On Security Aspects of IEEE 802.15.4/ZigBee WSs. A Literature Review Daniel de Jager Academy for Computer Science and Software Engineering, University of Johannesburg, South Africa, DanielDeJ@Discovery.co.za
Abstract. This paper addresses the importance of information security in the context of the integrity, availability and confidentiality problems encountered in wireless sensors (WSs) and wireless sensor networks (WSN) based on protocol related vulnerabilities or weaknesses and the associate attacks of each layer of the IEEE 802.15.4/ZigBee standard and attempts to relate it to a recent deployment of a point-to-point based wireless sensor network in a private SouthAfrican conservation game farm, attempting to use the wireless technology in order to detect poachers entering the restricted areas of the game farm, monitoring Rhino movement and to prevent Rhino deaths by implementing a proactive response in the form of first responders. A thorough background of the IEEE 802.15.4/ZigBee protocol is discussed in the first section of this paper, followed by a literature review of important security research being conducted addressing the weaknesses discovered in the protocol stack of IEEE 802.15.4/ZigBee. We argue that physical tampering of WSs makes more sense in the context of the African Bush, than attacking the protocol stack of IEEE 802.15.4, given that game rangers will not be able to detect the tampering attempts and that jamming radio frequencies, once a Rhino horn has been removed, is a feasible attack vector, in order to prevent tracking.
Keywords: WSs, Wireless Sensor Networks, Security, IEEE 802.15.4, ZigBee
1.
Introduction
“There has never been a better time to save the Rhinos” (Cisco, 2016). This, according to Cisco’s Connected Conservation efforts would help ensure the survival of the Rhino species for generations still come. A wide array of wireless sensing technology, which includes motion detection and thermal detection, including installation of WS in the horns of Rhino’s, in order to monitor their movement, has been deployed to a Rhino Conservation Park, in an attempt to thwart poaching activities. Most will agree that saving or protecting endangered species matters and for this reason, that the topic of Security of WSs is an important one, since failure of the wireless sensor technology, as result of security issues will result many more deaths of this protected species. The paper is organised as follows. Section 1 provides a background to the IEEE802.15.4/ZigBee protocol and explains the fundamentals of WSs (WS) and
Wireless Sensor Networks (WSN), and delves into the some mechanics of the Physical and Media Access Control Layers of IEEE 802.15.4/ZigBee. Section 2 presents the Security issues present today in modern WS and WSNs, using the categories of Tennina, Koubâa, Daidone, Alves, Jurčík, Severino, Tiloca, Hauer, Pereira, Dini, Bouroche (2013) to identify the protocol layer and then moving into specific flaws and weaknesses of each protocol layer. Not each and every attack on the protocol stack is discussed, due to constraints of the research topic specified, however the most documented attacks will be described, the current research underway to solve the security problems identified, and a critical evaluation of the literature is also presented. This paper is then concluded in the final section.
2.
Wireless Sensor Networks
2.1
Background
WSs (WS) are synonymous with the Internet of Things, a broad term for small connected devices which serves some purpose through remote data collection and processing. They are small, low cost, low powered and low resourced networked measurement instruments used in a variety of applications, from Military, Commercial, Conservation to Industrial Systems (Sohraby, Minoli, Znati, 2007). WSs can then be connected to a transportation medium and then effectively becomes Wireless Sensor Networks (WSN). Sohraby et al. (2007) describes two main categories of WSN, Namely Category 1, which are mesh based dynamic routing systems, and then Category 2, which are point to point or multipoint to point static routing systems. Inherently, data collected by WS are transported to a sink, which is a data collection node in a WSN. Both Category 1 and Category 2 WSNs are depicted in Figure 1 and Figure 2 respectively.
