Multi-Level Node Behavior Pattern Analysis for Malicious Node Detection with False Alarm Reduction in Wireless Sensor Networks

Wireless Sensor Network is a revolutionary technology which is used in many fields such as safety monitoring, environmental monitoring, smart city technology, military surveillance, health monitoring etc. Wireless network of sensor nodes that communicate autonomously in space by collecting, processing and transmitting information [1]. The self-organizing architecture and decentralized nature make WSNs powerful as well as prone to various adversities of security problems. Data injection, which corrupts the integrity of a WSN, and network eavesdropping, which threatens the security of the network, highlights two of the major problems caused by malicious nodes [2]. One of the most serious challenges for efficiently operating these networks is to detect malicious nodes without triggering a high number false alarm [3]. WSNs function in dynamic environments that can be hostile, as the sensor nodes are susceptible to threats like physical tampering, malicious attacks, and environmental disturbances [4]. Because these networks are often deployed in unsupervised or remote locations, they are susceptible to a variety of security threats including node capture, Denial of Service (DoS) attack [5], and false data injection. It is not trivial to detect these malicious nodes since these nodes can easily conceal in the network and conduct attacks without being immediately identified [6].

At the WSNs field, one of the most pernicious kinds of attacks consists of altered nodes behavior. Malicious nodes are also difficult to detect as they can modify their behavior and provide incorrect data or prevent the network from operating normally [7]. The nodes which indicated abnormal behavior can be abnormal data generation, high energy consumption, erratic communication patterns, etc. Detection of such behavior is critical, but ideally implemented without introducing network-level performance degradation [8]. Without minimization, false alarms could exhaust the network's capacity which results into needless reconfigurations and loss of critical data [9]. In WSNs, false alarms can lead to a significant degradation in performance, especially when legitimate nodes are mistakenly identified as malicious [10]. As a result, resources are wasted in identifying and isolating non-malicious nodes because of these false positives. The Attack detection in WSN general process is shown in Figure 1.

Figure 1. General IDS process

One of the main challenges of detecting malicious nodes is the development of an algorithm which achieves sensitive yet specific detection to avoid a benign node being incorrectly labelled as malicious [11]. Finding this balance is crucial to keeping the network agile and secure. Traditional malicious node detection methods based on anomaly detection methods or cryptographic methods often do not have good accuracy in the highly dynamic nature of WSNs [12]. Behavior analysis provides a better approach to identify malicious activity by monitoring the interaction of nodes in the network. It allows flagging as suspicious nodes that greatly deviate from the expected behavior [13]. This approach makes detection more nuanced and reduces the chances of false positives and false negatives, allowing for improved identification and resolution of the task at hand.

Machine learning-based approaches have been suggested for a more accurate analysis of node behavior patterns. Detection of malicious nodes at multiple tiers of the network is possible, transmission rates for packets at the physical layer, and data integrity at the application layer [14]. Other information from multiple layers allows the detection system to distinguish between the normal and malicious nodes more efficiently. It provides improved detection accuracy and reduces communication overheads arising from naive detection approaches [15]. Scalability is another key issue affecting the success rate of malicious node detection schemes for WSNs. And many traditional security solutions don't work well if the network grows because of the high communication overheads they introduce.

Flat topology networks, struggle to achieve the security of nodes and edges while avoiding an excessive degree of communication between them. Based on the summarized details, the solution proposed by this research purposefully tries to minimize communication costs through determining the malicious nodes locally rather than in a network-wide manner. It is possible to identify malicious nodes with lightweight protocols and alert the network to these nodes by monitoring packet drop rates and throughput values to isolate them in advance before they exhibit harmful behavior [16]. The proposed security architecture in this research can not only help to ensure the uninterrupted provision of Quality of Service (QoS), but also offers a rational approach to the actual allocation of QoS resources by more trusting and accredited nodes for normal operations [17].

