Biblio
Vehicular Adhoc Network (VANET), a specialized form of MANET in which safety is the major concern as critical information related to driver's safety and assistance need to be disseminated between the vehicle nodes. The security of the nodes can be increased, if the network availability is increased. The availability of the network is decreased, if there is Denial of Service Attacks (DoS) in the network. In this paper, a packet detection algorithm for the prevention of DoS attacks is proposed. This algorithm will be able to detect the multiple malicious nodes in the network which are sending irrelevant packets to jam the network and that will eventually stop the network to send the safety messages. The proposed algorithm was simulated in NS-2 and the quantitative values of packet delivery ratio, packet loss ratio, network throughput proves that the proposed algorithm enhance the security of the network by detecting the DoS attack well in time.
Although Vehicle Named Data Network (VNDN) possess the communication benefits of Named Data Network and Vehicle Opportunity Network, it also introduces some new privacy problems, including the identity security of Data Requesters and Data Providers. Data providers in VNDN need to sign data packets directly, which will leak the identity information of the providers, while the vicinity malicious nodes can access the sensitive information of Data Requesters by analyzing the relationship between Data Requesters and the data names in Interest Packages that are sent directly in plaintext. In order to solve the above privacy problems, this paper presents an identity privacy protection strategy for Data Requesters and Data Providers in VNDN. A ring signature scheme is used to hide the correlation between the signature and the data provider and the anonymous proxy idea is used to protect the real identity of the data requester in the proposed strategy. Security Analysis and experiments in the ONE-based VNDN platform indicate that the proposed strategy is effective and practical.
Network-on-Chip (NoC) is the communication platform of the data among the processing cores in Multiprocessors System-on-Chip (MPSoC). NoC has become a target to security attacks and by outsourcing design, it can be infected with a malicious Hardware Trojan (HT) to degrades the system performance or leaves a back door for sensitive information leaking. In this paper, we proposed a HT model that applies a denial of service attack by deliberately discarding the data packets that are passing through the infected node creating a black hole in the NoC. It is known as Black Hole Router (BHR) attack. We studied the effect of the BHR attack on the NoC. The power and area overhead of the BHR are analyzed. We studied the effect of the locations of BHRs and their distribution in the network as well. The malicious nodes has very small area and power overhead, 1.98% and 0.74% respectively, with a very strong violent attack.
Mobile Ad Hoc Networks are dynamic in nature and have no rigid or reliable network infrastructure by their very definition. They are expected to be self-governed and have dynamic wireless links which are not entirely reliable in terms of connectivity and security. Several factors could cause their degradation, such as attacks by malicious and selfish nodes which result in data carrying packets being dropped which in turn could cause breaks in communication between nodes in the network. This paper aims to address the issue of remedy and mitigation of the damage caused by packet drops. We proposed an improvement on the EAACK protocol to reduce the network overhead packet delivery ratio by using hybrid cryptography techniques DES due to its higher efficiency in block encryption, and RSA due to its management in key cipher. Comparing to the existing approaches, our simulated results show that hybrid cryptography techniques provide higher malicious behavior detection rates, and improve the performance. This research can also lead to more future efforts in using hybrid encryption based authentication techniques for attack detection/prevention in MANETs.
A Mobile ad hoc network (MANET) is a set of nodes that communicate together in a cooperative way using the wireless medium, and without any central administration. Due to its inherent open nature and the lack of infrastructure, security is a complicated issue compared to other networks. That is, these networks are vulnerable to a a wide range of attacks at different network layers. At the network level, malicious nodes can perform several attacks ranging from passive eavesdropping to active interfering. Wormhole is an example of severe attack that has attracted much attention recently. It involves the redirection of traffic between two end-nodes through a Wormhole tunnel, and manipulates the routing algorithm to give illusion that nodes located far from each other are neighbors. To handle with this issue, we propose a novel detection model to allow a node to check whether a presumed shortest path contains a Wormhole tunnel or not. Our approach is based on the fact that the Wormhole tunnel reduces significantly the length of the paths passing through it.
Mobile Ad Hoc Network (MANET) is pretty vulnerable to attacks because of its broad distribution and open nodes. Hence, an effective Intrusion Detection System (IDS) is vital in MANET to deter unwanted malicious attacks. An IDS has been proposed in this paper based on watchdog and pathrater method as well as evaluation of its performance has been presented using Dynamic Source Routing (DSR) and Ad-hoc On-demand Distance Vector (AODV) routing protocols with and without considering the effect of the sinkhole attack. The results obtained justify that the proposed IDS is capable of detecting suspicious activities and identifying the malicious nodes. Moreover, it replaces the fake route with a real one in the routing table in order to mitigate the security risks. The performance appraisal also suggests that the AODV protocol has a capacity of sending more packets than DSR and yields more throughput.
Mobile ad-hoc network (MANET) is a system of wireless mobile nodes that are dynamically self-organized in arbitrary and temporary topologies, that have received increasing interest due to their potential applicability to numerous applications. The deployment of such networks however poses several security challenging issues, due to their lack of fixed communication infrastructure, centralized administration, nodes mobility and dynamic topological changes, which make it susceptible to passive and active attacks such as single and cooperative black hole, sinkhole and eavesdropping attacks. The mentioned attacks mainly disrupt data routing processes by giving false routing information or stealing secrete information by malicious nodes in MANET. Thus, finding safe routing path by avoiding malicious nodes is a genuine challenge. This paper aims at combining the existing cooperative bait detection scheme which uses the baiting procedure to bait malicious nodes into sending fake route reply and then using a reverse tracing operation to detect the malicious nodes, with an RSA encryption technique to encode data packet before transmitting it to the destination to prevent eavesdropper and other malicious nodes from unauthorized read and write on the data packet. The proposed work out performs the existing Cooperative Bait Detection Scheme (CBDS) in terms of packet delivery ratio, network throughput, end to end delay, and the routing overhead.