Figure 1 Typical Category 1 WSN Deployment. Source: Sohraby et al. (2007)
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Figure 2 Typical Category 2 WSN Deployment. Source: Sohraby et al. (2007) The South-African Bush is dense with flora of all sorts. Point-to-Point WSNs deployed on game farms, Category 1 presented above, present the perfect solution to detect any intruders, since there isn’t always line of sight available for microwave based communication, nor is there enough financial resources to implement a wired solution. Also with the potential size of the area that must be monitored, it does not make sense to deploy a Category 2, since there is no guarantee that two Rhino’s will be standing next to one another indefinitely making routing of sensor data problematic. There is then a requirement that Terrestrial Routers are placed in strategic areas, such as watering holes and grazing areas that are frequently visited. However, WSs have energy constraints, some being battery powered. In the following section we review this limitation. 2.2
Energy Limitations of WSs
Energy efficiency of a Wireless Sensor is one of the major constraints presented by Wireless Sensor Technology. There are a vast amount of literature dealing with protocols and methods to improve energy consumption during the operational use of WSs and during the transmission of data. That is to say, WSs have a set lifetime, and one of the objectives of efficient design of WSs is the ability to extend the lifetime of the sensor (Rault, Bouabdallah, Challal, 2014). Rault et al. (2014), presents a classification of energy saving measures which includes data reduction, radio optimisation, sleep/wakeup schemes, energy-efficient routing and battery repletion. One of the main contributors to save energy is the use of low-cost protocols. For this reason, the Institute of Electrical and Electronic Engineers (IEEE) developed a standard for Low-Rate Personal Area Networks (LR-PAN), known as IEEE 802.15.4, to provide optimised Media Access Control and Physical Layer protocols, for power savings through low data rate specifications. This protocol has also been extended to include, layers above the Data Link, with a protocol referred to as ZigBee, working on the network and application layer, which provides high reliability, low cost and lower consumption. IEEE 802.15.4/ZigBee can cater for 65 000 nodes with data transmission rates from 20 Kbps – 250 Kbps over a maximum of 100 meters, and a two year battery lifetime.
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From a South-African weather point of view, it makes sense to use solar powered WSs. South-Africa has a mild to warm climate in the winter months and temperatures can be extremely warm in the summer months. In the next section, we will review the IEEE 802.15.4 and ZigBee specifications in light of the Open Systems Interconnect (OSI) Model, for a basic understanding of how the protocols operate. 2.3
IEEE 802.15.4 and ZigBee
The ZigBee Alliance is a collection of over 400 companies, which include prominent global organisations such as Samsung, LG, General Electric, Bosch and Phillips, to name a few, who are responsible for the creation of an open standard, for a low-power wireless sensor protocol known as ZigBee, which can connect a wide array of devices for applications in Smart Homes, Smart Grids and Retail Applications (ZigBee, 2016). Tennina et al. (2013) presents the architecture of IEEE 802.15.4/ZigBee. In Figure 3, we have derived a model from the architecture in order to compare the OSI Model to the IEEE 802.15.4/ZigBee protocol. The IEEE 802.15.4 protocol is only concerned around the services provided by Physical Layer and the Media Access Control Layer. All layers above the Media Access Control (MAC) Layer are defined by ZigBee. Also of note, is that a security mechanism is implemented as AES-128 on the Media Access Control Layer which we will elaborate on in Section 2.3.3. First we provide a high level background of the physical and MAC layer as described by Tennina et al (2013).
OSI Model
IEEE 802.15.4/ZigBee
Application Presentation
Application Layer
Session Transport Network Data Link Physical
Network Layer Media Access Control Physical
Security
Figure 3 IEEE 802.15.4/ZigBee Protocol Stack 2.3.1
Physical Layer
The Physical Layers’ primary function is to manage the radio transceiver and to provide information about signals to the upper stacks of the protocol. Five Physical Layer functions are described by Tennina et al. (2013).
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Services
Activation and Deactivation of Radio Transmitter
Energy Detection
Link Quality Indication
Clear Channel Assessment
Channel Frequency Selection
Description
A remote or physical shutdown or startup of the radio transmitter can be requested from the upper layer stack, in order to switch on or off the radio transceiver. In order to determine which radio channels are active or inactive, the Network Layer requires this information to select a channel to use. The Physical Layer is responsible to measure the quality or strength of a signal Using the Energy Detection function, a Clear Channel Assessment can be performed using one of three techniques. Higher Layers of the protocol stack may request that different channels are used for transmission. The physical layer must be able to tune the transceiver.
