A survey of security issues in wireless sensor networks <s> INTRUSION DETECTION IN WSNS <s> Distributed Sensor Networks (DSNs) are ad-hoc mobile networks that include sensor nodes with limited computation and communication capabilities. DSNs are dynamic in the sense that they allow addition and deletion of sensor nodes after deployment to grow the network or replace failing and unreliable nodes. DSNs may be deployed in hostile areas where communication is monitored and nodes are subject to capture and surreptitious use by an adversary. Hence DSNs require cryptographic protection of communications, sensor-capture detection, key revocation and sensor disabling. In this paper, we present a key-management scheme designed to satisfy both operational and security requirements of DSNs. The scheme includes selective distribution and revocation of keys to sensor nodes as well as node re-keying without substantial computation and communication capabilities. It relies on probabilistic key sharing among the nodes of a random graph and uses simple protocols for shared-key discovery and path-key establishment, and for key revocation, re-keying, and incremental addition of nodes. The security and network connectivity characteristics supported by the key-management scheme are discussed and simulation experiments presented. <s> BIB001 </s> A survey of security issues in wireless sensor networks <s> INTRUSION DETECTION IN WSNS <s> This paper describes an INtrusion-tolerant routing protocol for wireless SEnsor NetworkS (INSENS). INSENS securely and efficiently constructs tree-structured routing for wireless sensor networks (WSNs). The key objective of an INSENS network is to tolerate damage caused by an intruder who has compromised deployed sensor nodes and is intent on injecting, modifying, or blocking packets. To limit or localize the damage caused by such an intruder, INSENS incorporates distributed lightweight security mechanisms, including efficient one-way hash chains and nested keyed message authentication codes that defend against wormhole attacks, as well as multipath routing. Adapting to WSN characteristics, the design of INSENS also pushes complexity away from resource-poor sensor nodes towards resource-rich base stations. An enhanced single-phase version of INSENS scales to large networks, integrates bidirectional verification to defend against rushing attacks, accommodates multipath routing to multiple base stations, enables secure joining/leaving, and incorporates a novel pairwise key setup scheme based on transitory global keys that is more resilient than LEAP. Simulation results are presented to demonstrate and assess the tolerance of INSENS to various attacks launched by an adversary. A prototype implementation of INSENS over a network of MICA2 motes is presented to evaluate the cost incurred. <s> BIB002 </s> A survey of security issues in wireless sensor networks <s> INTRUSION DETECTION IN WSNS <s> This paper evaluates the performance of INSENS, an INtrusion-tolerant routing protocol for wireless SEnsor Networks. Security in sensor networks is important in battlefield monitoring and home security applications to prevent intruders from eavesdropping, from tampering with sensor data, and from launching denial-of-service (DOS) attacks against the entire network. The resilience of INSENS's multipath performance against various forms of communication-based attacks by intruders is evaluated in simulation. Within the context of INSENS, the paper evaluates implementations on the motes of the RC5 and AES encryption standards, an RC5-based scheme to generate message authentication codes (MACs), and an RC5-based generation of one-way sequence numbers. <s> BIB003 </s> A survey of security issues in wireless sensor networks <s> INTRUSION DETECTION IN WSNS <s> In sensor networks, nodes may malfunction due to the hostile environment. Therefore, dealing with node failure is a very important research issue. In this paper, we study distributed cooperative failure detection techniques. In the proposed techniques, the nodes around a suspected node collaborate with each other to reach an agreement on whether the suspect is faulty or malicious. We first formalize the problem as how to construct a dominating tree to cover all the neighbors of the suspect and give the lower bound of the message complexity. Two tree-based propagation collection protocols are proposed to construct dominating trees and collect information via the tree structure. Instead of using the traditional flooding technique, we propose a coverage-based heuristic to improve the system performance. Theoretical analysis and simulation results show that the heuristic can help achieve a higher tree coverage with lower message complexity, lower delay and lower energy consumption. <s> BIB004
