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8 March 2023

A Lightweight Anomaly Detection System for Black Hole Attack

,
,
and
1
School of Information Technology and Computer Science, Nile University, Cairo 11511, Egypt
2
School of Computer Science, University College Dublin, D04V1W8 Dublin, Ireland
3
National Telecommunication Institute, Nasr City 11765, Egypt
*
Author to whom correspondence should be addressed.
Electronics2023, 12(6), 1294;https://doi.org/10.3390/electronics12061294
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Abstract

Mobile ad hoc networks (MANETs) are now key in today’s new world. They are critically needed in many situations when it is crucial to form a network on the fly while not having the luxury of time or resources to configure devices, build infrastructure, or even have human interventions. Ad hoc networks have many applications. For instance, they can be used in battlefields, education, rescue missions, and many other applications. Such networks are characterized by high mobility, low resources of power, storage, and processing. They are infrastructure-less; this means that they don’t use infrastructure equipment for communication. These networks rely instead on each other for routing and communication. MANETs use a hopping mechanism where each node in a network finds another node within its communication range and use it as a hop for delivering the message through another node and so on. In standard networks, there is dedicated equipment for specific functions such as routers, servers, firewalls, etc., while in ad hoc networks, every node performs multiple functions. For example, the routing function is performed by nodes. Hence, they are more vulnerable to attacks than standard networks. The main goal of this paper is to propose a solution for detecting black hole attacks using anomaly detection based on a support vector machine (SVM). This detection system aims at analyzing the traffic of the network and identifying anomalies by checking node behaviors. In the case of black hole attacks, the attacking nodes have some behavioral characteristics that are different from normal nodes. These characteristics can be effectively detected using our lightweight detection system. To experiment with the effectiveness of this solution, an OMNET++ simulator is used to generate traffic under a black hole attack. The traffic is then classified into malicious and non-malicious based on which the malicious node is identified. The results of the proposed solution showed very high accuracy in detecting black hole attacks.

1. Introduction

Mobile ad hoc networks (MANETs) can be formed without standard fixed infrastructure or support from administrators [1]; they are rather pre-configured in order to work spontaneously. In general, wireless networks are divided into two main categories: infrastructure and infrastructure-less. In Infrastructure networks, the wireless devices are configured by administrators to be connected to fixed-base equipment to provide them with multiple services, for instance, routing, storage, and security services. In infrastructure-less networks, such as MANETs, nodes are self-configured and do not rely on fixed-base infrastructure; instead, they rely on each other. In other words, every node performs multiple functions [2]. In light of these limitations, MANETs have some challenges that are unique to them compared with traditional infrastructure networks. When establishing MANETs, two important aspects are taken into consideration: security and routing challenges [3]. As for security challenges, MANETs, in general, lack the infrastructure that can perform sophisticated perimeter security functions such as firewalls, border routers, IDS, IPS, etc. When it comes to routing challenges, in order for nodes to communicate with one another, they need a routing function. The routing function is mainly used to ensure that the message from the sender is going the most efficient way (route) to reach its intended destination. The standard traditional infrastructure routing protocols are not effective for MANETs for many reasons. One of them is that MANETs are infrastructure-less networks meaning that the routing function is not performed by dedicated devices such as routers. This function is rather performed by almost every node in the network. That is why new enhanced routing protocols were developed for MANETs.
MANET routing protocols can be divided into two main categories: proactive (table- driven) and reactive (on demand) [4,5]. In table-driven protocols, routing information is maintained regularly whenever any change takes place, while in on-demand routing protocols, routing information is collected only when needed [6]. One of the well-known on-demand routing protocols is the ad hoc on-demand distance vector (AODV). It shows better performance among other on-demand routing protocols [7]. However, due to its limitations, MANETs are exposed to multiple attacks. One of these attacks is the black hole attack. The blackhole attack is a major attack that affects the network performance dramatically [8]. In this attack, the attacker’s node acts as the shortest path to the destination, then drops the packets it receives; hence, it heavily affects the network delivery ration.
The main contributions of this paper are as follows:
  • Reviewing and categorizing the different approaches and comparing the different techniques used to mitigate the black hole attacks in MANETs.
  • Developing a dataset for studying the black hole attacks using OMNET++ in order to thoroughly analyze the traffic in order to effectively study node behavior in the presence of an attack.
  • Developing a lightweight detection system for identifying malicious nodes.
The remainder of this paper is organized as follows: Section 2 provides a background on mobile ad hoc networks, their applications, characteristics, and challenges. Section 3 reviews the related work and efforts undertaken by other researchers in this topic. In Section 4, the methodology of our proposed solution is explained. Section 5 concludes the paper and provides suggestions for future work.