2.png

Figure 2. Malicious node in WSN

QoS metrics such as data delivery reliability, network lifetime and latency also determine the performance of a WSN apart from security. Malicious nodes can have a significant impact on these metrics via normal operation disruption and false alarms generation. Thus, enhancement of QoS is not merely limiting to detection of attack, but also to sustain the proper communication of nodes that are not malicious along with the existences of the malicious node [18]. The approach proposed in this work aims to provide optimal QoS, is based on an intelligent of a model for behavioral analyses which monitors nodes and isolates malicious ones, and minimizes the probability of false alarms. The MLNPBA-MND-FAR technique which performs Multi Level Node Pattern and Behaviour Analysis for Malicious Node Detection with False Alarm Reduction is proposed in this research. This approach combines a multilayer analysis of the individual behavior of a node, employing statistical as well as machine learning techniques for better accuracy of detection. By cross-verifying the behavior of nodes at various degrees of linkage through the network, these two systems allow for the identification of malicious nodes. In addition to the augmentation technique, they also introduced a method to prevent the overall performance from being reduced when detection is performed. MLNPBA-MND-FAR is a dynamic-based algorithm that can perform genotyping in real-time. By monitoring the behaviour of the nodes, it continuously adjusts the threshold of detection to adapt to the current conditions of the network. Such flexibility in the algorithms is very critical in WSNs where the mobility of the nodes, changes in the environment state, and reconfigurations of the network can make a difference in the communication. Through iterative improvement of its detection model, the system maintains the ability to respond to new attack approaches while preserving network throughput. The malicious node detection in WSN is shown in Figure 2.

Malicious nodes in WSNs can adopt different attack strategies such as data injection, eavesdropping, and selective forwarding. We designed a technique called MLNPBA-MND-FAR to discover these behaviors at the beginning of each attack cycle before they can turn into bigger and destructive attacks. By quickly isolating compromised nodes, the system prevents the entire network from being disabled or compromised. This limits the number of false positives where a benign node is wrongly isolated, saving network bandwidth. Nodes in WSNs are often expected to cooperate and exchange data with each other. In this Harmonious collaboration, malicious nodes can disrupt the system by not sharing the data or providing the corrupted data to the other nodes [19]. The proposed approach before it infects the system, by early detecting malicious nodes and excluding them from the collaboration pool, thus helps ensure that the integrity of the network. The lower false alarm rate guarantees that trusted nodes can continue working together undeterred. One key benefit of the proposed technique is the possibility for improved WSNs long-term efficiency. Increased lifespan and more reliable data collection due to the reduction of false alarms means the ecosystem can function with less interference. So the system's capacity to evolve dynamically to new threats means that it continues to work as new strategies for attack arise. The goal of this research is to detect malicious nodes in WSNs more accurately and reliably while reducing the number of false alarms. Because of their decentralized structure, energy constraints, and open communication design, WSNs are susceptible to a wide range of assaults. Among these are sinkhole attacks, selective forwarding, Denial of Service (DoS), and fake data injection.

Using data transmission habits, energy consumption patterns, and interaction patterns as a starting point, this technique examines node behaviors at several tiers in search of anomalies that could signal malicious activity. With this in mind, we made sure the framework could detect a broad variety of suspicious node actions independently of any particular threat model. Static wireless sensor networks with different density of nodes (simulated to be between 100 and 600 nodes) are the main subject of the research. It disregards very dynamic topologies and doesn't investigate mobile or heterogeneous WSN settings. Consequently, one limitation of this study is that it has only been tested in network deployments that are static, homogeneous, and well-structured. Data analysis's purview is another crucial limit. The model doesn't go into intrusion detection at the encryption level or deep packet inspection, instead focusing on patterns of behavior at the node level. Although the model incorporates multiple layers of detection and mitigation of false alarms, it does not incorporate any external systems such as blockchain for decentralized trust management or cloud-based monitoring.

The limited energy resources impose severe constraints on WSNs that facilitate as their importance as the most critical objective in the design of networks. Active node detection algorithms can be resource-consuming as well as energy-consuming [20]. This algorithm is developed to reduce energy consumption by enhancing detection in suspicious behavior of nodes rather than complete network checking. Doing so extends the longevity of the network with strong security. There are many potential applications for this research. As a result, the ability to detect malicious nodes without adverse system performance is of vital importance depending on the application in critical systems such as military surveillance, healthcare monitoring, and environmental sensing. Topic Stability in varying conditions lies in Deriving MLNPBA-MND-FAR. Similar domain criteria remain applicable where the MLNPBA-MND-FAR approach can be implemented. The proposed MLNPBA-MND-FAR technique is superior to the current malicious node detection techniques in terms of the detection accuracy and the false alarm rate. Traditional methods often result in a higher rate of false positives when the network condition changes. This research thus employs a multi-level analysis approach for detecting malicious nodes, which not only offers a more adaptive mechanism for anomaly detection, but also outperforms the traditional anomaly detection methods.

