A specification-based IDS for detecting attacks on RPL-based network topology

Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with corresponding statistics, will be implemented as a set of rules in the intrusion detection agents, in the form of the cluster heads propagated to monitor the whole network. In order to save resources, we set the cluster members to report related information about itself and other neighbors to the cluster head instead of making the head overhearing all the communication. As a result, information about a cluster member will be reported by different neighbors, which allow the cluster head to do cross-check. We propose to record the sequence in RPL Information Object (DIO) and Information Solicitation (DIS) messages to eliminate the synchronized issue created by the delay in transmitting the report, in which the cluster head only does cross-check on information that come from sources with the same sequence. Simulation results show that the proposed Intrusion Detection System (IDS) has a high accuracy rate in detecting RPL topology attacks, while only creating insignificant overhead (about 6.3%) that enable its scalability in large-scale network

A specification-based IDS for detecting attacks on RPL-based network topology

Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with corresponding statistics, will be implemented as a set of rules in the intrusion detection agents, in the form of the cluster heads propagated to monitor the whole network. In order to save resources, we set the cluster members to report related information about itself and other neighbors to the cluster head instead of making the head overhearing all the communication. As a result, information about a cluster member will be reported by different neighbors, which allow the cluster head to do cross-check. We propose to record the sequence in RPL Information Object (DIO) and Information Solicitation (DIS) messages to eliminate the synchronized issue created by the delay in transmitting the report, in which the cluster head only does cross-check on information that come from sources with the same sequence. Simulation results show that the proposed Intrusion Detection System (IDS) has a high accuracy rate in detecting RPL topology attacks, while only creating insignificant overhead (about 6.3%) that enable its scalability in large-scale network

Intrusion Detection System for detecting internal threats in 6LoWPAN

6LoWPAN (IPv6 over Low-power Wireless Personal Area Network) is a standard developed by the Internet Engineering Task Force group to enable the Wireless Sensor Networks to connect to the IPv6 Internet. This standard is rapidly gaining popularity for its applicability, ranging extensively from health care to environmental monitoring. Security is one of the most crucial issues that need to be considered properly in 6LoWPAN. Common 6LoWPAN security threats can come from external or internal attackers. Cryptographic techniques are helpful in protecting the external attackers from illegally joining the network. However, because the network devices are commonly not tampered-proof, the attackers can break the cryptography codes of such devices and use them to operate like an internal source. These malicious sources can create internal attacks, which may downgrade significantly network performance. Protecting the network from these internal threats has therefore become one of the centre security problems on 6LoWPAN. This thesis investigates the security issues created by the internal threats in 6LoWPAN and proposes the use of Intrusion Detection System (IDS) to deal with such threats. Our main works are to categorise the 6LoWPAN threats into two major types, and to develop two different IDSs to detect each of this type effectively. The major contributions of this thesis are summarised as below. First, we categorise the 6LoWPAN internal threats into two main types, one that focuses on compromising directly the network performance (performance-type) and the other is to manipulate the optimal topology (topology-type), to later downgrade the network service quality indirectly. In each type, we select some typical threats to implement, and assess their particular impacts on network performance as well as identify performance metrics that are sensitive in the attacked situations, in order to form the basis detection knowledge. In addition, on studying the topology-type, we propose several novel attacks towards the Routing Protocol for Low Power and Lossy network (RPL - the underlying routing protocol in 6LoWPAN), including the Rank attack, Local Repair attack and DIS attack. Second, we develop a Bayesian-based IDS to detect the performance-type internal threats by monitoring typical attacking targets such as traffic, channel or neighbour nodes. Unlike other statistical approaches, which have a limited view by just using a single metric to monitor a specific attack, our Bayesian-based IDS can judge an abnormal behaviour with a wiser view by considering of different metrics using the insightful understanding of their relations. Such wiser view helps to increase the IDS’s accuracy significantly. Third, we develop a Specification-based IDS module to detect the topology-type internal threats based on profiling the RPL operation. In detail, we generalise the observed states and transitions of RPL control messages to construct a high-level abstract of node operations through analysing the trace files of the simulations. Our profiling technique can form all of the protocol’s legal states and transitions automatically with corresponding statistic data, which is faster and easier to verify compare with other manual specification techniques. This IDS module can detect the topology-type threats quickly with a low rate of false detection. We also propose a monitoring architecture that uses techniques from modern technologies such as LTE (Long-term Evolution), cloud computing, and multiple interface sensor devices, to expand significantly the capability of the IDS in 6LoWPAN. This architecture can enable the running of both two proposed IDSs without much overhead created, to help the system to deal with most of the typical 6LoWPAN internal threats. Overall, the simulation results in Contiki Cooja prove that our two IDS modules are effective in detecting the 6LoWPAN internal threats, with the detection accuracy is ranging between 86 to 100% depends on the types of attacks, while the False Positive is also satisfactory, with under 5% for most of the attacks. We also show that the additional energy consumptions and the overhead of the solutions are at an acceptable level to be used in the 6LoWPAN environment