2. Background

Mobile ad hoc networks have unique characteristics and in general are different from networks, especially when it comes to security. In this section, we discuss MANETs applications, security challenges and common attacks.

2.1. MANET Characteristics

MANETs have many special characteristics that help in their unique applications. Their characteristics are as follows:
  • Lack of infrastructure: MANETs are featured as infrastructure-less networks. This makes them efficient in terms of time and cost. They can be formed at very low cost and on the fly [9]. At the same time, it makes them more vulnerable to attacks than standard networks.
  • Distributed management: Due to the lack of centralized management and control, these functions are distributed across the nodes. Hence, the node security, network topology, authentication of new nodes, and data security are all affected [10].
  • Cooperativeness: Unlike standard networks that use client-server architecture, MANETs are peer-to-peer architecture. In order for MANETs to be effective, all nodes should cooperate by providing the functions that are left unattended due to the lack of infrastructure security and centralized management. This cooperation aims at building confidence among nodes.
  • Multi-hop routing: One of the functions that are fulfilled by nodes themselves is routing. In order for a node to send a message to another node, it uses adjacent nodes as hops to reach out to the destination [11]. This is what is called multi-hop routing.
  • Dynamic topology: As there are perimeter boundaries for MANETs, nodes move in and out of the network unpredictably at any time. Furthermore, there is no centralized management, so networks themselves can be formed autonomously at any time [12,13].
  • Decentralized architecture: All the nodes in the network are independent. They are self-configured and do not require any support to join or leave the network. They are autonomous in taking such decisions. They are also free to forward or drop data packets even if they are not supposed to do so [13].
  • Limited resources: Nodes in MANETs are characterized by low resources of power and processing as they run on batteries and have less powerful processing units. The key issue of a limited power supply is that it makes MANET nodes more targeted by denial-of-service attacks [14]. The attacker, in this case, sends additional packets to nodes in order to consume their batteries.

2.2. MANET Security Challenges

MANETs are more vulnerable than standard wired networks due to their limited resources as well as physical security, dynamic topology, and lack of perimeter security. They are more prone to attacks from inside and outside the network. MANET attacks can be divided into two main categories: passive attacks and active attacks [15].
Active attacks are the ones in which the attacker tries to modify or distort the data being transmitted into the network. There are many examples of active attacks, such as black hole attacks, which are the focus of this paper, routing table overflow, impersonation, rushing attacks, denial-of-service attacks, Byzantine attacks, packet replication, and distributed denial-of-service attacks.
Passive attacks are the ones in which the attacker tries to get authorized access to eavesdrop on the data [6]. Some of the examples of passive attacks are eavesdropping, traffic analysis, and location disclosure. In this section, some the MANET challenges are explained as follows:
  • Lack of perimeter security: As MANETs are infrastructure-less, there are no defined boundaries for their nodes. Furthermore, any node can join or leave the network freely, which makes the topology dynamic and challenging. When a malicious node reaches the range of the network, it can impersonate a legitimate node and start an attack.
  • Limited physical security: MANETs are formed on the fly anywhere and at any time. There is no physical security to protect the core service, such as keeping the network backbone in a secure data center in traditional networks.
  • Lack of centralized control: In MANETs, there is no centralized system to provide essential security requirements, such as identification, authentication, and authorization as well as other security services, such as firewalls, network access controls, etc. This makes MANETs more challenging in terms of security than standard networks.
  • Dynamic topology: MANET nodes are free to move in and out of a networks; hence, the connectivity between nodes in MANETs can change anytime because nodes can move freely. The same also applies to networks. Some networks can move and merge into other network. This can change the routing information rapidly all the time.
  • Scalability: MANETs consist of large number of nodes that can grow and shrink according to different situations. This makes MANETs very efficient yet challenging due to the security requirements of identifying and authenticating new nodes.
  • Quality of Service: Different data types have different requirements. For instance, media streaming and live transmission require a higher bandwidth and stability. In such cases, the avoidance of latency and data loss needs to be guaranteed through QOS policies and algorithms.
    ○
    Resource limitations: Nodes in MANETs have limited resources in terms of batteries, processing, and storage. This is a source of two potential issues: first, the nodes can be equipped with sophisticated end protection due to limited processing capability; second, is being targeted by some attacks to drain batteries.
    ○
    Security: Due to the various vulnerabilities stemming from the lack of physical recourse, limited resources, absence of infrastructure, and dynamic topologies, MANETs possess more security challenges than traditional networks.