In Mobile Ad-hoc Network (MANET), we cannot predict the clear picture of the topology of a node because of its varying nature. Without notice participation and departure of nodes results in lack of trust relationship between nodes. In such circumstances, there is no guarantee that path between two nodes would be secure or free of malicious nodes. The presence of single malicious node could lead repeatedly compromised node. After providing security to route and data packets still, there is a need for the implementation of defense mechanism that is intrusion detection system(IDS) against compromised nodes. In this paper, we have implemented IDS, which defend against some routing attacks like the black hole and gray hole successfully. After measuring performance we get marginally increased Packet delivery ratio and Throughput.
The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.
Secure routing in the field of mobile ad hoc network (MANET) is one of the most flourishing areas of research. Devising a trustworthy security protocol for ad hoc routing is a challenging task due to the unique network characteristics such as lack of central authority, rapid node mobility, frequent topology changes, insecure operational environment, and confined availability of resources. Due to low configuration and quick deployment, MANETs are well-suited for emergency situations like natural disasters or military applications. Therefore, data transfer between two nodes should necessarily involve security. A black-hole attack in the mobile ad-hoc network (MANET) is an offense occurring due to malicious nodes, which attract the data packets by incorrectly publicizing a fresh route to the destination. A clustering direction in AODV routing protocol for the detection and prevention of black-hole attack in MANET has been put forward. Every member of the unit will ping once to the cluster head, to detect the exclusive difference between the number of data packets received and forwarded by the particular node. If the fault is perceived, all the nodes will obscure the contagious nodes from the network. The reading of the system performance has been done in terms of packet delivery ratio (PDR), end to end delay (ETD) throughput and Energy simulation inferences are recorded using ns2 simulator.
Mobile ad-hoc networks (MANETs) are decentralized and self-organizing communication systems. They have become pervasive in the current technological framework. MANETs have become a vital solution to the services that need flexible establishments, dynamic and wireless connections such as military operations, healthcare systems, vehicular networks, mobile conferences, etc. Hence it is more important to estimate the trustworthiness of moving devices. In this research, we have proposed a model to improve a trusted routing in mobile ad-hoc networks by identifying malicious nodes. The proposed system uses Reinforcement Learning (RL) agent that learns to detect malicious nodes. The work focuses on a MANET with Ad-hoc On-demand Distance Vector (AODV) Protocol. Most of the systems were developed with the assumption of a small network with limited number of neighbours. But with the introduction of reinforcement learning concepts this work tries to minimize those limitations. The main objective of the research is to introduce a new model which has the capability to detect malicious nodes that decrease the performance of a MANET significantly. The malicious behaviour is simulated with black holes that move randomly across the network. After identifying the technology stack and concepts of RL, system design was designed and the implementation was carried out. Then tests were performed and defects and further improvements were identified. The research deliverables concluded that the proposed model arranges for highly accurate and reliable trust improvement by detecting malicious nodes in a dynamic MANET environment.
It is hard to set up an end-to-end connection between source and destination in Opportunistic Networks, due to dynamic network topology and the lack of infrastructure. Instead, the store-carry-forward mechanism is used to achieve communication. Namely, communication in Opportunistic Networks relies on the cooperation among nodes. Correspondingly, Opportunistic Networks have some issues like long delays, packet loss and so on, which lead to many challenges in Opportunistic Networks. However, malicious nodes do not follow the routing rules, or refuse to cooperate with benign nodes. Some misbehaviors like black-hole attack, gray-hole attack may arbitrarily bloat their delivery competency to intercept and drop data. Selfishness in Opportunistic Networks will also drop some data from other nodes. These misbehaviors will seriously affect network performance like the delivery success ratio. In this paper, we design a Trust-based Routing Protocol (TRP), combined with various utility algorithms, to more comprehensively evaluate the competency of a candidate node and effectively reduce negative effects by malicious nodes. In simulation, we compare TRP with other protocols, and shows that our protocol is effective for misbehaviors.
Mobile ad hoc networking (MANET) has been most popular research area for last decade. In MANET node (mobile node) is communicate with each other over wireless link where all nodes behave like both as host and router. In comparison with wired networks, mobile network is more vulnerable to security threat because of no centralized administration. One of the momentous routing protocols used in MANET is AODV (Ad hoc On demand Distance Vector) protocol. The Ad hoc On demand Distance Vector (AODV) protocol is compromised with its security by a various types of attacks due to malicious nodes present in the network. A hybrid approach is given for intrusion detection by removing malicious nodes during the route discovery process. The proposed approach increases the network performance in terms of PDR, throughput and end to end delay and security also.
Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.
Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.
Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.
The recent trend of mobile ad hoc network increases the ability and impregnability of communication between the mobile nodes. Mobile ad Hoc networks are completely free from pre-existing infrastructure or authentication point so that all the present mobile nodes which are want to communicate with each other immediately form the topology and initiates the request for data packets to send or receive. For the security perspective, communication between mobile nodes via wireless links make these networks more susceptible to internal or external attacks because any one can join and move the network at any time. In general, Packet dropping attack through the malicious node (s) is one of the possible attack in the mobile ad hoc network. This paper emphasized to develop an intrusion detection system using fuzzy Logic to detect the packet dropping attack from the mobile ad hoc networks and also remove the malicious nodes in order to save the resources of mobile nodes. For the implementation point of view Qualnet simulator 6.1 and Mamdani fuzzy inference system are used to analyze the results. Simulation results show that our system is more capable to detect the dropping attacks with high positive rate and low false positive.