To eliminate the security challenges in WSNs, the proposed model is Multi-Level Node Pattern and Behaviour Analysis for Malicious Node Detection with False Alarm Reduction (MLNPBA-MND-FAR). Inherently, the WSNs can be prone to various security threats, mainly when they are used in an unattended or hostile environment. One of the major security issues is the detection of malicious nodes, which involves malicious nodes actively disrupting overall network functioning by taking actions such as injecting false data or engage in selective forwarding. A major challenge of the detection process itself is to minimize false alarms in line with ensuring that true malicious behaviors are detected. The system effectively tackles this challenge with a multi-dimensional analytical approach alongside adaptive mechanisms enabling detection of affected nodes without inundating the network with spurious alerts.

Multi-level node behavior analysis is the core of this model. In WSNs, every sensor node communicates with its neighboring nodes to share information; therefore, any behaviour diverging from the CNC can point to the existence of a malicious node. Low-level network metrics can be focused on the first level of the model, such as packet loss rates, delays, and unexpected data flow [21]. At this level, the anomalies are typically linked with fundamental attacks like selective forwarding or DoS attacks where malevolent nodes interrupt or stop valid communication. At the second level, the model looks more deeply at interactions between individual nodes, such as how they process and transmit information [22], and their overall contributions to the direction of the network topology. In case a node fails to transmit data or keeps sending misleading information, it is marked for further review and scrutiny [23]. Level 3 is at a much higher level of sophistication being complex pattern recognition and machine learning techniques be used that analyze high-order statistics of node behavior, different nodes activity covaries in time and/or response to random network conditions. This layered approach provides coverage against malicious nodes at different stages of the network's operation, reducing the chances of ignoring stealthy attacks [24].

One of the important parts in the model is dynamic thresholds adjustment. Such dynamic environments, such as WSN, treat nodes that behavior differently depending on the dynamics of the networks provided in terms of their incoming network requests, environmental circumstances or node mobility. The model overcomes this issue by introducing an adaptive approach that fine-tunes threshold settings according to current event data [25]. They will adjust the thresholds of detection because of increased traffic and prevent false flags due to overload regarding packet loss/delay. Training the system with various normalizable events that might occur enables the DDoS attack detection system to dynamically adapt to the fluctuations of the network over time, suppressing false alarms that would have resulted from normal network activity.

The application of machine learning techniques is significant to improve the detection accuracy of the model [26]. The model can progressively develop and adapt its understanding of what is normal behavior for a given WSN deployment by analyzing node behavior over time [27]. Conventional malicious node detection techniques usually are based on frequent communication among nodes, leading to increased energy consumption resulting in the reduced life span of a network [28]. The localization approach for detecting malicious activity, which is generated by MLNPBA-MND-FAR model to minimize the aforementioned issue. Instead of demanding full worldwide communication, the model uses local node engagement and behaviors to identify anomalies. The model can use local communication and behavior patterns to highlight suspicious nodes without extensive data transfer across the network. This localized detection approach not only significantly lowers the overall communication traffic but also saves energy, which is essential for battery-powered sensor nodes [29].

It then uses a packet drop and throughput evaluation mechanism to improve its ability to detect malicious nodes. In particular, malicious nodes that send data selectively or conduct DoS attacks have abnormal packet drop curves or inconsistent upflow per node. In the MLNPBA-MND-FAR model, these metrics are used to monitor the performance of each node continuously. A node is marked as suspicious when it acts abnormally. Such early detection enables the scheme to prevent malicious nodes from damaging the performance of a network significantly. After identifying a malicious node, the model cuts it off from the network to cease any damage it could cause. Isolation process ensures that the rest of the nodes in the network aren't disrupted and continue functioning normally. Once the malicious node is identified and removed, a network recovery protocol is initiated, wherein the remaining nodes collaborate to reconfigure the network, aiming to resume typical functionality. This could also consist of deciding new pathways for the traffic of information or redistributing workloads among other nodes in the network. Minimizing downtime and allowing the network to continue operating in the presence of compromised nodes are the goals of the recovery mechanism.

