Pragmatic Security-Aware Cross-Layer Design for Wireless Networks from Vampire Attacks

Now people are widely using the Internet. It only takes a few seconds to send a text message. Other people can upload a video when alerted in microseconds. Even though in many places, Internet connectivity is weak, but the multimedia communications required. Sensor zones need communication between drones. The multimedia server has to make rescue operations, search activities, emergency usage in battle areas and meeting rooms. In the examples provided, everyone needs a node to collaborate. The ad hoc networks of two types Static and Dynamic. In static nodes, fixed called Static-Ad-Hoc-Networks (SANET). In dynamic AdHoc networks, the mobility of nodes (active nodes) happens called the Mobile Ad Hoc Network (MANET). In MANET, an individual node acts as a system and router. It can be deployed everywhere without infrastructure networks. These features make the AdHoc network ideal for situations such as search, rescue operations, combat areas and emergency use. It also challenges the MAC layer to communicate between transport and network layers of TCP / IP reference models [1].

The MANET faces many architectural issues, such as persistent link crashes. Several routing algorithms proposed to solve these problems. Here a couple of ways to manage ad hoc network routing protocols. The first is a proactive Destination-Sequenced Distance Vector (DSDV) routing protocol. The second is the reactive Optimized Link State Routing Protocol (OLSR). DSDV is an active routing protocol that uses the destination generated array number [2, 3]. It helps to identify the old ways. The OLSR prevalent link-state routing protocol overheads with a link table. In OLSR, delay and scalability are low. Existing well-known algorithms are Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing protocols [4]. It searches for the route when needed. The primary use of a proactive routing technique is the scalability of nodes, but it leads to transmission delay. The IEEE 802.11 is standard for MAC layer and ad hoc networks, but Ethernets not ensembles in wireless environments. The IEEE 802.11 standard assumes error is minimal in wired, but in wireless links, it is very high [5].

On the other hand, two-layer security, power usage, and frequent connection breaking problems in Ad-hoc networks addressed. The Network, MAC Layers and Protocol Stack to address these issues. Issues such as power optimization and throughput improvement with energy control. Transmission of power is a significant issue in MANET for more performance. It is because it affects almost all layers. However, this requires cooperation between all layers. CLD manages to find the right resolution for optimization. The cross-layer design acts as intermediate to each layer to solve the problem [6]. The primary method of energy transfer proposed. In this case, the RTS-CTS sends the highest transmission power. The Data-ACC packets the packets to deliver the minimum required level.

The advantage of consumptive transmission control is that it optimizes power intake and increases capability. Energy requirements disturb layers transmissions. However, physical, MAC, and network layers play an important role in enhancing performance. Srivastava et al. [7] proposed the mechanism for interactions between the network, MAC and physical layers for cross-layer design. In wireless sensor networks, (WSN) power transmission utilized to solve energy issues in VANETs, and MANETs. In VANET, the transmission of security communications required to send the maximum number of vehicles. However, this requires further expansion of power and increases the controversy. Power consumption is the highest in sensor networks. However, this can lead to dis-connectivity on the network. De Cicco and Mascolo [8] proposed an algorithm that minimizes connectivity maintenance, energy consumption. The significant problems in cellular networks are power, age, and throughput, verified for Code Division Multiple Access.

Additionally, Cognitive Radio (IEEE 802.22) permits unlicensed operators to make better usage of the licensed networks. The primary issue with 802.22 is that it reduces secondary user engagement. Based on the non-cooperative game theory, the dynamic bandwidth allocation performed.

An extended power control algorithm, which reduces tension and increases the overall flow of the network. Interference and maturation of the databased optimization algorithm used to reduce conflict and energy consumption. Additional hardware such as transceivers needed to control transmission power. In a single channel with no additional hardware, a transmission power control uses the node's scattering power. The development of an algorithm that adjusts its transmission energy based on distance from the destination. In which the nodes determine their transmission power based on the distance from the source and the destination. At high transmission power, a method used to control the RTS sending power. Although it gradually increases RTS's power, CTS sends it to a higher transmission power to prevent data packets from sent to its neighbors. This process improves the throughput of network lifetime and decreases energy usage. It uses interference levels and uses RTS-CTS packets to energize neighbors and optimize their transmission power.

Excessive transmitting power increases link length and causes tension. In the event of a collision, the conflict window in the MAC layer increases. The algorithm is transferring energy to tuning, resulting in a controversial window. A similar method used to optimize the expansion power of the node. The topology of the network and the nodes' degree depend on the node's expansion power. If the node's transmit power is high, it has a high degree of correlation with the node's amplitude. Node degrees are essential for network performance.