2.3. MANET Routing Protocols

Routing protocols are the methods or rules that define how nodes will communicate with each other. The key function of a routing protocol is to find the best way a message can take from a sender to reach the destination [16]. Generally, there are three common categories of routing protocol as depicted in Figure 1 proactive, reactive, and hybrid [17].
Figure 1. Classification of MANET routing protocols.
In proactive routing protocols, each node keeps a table of all the possible routes, and in case of any change in the network (i.e., a node moves in or out of the network), the table is updated at a predefined interval. That is why it is called a table-driven protocol. Proactive protocols aim mainly at efficiency. The nodes react effectively with changes, and they are ready every time to send data efficiently. The key issue is the overhead on the network to update changes at a fixed interval.
In reactive protocols, when a change takes place in the network, nothing happens unless a data exchange starts and discovers the change. Then, the update takes place; that is why it is called an on-demand routing protocol [4]. These protocols do not put a heavy overload on the network proactive routing protocol as it does not update regularly regardless of the need. That is why there is a latency every time during data exchange in case of having a change in the network. To solve the previous two issues, hybrid protocols were developed. In such a type, a mix of both algorithms is adopted. For instance, a proactive routing protocol is performed with close nodes so that routing tables are updated without creating a heavy overhead. At the same time, a reactive protocol algorithm is adopted for remote nodes to decrease the time taken to discover the routes with remote nodes.

2.4. Black Hole Attack

A black hole attack exploits the way the AODV routing protocol works. In the AODV routing protocol, every node in the network maintains a routing table in which it keeps all the information about the most efficient routes to particular destinations. When a node tries to send a packet to another node, it first checks whether or not it has the information needed in its own routing table. If it doesn’t find it or if the required route is not active anymore, it initiates a discovery process. In this case, the node broadcasts a route request (RReq) to all its neighbors. If the receiving node is the destination node, it then sends back a route reply (RRep) containing the most updated sequence number, the broadcast ID, and hop count [18]. If not, it checks its own routing table to see if it has any routing entries to the destination, and it then compares the routing destination sequence. If the sequence number is less or equal to the one it has, then it sends out an RReq to its neighbors. If the sequence number if higher, then it means that it is a fresh route, and it updates its own table with it and sends back an RRep to the one it sent the RReq to [19]. A blackhole attack happens when a malicious node injects itself into a network and then claims that it has the shortest path to the destination [20]. Figure 2 shows an illustration of this process. Node “S” wants to reach node “D”, so it sends out “RReq to the adjacent nodes. Node “M” injects itself and replies rapidly claiming that it has the best route. Once communications start, node “M” drops all the information sent through it.
Figure 2. Malicious node (M) drops packets in a black hole attack.
A blackhole attack is one of the active attacks [21] when all the data passing by the malicious node are dropped. In such an attack, the malicious node broadcasts false information to its adjacent nodes that it has the shortest paths to the destination requests by the other nodes. The black hole attack affects the performance of the network, especially the throughput and the packet delivery ratio. There are two types of black holes, single and cooperative. A single attack happens when only one node is malicious. A cooperative attack happens when there is more than one malicious node in the same network, and they drop the packets together, which is more complex than a single black hole attack.

4. Methodology

The methodology used in this paper is based on four steps depicted in Figure 4. The first step is generating the data that will be used for machine learning analysis. This is performed through an OMNET++ simulator in order to generate traffic data that is very similar to a real traffic while having a black hole attack. Then, the generated data are collected in a certain format that can be further analyzed later on. There are some common features or characteristics of the traffic records that are collected. These behaviors are analyzed via a support vector machine (SVM) in order to classify the traffic into normal and malicious traffic. Based on this analysis, malicious nodes can be identified and blocked.
Figure 4. Methodology used in the paper to mitigate a black hole attack.

4.1. Proposed Solution

In MANETs, all nodes should be cooperative. In other words, they should rely on each other to perform the missing functions by the lack of infrastructure. Otherwise, the whole network will not work properly. A blackhole is one of the attacks that targets MANETs to corrupt them. Such attacks can be detected by studying the behavior of those malicious nodes in a network. They have some common behavioral characteristics that can be summarized as follows:
  • They increase their transmission power so that they can respond to most of the RREQ.
  • They almost never send any RREQ.
  • They always unicast and almost never broadcast.
Our solution is to develop a lightweight anomaly detection system (LADS) that can detect malicious nodes based on the behavioral characteristics mentioned above and then label those nodes as malicious in order to isolate them from the network.