In addition, to facilitate the recording of nodes' behavior over time, this model also includes a trust-based monitoring system. Nodes that display consistent benevolent behavior, such as successful data deliveries and low packet loss rates, are rewarded with a high trust score. On the other hand, nodes that exhibit abnormal characteristics like a high volume of lost data or losing part in the routing process are given low trust scores. It would not only conduct the detection, but also dynamically evaluate its trust through assigning adaptive and reasonable weight to nodes. The MLNPBA-MND-FAR model provides a significant advantage by drastically reducing false positives, resulting in a common issue found in traditional detection schemes. A false positive benign node that has been tagged as malicious can cause massive disruptions to network performance and waste valuable computational resources on needless checks. By dynamically changing the thresholds and having machine learning techniques, near-real-time behavior of the devices can be analyzed at a localized level, which reduces false alarms. Such false alarms can further create cascading effects in large-scale networks, which leads to the performance deterioration of the entire system. The model handles the WSN more reliable and efficient by addressing the malicious nodes correctly with no further disturbance for benign nodes.

The model is designed with a special consideration for efficient power consumption. In addition, WSNs are usually made up of sensor nodes that have limited energy, which means that any solution with significant energy overhead can reduce the network operational lifetime. By considering localized analysis and reducing inter-node communication, the MLNPBA-MND-FAR model is also energy-efficient. Dynamic adjustment of detection thresholds also guarantees that the system does not perform unnecessary checks or communications, thereby decreasing energy consumption. This energy-efficient mechanism is crucial for the sustainable longevity of WSNs, particularly in distant or inaccessible conditions where regular upkeep is unfeasible.

Dynamic threshold adjustment is an important part of the MLNPBA-MND-FAR model, which aims to reduce false positives and improve the accuracy of malicious node identification. When deciding whether a node's actions are harmful or not, traditional threshold-based models frequently employ fixed or static thresholds. However, in actual WSNs, where factors (such as energy levels, node behavior, and traffic load) fluctuate regularly, fixed thresholds might not function.

In response to changes in average transmission rates, energy consumption patterns, or behavioral outliers over time, the detection system can dynamically adjust the threshold and update the decision border. In order to keep its sensitivity to changes in network dynamics while avoiding overreaction to transient fluctuations, the model routinely recalculates the anomaly detection threshold and analyzes these metrics. This flexibility greatly decreases the number of false alarms, which is particularly useful in networks that are dense or dynamic, where the normative behavior might differ greatly. If the computed node behavior score is higher than the adjusted threshold, the node is marked as possibly malevolent. Future threshold calibration can be informed by the feedback loop from previous detection outcomes, which further improves detection accuracy. The pseudo code for the proposed model is clearly indicated.

Pseudo Code: MLNPBA-MND-FAR

Input: Network nodes N[], behavior metrics B[], energy levels E[], transmission logs T[]

Output: Detected malicious nodes M[], updated threshold θ

Initialize:

    M[] ← empty

    Set initial threshold θ = θ_base

Begin

    For each node i in N[]:

        Monitor behavior metrics B[i]

        Monitor energy consumption E[i]

        Monitor transmission activity T[i]

        Compute Behavior Score BS[i] using:

            BS[i] = weight1 * anomaly_rate(B[i]) +

                    weight2 * energy_drift(E[i]) +

                    weight3 * packet_drop_rate(T[i])

    End For

    Compute network average behavior score BS_avg

    Compute deviation σ from BS_avg

    Update threshold θ dynamically:

        θ = BS_avg + α * σ   // α is sensitivity coefficient

    For each node i in N[]:

        If BS[i] > θ then

            Mark node i as malicious

            Add node i to M[]

        Else

            Mark node i as normal

        End If

    End For

    Return M[], updated θ

End

Another major benefit is the model’s scalability. Scalability is a key factor in WSNs as they are mostly deployed in a large-scale environment and performance of such networks is directly proportional to the number of nodes and scale of the area. Using multi-level analysis framework also enhances the labeling process and the localized detection and dynamic threshold adjustments also balances between the model scale up and scalability without any overhead on it. The versatility of the model makes it adaptive to networks of differing scales, from small instrumentation deployments to large dense sensor fields, while ensuring no degradation in detection accuracy or system performance. The proposed model framework is shown in Figure 3.