Moreover, the power control system for power optimization, network connectivity, topology and optimal node degrees proposed. The cross-layer method to maximize local reuse, limit involvement and conflict. With high transmission power, RTS and CTS performed. In acknowledgments of data packets, the transmission control is equal to the power carrier range and interference range. With the RTE-CTS MAC, IEEE 802.11 reveals unseen node issues once the transmission control is less. Also, the node issue exposed when the transmission power is less. The unseen nodes in the network are available nodes to enhance three-dimensional recycling. High transmission power interferes with additional nodes, and then small transmission power causes link distortion. To address the relationship to contact and engagement between layers, the CLD considers parameters like delay, interference, and familiarity in MAC and physical layers. The link kernel operating system, power control to reduce interference between bandwidth capacity and increase bandwidth efficiency.

Network availability is essential on behalf of wireless nodes because wireless networks consume partial power sources. Power degrading attacks are serious issues from the node of the wireless network and reduce network longevity. Vampires attack Carousal and Stretch [9]. He proposed a plan to detect vampire attacks. In addition to creating simulations using NS2, they also used the algorithm to achieve this. They have tried to provide flexible solutions aimed at vampire power degrading attacks [10, 11].

Moreover, an investigative model is proposed [12] to approximate all node duration from the net lifetime setting. They analyzed power inclusive development and developed methods to enhance the time duration availability of net. Time duration technique All Node Died Time, First Node Died Time towards estimation in the remaining lifetime. The node energy prone to attacks leads to power holes. Power hole in words that describe the situation. Synchronous neighbors on the wireless network exchange packets for quick exits lead to malicious attacks with a stream of traffic. With the power hole, the lifetime of the network significantly reduced. Associated algorithms are Geographic Routing Algorithm and determined the emission time then power perforation order to minimize problems with energy holes.

Furthermore, Peng et al. [13] focusing on attacks on social networking sites. The anonymous attack is intensive in social networking sites. Even though it is private, information is still available to the public. It has the potential to be utilized by criminals to launch fraud attacks. The material is now out to the public, and anybody who comes across it will access it quickly. Even if the info in social sites keeps the amount of information disclosed to a minimum, the more personal the information becomes, the more susceptible it becomes. Social consumers provide more information, the greater the likelihood that they may be impersonated and deceived into allowing access to restricted websites and applications. Furthermore, Raikwar & Mishra [14], fraud attacks on wireless networks studied. They have made it difficult for counterfeit physical assets to detect fraudulent attacks, identify the number of attackers, and find obstacles. Energy computation discovered to determine supports by safeguarding the approach for the incidence of attacks. Marti et al. [15] designed the vampire attack simulation in wireless nets. A mechanism to optimize such attacks from the research is a typical vampire attack. These attacks not exact protocols and should not hinder the instant accessibility of networks. It uses messaging to follow protocol, low data transfer with high power consumption, no problem with detected routes. Therefore, it is challenging to detect vampire attacks. The vampire has proposed a method to reduce aggression. This process involves initiating the mitigation process, finding aggressive nodes. The blacklisting the vampire nodes discusses low-power wireless networks and power drain attacks on such networks. He highlighted the discovery and avoidance of such attacks by creating model evidence of thought.

On the other hand, Singh & Jain [16] were aware of the attacks, which ended batsmen's lives with modified packets. Instead of having to move unnecessarily before arriving at the destination, the packets arrive at the destination. This malicious activity can lead to the spread of energy over wireless networks to investigations conducted by Singh & Jain [16] performed geological investigations on carousel and vampire attacks. This work has prompted an additional investigation into power-drain attacks. It identified both attacks used legitimate but unnecessary packet durations to make energy and dust. Due to vampire attacks, the nodes look like real nodes and too small to resist them. Also proposed a method to identify and avoid similar attacks [17].

There have been numerous attempts done in the past to define drain attacks that hold up the base station to attack. However, some studies extensively analyzed different routing protocols and named the carousel and stretch attack features of the two major power lap attacks. As mentioned earlier, these two attacks are the result of malicious routing on WSN. The data stream passes through malicious means created using an adversary through energy out of the network, resulting in the network lifetime decline. The effective method by Perno, Look, Gustad and Perig (PLGP) first developed to solve the packet-forwarding problem due to security flaws. PLGP modified the forwarding point of this method to be more lenient against fuel-exit attacks. The improved PLGP method found to be suitable for the protection of SCLD-AHNs explored in this section. The integrated optimization of a physical layer and MAC layers integrated into the SCLD-AHN protocol. This methodology designed to use and improve PLGP to enable SCLD-AHN for effective and secure multimedia broadcast over wireless networks.