4.2. Data Generation

Omnet++ 5.7 is used to simulate both the behavior of normal and malicious nodes. The simulation is performed on 7 nodes as shown in Figure 5. One of them is stationary and functioning as a sender (Node 1). This node is omitted from the graphs as it is not relevant to show the behavior of the mobile nodes, which are the core of the simulation. Two scenarios are performed in this simulation. Scenario one simulates that all nodes are cooperative and no malicious node exists. All the nodes accordingly are behaving normally. In the second scenario, Node 6 is setup as a malicious node, and the rest of nodes continue doing their functions normally. The radio transmission power of all nodes is set to 1 mW.
Figure 5. A snapshot from OMNET++ while a node sends packets (RReq) to adjacent nodes.
In this scenario as shown in Table 2, the radio transmission power of Node 6 is set to 5 mW. By increasing its radio transmission power, Node 6 is able to deceive all its neighbors that it is the most adjacent one to them. Accordingly, it receives as many requests as possible. In other words, when a node searches for the best routes and sends a RReq, the attacking node will appear as an adjacent node and will be the first to reply with an RRep as soon as possible.
Table 2. The parameter configuration used to generate the dataset in OMNET++ simulator.

4.3. Data Collection

The results were collected from the first scenario as depicted in Table 3, which assumed that all the nodes are cooperative and behaving normally as well as the second scenario, which assumed that one of the nodes acted as a malicious node. The results of the two scenarios were then fed into the detection system for analysis. The dataset contained the most critical data that resulted from the simulation run in OMNET++. It contained the AODV request, the transmission power of the nodes while sending their packets, and the data transfer type (broadcast/unicast).
Table 3. The parameter configuration used to generate the dataset in OMNET++ simulator.

4.4. Feature Selection

As mentioned in Section 5, there are 3 factors featuring the malicious behavior of black holes in this research as depicted in Figure 6. The first feature is that malicious nodes increase their power to deceive the other nodes that it is the most adjacent one to them. The second one is that malicious nodes almost never send any RReqs. Rather, they reply to as many requests as possible. The last but not least feature is that they always unicast and almost never broadcast.
Figure 6. Feature selection based on malicious node behaviors.

4.5. Data Processing

The data extracted from the OMNET++ simulator consist of eight columns. Five out of the eight columns will be used for analysis, while three of them will be discarded as they will no add value to the analysis. The five values are as below:
  • Hops: This column helps the research in two ways. First, it reveals the direction of the transmission as well as the node that is used as a hop, or in other words, the node that performed the routing function.
  • Transmission type: This field provides the value of the transmission power. It reveals two important values: whether it is a route request (RReq) or route reply (RRep). This is one of the important fields that is used to identify the nodes that are not sending any RReqs. This is one of the features with other ones that reveal misbehavior.
  • Node name: This field contains the node name that transmits the data. It is used to identify each node and recognize the node that misbehaves.
  • Transfer type: This field contains the value of the transfer type and whether it is broadcast or unicast. The importance of this is that it is used to show the nodes that are not broadcasting. These nodes are the suspicious ones that are expected with other features to misbehave.
  • Transmission power: This field shows the transmission power used to send the data. It is also important to show if there is any manipulation in the power used for communication because usually a black hole attack increases the power of the node.
  • The traffic then will be fed into system as shown in Figure 7 where it can be processed and based on the feature selection the malicious nodes can be identified using SVM machine learning algorithm.
    Figure 7. Intrusion detection system methodology.

4.6. Using Machine Learning (SVM)

A support vector machine (SVM) is a well-known machine learning algorithm that is widely used for pattern classification problems. As depicted in Figure 8, the model consists of three lines. The line in the middle is called the optimal classification line, and the other two lines are called the lines of the margin. These lines are used to classify patterns into two classes [35,36,37]. In our case, this model is used for separating the normal behaving nodes from the malicious ones based on the analyzed traffic:
D = x i , y i , i = 1 n  
Figure 8. Support vector machine (SVM).
The first class is positive class (+1), and the second class is the negative one (−1). In the collected dataset, n represents the sample size, x i represents the vector characteristic of i n , and y i is the value of −1 or +1. Characteristics cannot be identical, and there is room for some deviation, but they can be accurately classified. There is a margin defined that can give some room for acceptable deviation. As mentioned previously, the line in the middle is called the optimal classification line, where the sum of w (weighted vector) and b (the bias) is equal to zero, as depicted in Equation (2):
w . x + b = 0
As the vectors’ characteristics are not identical, there are two other lines with some margin. These two lines are parallel to the optimal classification line with the marginal bias.
These two marginal lines form what is called the hyperplane. The points above the hyperplane are captured as the first class, as mentioned in Equation (3):
w . x + b ≥ 1
Similarly, the points below the hyperplane are captured as the second class, as described in Equation (4):
w . x + b ≤ 1
These two classes are referred to later on as malicious and normal vectors.
The main disadvantages of a SVM is that it does not perform well with big datasets, when there is too much noise, and when the number of features exceed the number of trained data samples. These drawbacks of a SVM did not impact the quality of our work as our dataset was not very big, and the features were clear.