• Every node shares a master secret key with the base station. • Each node knows its one-hop neighbors and has established a pairwise key with each of them. • A node can establish a pairwise key with another node that is multiple hops away if needed. Further, IHOP also assumes that the base station has a mechanism to authenticate broadcast messages (e.g., µTES-LA). A cluster head collects information from its members and sends a report to the base station only when at least t + 1 sensors observe the same result. Meanwhile, a cluster head also collects the message authentication codes (MACs) from detecting nodes. Each detecting node sends two MACs to the cluster head: a MAC using the key shared with the base station, referred to as the individual MAC, and a MAC using the key shared with its upper associate nodes, referred to as the pairwise MAC. The cluster head then compresses the t + 1 individual MACs by XORing them to reduces the size of a report. However, the pairwise MACs are not compressed for transmission. If they were, a node relaying the message would not be able to extract the pairwise MACs of interest to it. Thus, a legitimate report includes t + 1 pairwise MACs and a compressed MAC for the base station. When an intermediate node receives a report, it verifies the MAC of its lower associate node. If it fails, the report is eliminated. Otherwise, it removes the MAC, generates a new MAC using its upper associate node pairwise key, and appends it to the report. IHOP ensures that the base station can detect false data packets when no more than t nodes are compromised. However, the article does not show how to select the parameter t for a sensor network. Ye et al. proposed a statistical en-route filtering (SEF) mechanism that can detect and drop false data  . SEF uses a similar key assignment scheme as the basic random key scheme presented in BIB001 . There is a global key pool and each sensor is pre-installed with a partition selected from the pool. When a stimulus occurs in the fields, the sensors detecting this event elect one of the nodes as the center of stimulus (CoS), a node which collects and summarizes the detection results from all detecting nodes and produces a synthesized report on behalf of the group. The CoS generates the report and broadcasts the report to all detecting nodes. If a detecting node agrees with the report, it generates a MAC using a key in its partition and sends the MAC to the CoS. The CoS reports the stimulus to the base station only if it receives adequate MACs. A legitimate report carries multiple MACs and a single compromised node cannot fake all MACs. When an en-route node receives the report, it verifies the correctness of the MACs probabilistically and drops those with invalid MACs immediately. Finally, if a report reaches the base station, the base station checks all the MACs and filters out any remaining false reports that escaped the en-route filtering. When a stimulus appears, multiple nodes that detect it collaborate to process the signal and elect the CoS based on the sensing signal strength. The node with the strongest signal stands out as the CoS. To reduce the communication overhead, SEF further uses a Bloom filter  to reduce MAC sizes. SEF is designed to protect against injected false information and cannot defend against selective forwarding attacks. Deng et al. proposed an intrusion-tolerant routing in wireless sensor networks (INSENS) in BIB002 and they further evaluated its performance in BIB003 . INSENS is a proactive routing protocol. The sensors collect local topology information and send this information back to the base station. The base station generates a forwarding table based on the collected information and sends the routing table to the corresponding sensors. The base station is the central control point for calculating the routing table, which relieves the computation load of individual sensors. Protecting against intrusions focuses on three attacks: DoS-type attacks, routing attacks, and select forwarding attacks. To protect against DoStype attacks, only the base station is allowed to broadcast to the entire network and individual sensors can only send unicast messages. INSENS requires some broadcast authentication scheme such as µTESLA. Although a compromised node may still alter a valid message and broadcast that message to its neighbors, the damage is restricted to only nearby nodes and the downstream nodes. To protect against routing attacks which propagate erroneous control packets, a symmetric key is chosen for confidentiality and authentication. Further, to protect against select forwarding attacks, data are sent to base stations along two separate paths which are calculated by the base stations in the route discovery step. However, INSENS is built on a table based routing protocol, and as such depends on the base stations to collect all needed topology information to calculate the forwarding table for each individual sensor. Thus, INSENS is not scalable in large sensor networks. Wang et al. proposed a scheme to detect whether a node is faulty or malicious with the collaboration of neighbor nodes BIB004 . In the proposed scheme, when a node suspects that one of its neighbors is faulty, it sends out messages to request opinions on the behavior of this suspected node from other neighbors of the suspect. After collecting the results, the node analyzes the results to diagnose whether the suspect has a fault. The authors formalized the problem as how to construct a dominating tree to cover all the neighbors of the suspect and further proposed two tree-based propagation collection protocols to construct a dominating tree and collect information via the tree structure.