A packet passes from one source node to sink, and data streams contain routing data, allowing it to move a specified route. However, in the presence of an attack, the packet is injected with the root's malicious structure. The use of PLGP in the proposed Vampire Attack Prevention (VAP) algorithm provides a secure means for data transmission. PLGP's topology search phase provides the correct information. The search result used in the next step is called a packet dispatch step. The topology detection stage states its summary and its other information. When using root search, it can help to find the right path. When attacked by malicious means, the method executing on individual node adds a symbol when a packet moves around. In other cases, any sign of iterations in the packet moves, the compromised node will find it. When packets forwarded to each node, potential vampire nodes checked to identify it, and then place required actions. The Vampire Prevention Algorithm (VPA) is as follows.

5.1 Vampire prevention algorithm implementation in SCLD-AHN

The VPA defends the SCLD-AHN system since power drain attacks. The reason for defense is to resolve CLD not to fail in QoS multimedia flooding. The proposed procedure increases the security of SCLD-AHN.

When the algorithm runs on each node, any packets found in the looping or stretch path initiated by the opposition must reach the node and know its source. In addition, the history of the way the packet traveled. It effectively determines where the packet displays uncompromising behavior.

5.2 Simulation

This section provides targeted VAP results using wireless networks for multimedia propagation and attack prevention on SCLD-AHN. Specific VAP evaluated using a variety of performance metrics. These comprise active nodes per round, the overhead due to complex routing, the source to destination, end delays, the number of rounds in-network, and the network's lifespan. These measures improve the QoS in multimedia broadcast contrary to power drain attacks on wireless environments. Experimental studies conducted using Omnet ++, where specific VAP results implemented without comparing SCLD-AHN to power-exit attacks.

5.2.1 Simulated settings

The metrics parameters such as end-to-end delay, nodes per round and the overhead in complex routing used to enhance security improvements in the VAP algorithm. The network lifetime calculated as Eq. (1).

Network lifetime $=\left(\mathrm{I}_{\mathrm{t}}+\mathrm{S}_{\mathrm{t}}+\mathrm{P}_{\mathrm{t}}+\mathrm{R}_{\mathrm{t}}\right) * \mathrm{~m}$           (1)

Here, I_t denotes the start time, S_t the sending time, P_t the stopping time, R_t the time point, m represents the total nodes. Remaining energy calculated using energy lifetime network in Eq. (2).

Energy Residual $=\frac{\mathrm{I}(\mathrm{E})(\mathrm{St}+\mathrm{Pt}+\mathrm{Rt}) * \mathrm{k}}{100}$          (2)

Eq. (2) network lifetime variables and other parameters ‘k’ used as total rounds, also initial strength I (E) of the round. The rounds 100, 200, 300, 400, 500, 600,700, and 800 network lifetime, residual energy count and several rounds presented in Table 2. Establishment values are created by the simulated revalues. Prevention of power-drain attacks along with the target prevention algorithm. Similarly, the residual force energy attributed to Eq. (2) presented in Table 3. At the end of the computational delay, according to Eq. (3).

End-to-end Delay = Total Node Delay/Number of Nodes       (3)

If the total number of packets counted, the count for each packet delayed. Therefore, the following illustrations describe the end of delay calculations.

Sample calculation

j= Id (Sequence number) of the packet.

Delay[j]= Time Delivered [j] – Time Dispatched[j]

Total Delay[j]= Delay[j] + Early Delay [j]

Average Delay[j] = Total Delay[j] / Count

Delay[j] = |9.2-16.8 | =7.6

Total Delay[j] = 0+7.6

Average Delay[j]= 7.6 / 1.2 = 6.3

The routing overhead is taken as a quantity parameter in the assessment process to find the number of routes the packets pass.

5.2.2 Simulation results and discussion

Wireless networks analyzed to provide CLD flexibility against carousel and stretch attack to provide a better quality of multimedia streaming using SCLD-AHN. Omnet++ used to evaluate the security parameters in the CLD design. The parameters intended to evaluate and prevent power-exit attacks using the VAP algorithm. The parameters include end-to-end delay, nodes per round and the overhead due to routing to measure performance metrics. The Table-2 results show the lifetime of nodes with and without VAP technique. As noted above, resistance compared to attacks with and without attack.

Table 2. Lifetime of network in different rounds

Table 3. Number of attack sequences with and without network energy

To monitor multimedia broadcasting performance with and without VAP algorithm. The network lifetime affects the number of nodes as per the experimental facts. As the number of nodes increases, the network lifetime increases when attacks block the VAP algorithm, the network lifetime increases.

Preventing an attack In Figure 4 horizontal axis, the vertical axis represents the total number of nodes and the number of rounds the iterations conducted in the simulator. The total node considered are 100, 200, 300, 400, 500, 600, 700 and 800. It observed that if nodes increase, then lifetime affected. Similarly, network lifetime increases if attacks disallowed using the proposed technique.