3.png

Figure 3. Proposed model framework

The proposed model can be applied widely in real-time applications, which include but are not limited to military surveillance, smart cities, healthcare, and environmental monitoring, all of whose utmost goals are to achieve maximum security. In these situations, the effects of a corrupted sensor node can be serious, causing wrong data transmission, wrong interpretation of essential information and even filling the gap in complete system failure. The risks of the previously mentioned attempts are conquered by MLNPBA-MND-FAR model as it identifies and isolates the malicious nodes that keep data integrity and that the network is working. The proposed MLNPBA-MND-FAR model provides an effective and flexible framework for malicious node detection in WSN that minimizes generation of false alarms and reduces energy consumption. The model maintains a well-balanced control among security, performance, and energy efficiency through multi-level behavior analysis, machine learning approaches, dynamic threshold arrangement, and working in a localized manner. This research proposes a Multi Level Node Pattern and Behaviour Analysis for Malicious Node Detection with False Alarm Reduction technique for improving the Quality of Service (QoS) Levels in the network.

Node information processing involves aggregating and analyzing the data received from all nodes in the network. This can be mathematically represented as:

$N_{I n f o[M]}=\sum_{n=1}^M$ nodeattr $(n)+$ nodeaddr $(n)+T I(n)$

Nodeattr(n) model collects the node properties and nodeaddr(n) model is used to identify the node physical address and TI(n) is the time instant, n is represented as current node and M is the total nodes in the network.

Node authentication ensures that only legitimate nodes participate in the network. The authentication process can be modeled as:

Node_Authen $[M]=\sum_{n=1}^M H f(K e y(n) \| D(n))$

Here Hf is the Hash function, Key(n) is Key of node n and D is the Nonce generated for node n used for authentication.

The dynamic threshold calculation is performed as

$S D[M]=\sum_{n=1}^M \sqrt{\frac{1}{M} \sum_{n=1}^M H f(k e y(n))+N o d e \_A u t h e n t(n)}$

$D T[M]=\sum_{n=1}^M \gamma(S D(n))+\max (S D(n, n+1))$

Here γ is the model is used to identify the behaviour score of each node.

Pattern and behavior analysis compares current behavior to historical patterns. It can be represented as:

NpattrnBehav $[M]$$=\sum_{n=1}^M D(n)+\|P(n)-\operatorname{Pref}(n)\|+S D(n)$

Here D(n) is the Deviation score of node n, P(n) is the Current pattern of node n and Pref indicates the historical previous pattern in data transmission.

Malicious nodes are identified by setting a threshold for deviation. If a node's deviation exceeds the threshold, it is flagged as malicious. The process is performed as

$\operatorname{MalNode}[M]=\sum_{n=1}^M \operatorname{MalNode}(n)$$=1$ if $(D(n) \& \& N p a t t n B e h a v(n)>T h$

Here Th is the Threshold for deviation to consider a node as a malicious node.

False alarm detection involves identifying incorrect flags of malicious behavior. It can be modeled as:

$\operatorname{Falarm}[M]=\sum_{n=1}^M \operatorname{prob}(\min (D(n)))+\min (\operatorname{simm}(P(n)))+\max (\operatorname{diff}(NpattrnBehav(n)))$

Simm(P(n)) model considers the similarity in the pattern, diff(NpattrnBehav(n)) model considers the difference and prob(min(D(n))) model calculates the probability function in false alarms.

Algorithm MLNPBA-MND-FAR

BEGIN

nodes_info = GetNetworkNodesInformation()

authenticated_nodes = []

    FOR each node IN nodes_info DO

        IF AuthenticateNode(node) THEN

authenticated_nodes.ADD(node)

        END IF

    END FOR

node_behavior_data = []

    FOR each node IN authenticated_nodes DO

        behavior = AnalyzeNodeBehavior(node)

node_behavior_data.ADD({node, behavior})

    END FOR

trusted_nodes = []

    FOR each behavior_data IN node_behavior_data DO

        IF CheckDataPatternSimilarity(behavior_data) THEN

trusted_nodes.ADD(behavior_data.node)

        END IF

    END FOR

selected_trusted_nodes = SelectTrustedNodes(trusted_nodes)

data_transmission_results = []

    FOR each node IN selected_trusted_nodes DO

        result = AnalyzeDataTransmissionAndPattern(node)

data_transmission_results.ADD({node, result})

    END FOR

malicious_nodes = []

false_alarms = []

    FOR each result IN data_transmission_results DO

        IF IsMaliciousNode(result) THEN

malicious_nodes.ADD(result.node)

        ELSE IF IsFalseAlarm(result) THEN

false_alarms.ADD(result.node)

        END IF

    END FOR

Generate selected_trusted_nodes

Generate  malicious_nodes

    Generate  false_alarms

END