4.7. Results

The simulator was configured to examine seven nodes interacting with one another. A rogue node that imitated a black hole attack was set up as one of the nodes. Seven minutes were allocated for it. A total of 13,336 records produced by the simulation were among the records the system examined. The algorithm eventually succeeded in categorizing the records into two groups: legitimate records and harmful records. A total of 10,381 of the 13,336 records were classified as normal, while 2954 were classified as malicious. This was supported by the following three main aspects:
  • A change in the transmission power. As previously explained, the malicious node changes its transmission power in order to appear adjacent to the RReq sender.
  • A remarkable increase in responding to as many RReqs as possible.
  • Always sending unicast, and almost never sending broadcast.
In our simulation, the radio transmission power of Node 6 increased to 5 mW, while the rest of the nodes were normally set to the default of 1 mW. The second feature is that the black hole attacker replied to as many requests as possible while keeping almost salient in terms of route requests (RReqs). The last but not least feature is that the black hole attacker almost never broadcast, and all of its communication was in the form of unicast. As shown in Figure 9, the graph shows the normal behavior of six nodes. All of them send both RReqs and RReps in a normal way. In other words, the number of RReqs to the number of RReps is in a relative proportion in all nodes.
Figure 9. Results of the simulator in the absence of a black hole attack.
Figure 10 shows that there is a huge gap between the number of RReps sent by Node 6 to the rest of the nodes. The reason behind this is that the transmission power of Node 6 increased to 5 mW, while the transmission power of the rest of the nodes is 1 mW. In addition, the number of RReqs sent by Node 6 is almost zero, while it is high with the rest of the nodes and close to each other.
Figure 10. Results of the simulator in the presence of a black hole attack.
The machine learning algorithm identified the malicious records clearly based on the features. The system showed a high accuracy in discovering the malicious nodes through analyzing its behaviors, based on the features mentioned above. The detection accuracy of our solution (LDAS) reached around 99%. When comparing the results of LDAS to other proposed solutions, such as alleviating blackhole identification and prevention (ABIP) that showed a 97% accuracy level and another solution called detection and presentation of a black hole attack (DPBHA), our solution showed a better performance in detecting malicious nodes, as shown in Figure 11.
Figure 11. Intrusion detection system methodology.

5. Conclusions and Future Work

MANETs are networks that are infrastructure-less; rather, they depend on cooperation between nodes by providing both features of being clients and routers. Such networks lack resources and many security features. Hence, they are more fragile than standard infrastructure networks. In this paper, we discussed the different applications of MANETs, their security challenges, and one of the most common attacks, which are black hole attacks. We surveyed, categorized, and compared the solutions proposed in the literature to mitigate black hole attacks. Then, a solution for discovering and avoiding such attacks was proposed using machine learning. In order to examine black hole attacks thoroughly, we used OMNET++ to simulate a malicious node in a MANET network and generated a dataset that we used for analysis and to study the behavior or malicious node acting as a black hole attack. We focused on three key features for identifying black hole attacks: transmission power, the number of responses in relation to the rest of nodes, and the communication method (whether it is unicast or broadcast). These three features were thoroughly examined using machine learning. The limitation of this research is that the simulation was performed on seven nodes only, and the attacker was only one node. In the future, a bigger network can be set up for better analysis, and the attackers can be more than one node. This will allow a deeper analysis of black hole attacks in a bigger network as well as a network traffic analysis with the presence of more than one attacking node.

Author Contributions

Methodology, A.A.; Validation, M.S.E., A.D.J. and M.A.A.; Formal analysis, A.D.J. and M.A.A.; Investigation, M.A.A.; Data curation, A.A.; Writing—original draft, A.A.; Writing—review & editing, M.S.E.; Supervision, A.D.J.; Project administration, M.S.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by the University College Dublin, School of Computer Science.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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