Exploring placement of intrusion detection systems in rpl-based internet of things

Intrusion detection is an indispensable part of RPL security due to its nature opening to attacks from insider attackers. While there are a good deal of studies that analyze different types of attack and propose intrusion detection systems based on various techniques that are proposed in the literature, how to place such intrusion detection systems on RPL topology is not investigated. This is the main contribution of this study, and three intrusion detection architectures based on central and distributed placement of intrusion detection nodes are analyzed rigorously against different types of attacks and attackers at various locations in the RPL topology and evaluated from different aspects including their effectiveness, cost, and security.Comment: 15 pages, 5 figures, research articl

Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks

Wireless Sensor Network (WSN) technology has grown in importance in recent years. All WSN implementations need secure data transmission between sensor nodes and base stations. Sensor node attacks introduce new threats to the WSN. As a result, an appropriate Intrusion Detection System (IDS) is required in WSN for defending against security attacks and detecting attacks on sensor nodes. In this study, we use the Routing Protocol for Low Power and Lossy Networks (RPL) for addressing security services in WSN by identifying IDS with a network size of more or less 20 nodes and introducing 10% malicious nodes. The method described above is used on Cooja in the VMware virtual machine Workstation with the InstantContiki2.7 operating system. To track the movement of nodes, find network attacks, and spot dropped packets during IDS in WSN, an algorithm is implemented in the Network Simulator (NS2) using the Ad-hoc On-Demand Distance Vector (AODV) protocol in the Linux operating system.Keywords—Intrusion Detection Systems, wireless sensor networks, Cooja simulator, sensor nodes, NS

Improved Intrusion Detection System using Quantal Response Equilibrium-based Game Model and Rule-based Classification

Wireless sensor network has large number of low-cost tiny nodes with sensing capability.  These provide low cost solutions to many real world problems such as such as defence, Internet of things, healthcare, environment monitoring and so on. The sensor nodes of these networks are placed in vulnerable environment. Hence, the security of these networks is very important. Intrusion Detection System (IDS) plays an important role in providing a security to such type of networks. The sensor nodes of the network have limited power and, traditional security mechanisms such as key-management, encryption decryption and authentication techniques cannot be installed on the nodes. Hence, there is a need of special security mechanism to handle the intrusions. In this paper, intrusion detection system is designed and implemented using game theory and machine learning to identify multiple attacks. Game theory is designed and used to apply the IDS optimally in WSN. The game model is designed by defining the players and the corresponding strategies. Quantal Response Equilibrium (QRE) concept of game theory is used to select the strategies in optimal way for the intrusion’s detection. Further, these intrusions are classified as denial of service attack, rank attack or selective forwarding attacks using supervised machine learning technique based on different parameters and rules. Results show that all the attacks are detected with good detection rate and the proposed approach provides optimal usage of IDS

Intrusion detection in IPv6-enabled sensor networks.

In this research, we study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks through the lens of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following the architecture of ad-hoc networks. Current state of the art IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they do not efficiently address the paradigm of ad-hoc networks, which is highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. In this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine-tune this trade-off, we model networks as Random Geometric Graphs; these are a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach that consists of a central IDS agent and set of distributed IDS agents deployed uniformly at random over the network area. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols, such as RPL. The detailed experimental evaluation conducted in this research demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes. The experiments show that our proposed IDS architecture is resilient against frequent topology changes due to node failures