homing feature of the Mobile Router and uses make-before-break strategy to ensure seamless handover for NEMO. Using experimental testbed, we have presented a thorough handoff performance evaluation of multihomed NEMO and compared it with basic NEMO. Results demonstrate that the proposed multihomed NEMO outperforms the basic NEMO while achieving seamless handover.

Network Mobility (NEMO) efficiently manages the mobility of multiple nodes that moves together as a mobile network. A major limitation of the basic protocol in NEMO is the inefficient route between end hosts. A number of prefix delegation-based schemes have been proposed in the literature to solve the route optimization problem in NEMO. Approaches used by the schemes trade off delivery of packets through the partially optimized route with signaling and other processing overheads. Cost of delivering packets through the partially optimized route along with signaling and processing cost need to be measured to find out the gain from tradeoff. However, cost analysis performed so far on NEMO protocols consider only the cost of signaling. In this paper, we have developed analytical framework to measure the costs of the basic protocol for NEMO, and four representative prefix delegation-based schemes. Our results show that cost of packet delivery through the partially optimized route dominates over other costs. Therefore, optimizing the route completely is preferable to reduction of signaling as far as cost of network mobility is concerned. Our cost analysis framework will help in decision making to select the best route optimization scheme depending on the load imposed by the scheme on the infrastructure.

Seamless IP-diversity based NEtwork MObility (SINEMO) was proposed to address a number of drawbacks of the Network Mobility (NEMO) protocol for mobility management of a number of hosts moving together in a Local Area Network. Increasing number of mobile hosts results in higher level of signalling cost on the mobility agents in a mobility protocol. Previous works on cost analysis of mobility protocols have considered the cost on some of the mobility agents, but did not consider all possible costs for mobility management, resulting in incomplete cost estimation. In this paper, we have developed analytical models to estimate costs of mobility management as functions of network size, mobility rate, traffic rate and data volume for all the entities of SINEMO. Numerical results, comparing the cost between mobility entities of SINEMO and NEMO, reveal that SINEMO has higher efficiency and lower cost than NEMO. Our comprehensive cost model can be used as a framework for estimating total cost of key mobility management entities of different handover protocols, and can aid in decision making to choose the most efficient protocol for future all-IP mobile and wireless networks.

The traffic characteristics in recent years have changed considerably having more video and audio traffic with critical real-time constraints. This situation is compounded by the increased number of mobile nodes communicating over Internet. Due to such high data traffic, the access routers are often overloaded with packets with different priority. The access routers have to act quickly enough to avoid loss of connection of mobile nodes' ongoing communication. However, there is a lack of analytical models that focus on the internal queue management access router of IP-mobility protocols. In this paper, we have developed an analytical model using multi-class non-preemptive priority queues to measure average queuing delay, queue occupancy, and packet drop probability for different classes of data and signaling traffic in the access router. We present numerical results that reflects the impact of node density, service rate and traffic pattern on these measures. The analytical framework presented in this paper will be helpful to better manage access routers with different classes of data and signaling traffic causing least queuing delay and packet loss.

To facilitate mobile computing, mobility protocols have been proposed and mobility management involves signaling costs. Widespread use of IP-enabled mobile devices have resulted in increase in number of mobile users in the network and the signaling cost on underlying mobility entities have increased significantly, which will result in performance degradation of the mobility protocols. However, there has been no comprehensive cost analysis of mobility protocol entities that considers all possible costs for mobility management. In this paper, we have developed analytical models to estimate total costs of mobility management entities of SIGMA, an IP-diversity based seamless handover protocol. We have presented numerical results to demonstrate the impact of increased of network size, mobility rate, traffic rate and data volume on these costs. Our results show that a significant amount of resources are required by the mobility entities for transmission, processing of various signaling messages, as well as searching location database. Our cost analysis will thus help network engineers estimate actual resource requirements of the network in future design.

A number of prefix delegation-based schemes have been proposed to solve the route optimization problem in NEMO, where a group of hosts move together as a mobile network. The schemes trade off between inefficiency of routes and various overheads. With the rapid growth of mobile computing, this overhead will give rise to the scalability issue of these schemes. However, there has been no quantitative study on the asymptotic scalability analysis of these schemes. In this paper, we have developed analytical models for scalability analysis of these schemes in terms of network size, mobility rate, distance between mobility agents, and traffic rate. Our analysis shows that the prefix delegation-based schemes exhibit asymptotically identical overhead on the network, and they show better asymptotical scalability in terms of number of mobile routers. The analytical framework for scalability analysis presented in this paper will help in visualizing the effects of future network expansion on the performance of these route optimization schemes of NEMO.

Node mobility has a direct impact on the performance evaluation of various network mobility protocols. Unfortunately, most of the analysis on mobility protocols used Random Waypoint mobility model which does not represent real-world movement patterns of mobile nodes. In this paper, we have analyzed City Section mobility model, a realistic mobility model for the movement in the city streets. We have developed an analytical model to derive certain stochastic properties, such as, expected epoch length, expected epoch time, expected number of subnet crossings and subnet residence time of this model. Finally, we have applied the model to calculate the signaling cost of NEMO BSP and compared it with Random Waypoint mobility model. Results show that the use of realistic mobility model leads to better estimation of the signaling cost of network mobility protocol.

GRSB - guaranteed, reliable, secure broadcast - is a protocol that provides reliable and secure broadcast/multicast communications. It can be implemented in many types of networks - local area networks, wide area networks, as well as satellite communications. The methodology used in this protocol is surprisingly simple. Three logical nodes are enforced in the network - a central retransmiter, a designated acknowledger, and a playback recorder(s). Through the coordinated service of the three nodes, every user node can be guaranteed to receive all broadcast messages in the correct temporal order. A fourth logical node, the security controller, can be added to the protocol to provide security-related services such as user authentication, message encryption, etc. In this paper, we have proposed a multilevel broadcast model of GRSB (guaranteed, reliable and secure broadcast) protocol , where 1stlevel broadcast mostly maintains the traditional GRSB model. 2ndlevel broadcast has two types of nodes where nodes of Is' kind i.e. nodes that are directly connected with the attachment nodes, can be temporarily in 'not alive' condition and hence follow the traditional GRSB model as well. On the other hand, nodes of 2ndkind i.e. nodes that are not directly connected with the attachment nodes, must be 'alive' all the time and have to follow two phases to finish the broadcast.