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Figure 4. Lifetime of network with the number of nodes and rounds

Table 4 shows the total capacity of the network and total nodes. It observed that if the attacks are stationary, then the rest of the power improved. The total rounds in-network affects the power remaining with the network.

Resistance In Figure 4 horizontal axis, the vertical axis represents the total rounds and energy remaining throughout the network with a certain number of rounds. Number of rounds are 20, 40, 60, 80, 100, 120, 140, 160. Increasing the number of rounds affects the remaining power of the network as shown in Figure 5. When attacks stopped using the proposed algorithm, the force remaining in each observation round is greater.

Table 4 displays the experimental results corresponding to the number of nodes, with prevention and without prevention of attack resistance mechanisms. The observations indicate that the delay from end to end affects total rounds in the network. If rounds increased, then the delay is increased.

Table 4. Number of nodes for delayed and non-delayed attacks

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Figure 5. Limit the number of forces in the presence and absence of attacks

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Figure 6. The number of nodes with and without attack resistance

Table 5. Number of overhead routing nodes without prevention and prevention

The Figure 6 represents nodes used in the experiment are 100, 200, 300, 400, 500, 600, 700 and 800. The horizontal, vertical axis represents total nodes and final delays, respectively. By preventing attacks, the delayed end significantly reduced.

The results of total nodes and overhead in routing represented in Table 5. The observations show that total nodes in the network affect the overhead path. Overhead reduction occurs if attacks prevented using the proposed algorithm.

As shown in Figure 7, the horizontal, vertical axis represents total nodes and routing overhead correspondingly. The total nodes simulated are100, 200, 300, 400, 500, 600, 700 and 800. It observed that if routing overhead increases as the number of nodes increased. However, these attacks were blocked by the VAP algorithm, then routing overhead reduced compared to the non-blocking of algorithms.

Table 6 shows rounds and live nodes in with several rounds in the network. If rounds increased in the network, then real-time nodes increased.

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Figure 7. Node count vs. routing overhead and lack of attack block

Table 6. The nodes with and without attack resistance

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Figure 8. Number of live nodes and attendance range

Lack of offensive protection As shown in Figure 6, horizontal, vertical axis depicts total rounds and live nodes, respectively. The live nodes with 500, 600, 1000, 1500, 2000, 2500 and 3000 different rounds as shown in Figure 8. It observed that if live nodes decline, then total rounds increased. If the power-drain attack was blocked, the number of active nodes against the various numbers increased significantly.

5.3 Discussion

The proposed CLD design embedded between the MAC and physical layers. The physical layer maintains the energy control using QoS with higher-level layers. Simulation experiments have shown that physically improves the QoS of multimedia data transfer in wireless channels. The QoS parameters end-to-end delay, throughput, and delivery ratio by 40%, 30%, and 60% of average reduced ratios. The MAC layer optimized to test performance gains. Preferences used for data packets in the MAC layer. The precedence obtained by the length of the sag and hop. The possibility of expansion with the MAC layer also considered. It is not a time interval, but the node has the right to start the transmission.

The results summary of average end-to-end delay by 70%, while the energy efficiency increased by 18%. The packet delivery throughput ratio enhanced by 18% and 75%, respectively. Results in the underlying mac layer found to classify media coverage in wireless networks. The results show an average end-to-end delay of 25%, average packet delivery ratio of 45%, average throughput of 29% and average efficiency of 15%. Another cross-layer design developed to investigate further the limitations of MAC and physical layers.

In contrast, energy variation and power are significant parameters in the physical layer. An integrated CLD method combines two layers with other layers for more comprehensive optimization. It estimated against packet error rates in the case of PSNR and PSNR using the Evolution Toolkit. Optimization combined with the proposed CLD improved performance compared to other CLD methods. Simple masking with interest-based error masking in the literature. QS Is 8% dB higher than the average PSNR and 1% dB higher than the most straightforward ROI technique? Multimedia transmission with SCLD-AHN showed a 60% improvement in PSNR without using SCLD-AHN. Security improvements made to SCLD-AHN to protect against forced-exit attacks such as carousel and stretching. Therefore, SCLD-AHN performs its essential features to enhance secured video broadcasting over wireless channels through the proposed Vampire Attack Prevention (VAP) algorithm. The algorithm uses the SCLD-AHN running network, which protects the network from malicious attacks, protecting it from power destruction and vulnerability. With increased security, SCLD-AHN showed a 76% increase in average lifetime and a 36% increase in average remaining network power. The average end-to-end delay against the different active nodes is 76%. The average routing overhead is 72%, and the average number is 20%.