Table 1 Physical Layer Services 2.3.2
Media Access Control Layer
As with most other Wireless Protocols, the Media Access Control (MAC) layer supports the frames that are to be sent and received. IEEE 802.15.4 defines two modes of operation namely a beacon enabled and non-beacon enabled modes, which are supported by slotted and unslotted CSMA/CA respectively (Tennina et al., 2013). 2.3.3
Security within the Media Access Control Layer
Tennina et al. (2013) describes several modifications or amendments that have been made to the original IEEE 802.15.4 protocol, which includes three security elements based on Advanced Encryption Standard (AES) 128-bit symmetric cryptography. These elements include the addition of an Auxiliary Security Header (ASH), security modes and lastly security procedures. The ASH contains all the necessary information to be able to process security content. Security Modes includes options for authentication and encryption or authentication only or encryption only. Most importantly, IEEE 802.15.4 has the following security procedures implemented:  
Outgoing Frame Security Procedure Incoming Frame Security Procedure
These procedures, when enabled, checks parameters such as Security Levels, Frame Length, Physical Packet Size and Frame Counters for consistency. If any irregularities are detected Error Statuses are returned describing the issue.
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Enabling Security will have a performance impact in terms of processing overheads as well as communication overheads as a result of the limitations described in Section 2.2, in that more energy is consumed, affecting the lifetime of a wireless sensor, and this can lead to interesting attack scenarios that can be used as a basis for Denial of Service. In Section 3 of this paper, we have provided a solid background on the use, characteristics and elements of WSs, the IEEE 802.15.4/ZigBee protocol and the security mechanisms provided in the MAC Layer. 2.3.4
TinyOS
One of the most popular “operating systems� used for sensor technologies, is TinyOS, a de facto standard as stated by Sohraby et al. (2007). Sohraby et al. (2007) further elaborates that the operating system is a micro threaded system which guarantees concurrent data flows between hardware devices and provides little overhead as a result of memory and processing limitations of sensors in general. For the purposes of this paper, we only mention that TinyOS has vulnerabilities, but do not form part of the research topic and has been only mentioned for background purposes. In the next section we provide a Literature Review of research done on Security Weaknesses of WSs, based on recent literature up to 2016, and identify and present the current and key areas of research underway.
3.
Security aspects of WNS
Lopez, Roman and Alcaraz (2009) provide an excellent overview of security threats, and security requirements of WSs, and provided a category of threats that are faced by WSs. These categories are: 1. 2. 3. 4. 5. 6.
Common Attacks Denial of Service Node Compromise Side-Channel Attacks Impersonation Attacks Protocol Specific Attacks
The categories are well organised, however, there is a lack of specifics in their paper in terms of the attacks that can be executed. However for the purposes of this research paper, we are only interested in protocol related threats and attacks. One must also consider that the categories overlap in some form, since we argue that protocol layer attacks include for example Denial of Service Attacks. Kavitha and Sridharan (2010) paper describes a taxonomy scheme based on the protocol stack which is summarised in Table 2. This taxonomy provides far more detail, than just the categories provided by Lopez et al. (2009). In section 3.1 we highlight the key concepts behind some of the attacks as well as current research for controls which might mitigate the risks of Confidentiality, Availability and Integrity listed in Table .2.
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Physical Layer Jamming
Radio Interference Tampering or Destruction
Data Link Layer Continuous Channel Access Collision Unfairness
Network Layer Sinkhole
Hello Flood Node Capture
Transport Layer Flooding
Application Layer Overwhelm Attack
Desynchronisation
Path Based DOS Attack Deluge Attack
Interrogation
Selective Forwarding Sybil Attack Sybil Attack Wormhole attack Spoofed, Altered, Replayed Routing Information Acknowledgement Spoofing Misdirection Smurf Attack Homing Table 2 Taxonomy of Attacks based on the Protocol Layer. Source: (Kavitha, Sridharan, 2010)
3.
Discussion of Attacks
3.1
Physical Layer Attacks
The physical layer is concerned with radio operations. Table 2 describes Jamming, Radio Interference and Tampering/Destruction of the WSs ability to function appropriately to receive data. The Physical layer therefore is affected by Availability issues either permanently or intermittently. 3.1.1
Jamming and Radio Interference
Bartariya and Rastogi (2016) indicate that Jamming and Radio Interference can be avoided through the use of frequency hopping, and code spreading as well as error correcting codes. Kavitha and Sridharan (2010) only mention Frequency Hopping. Chawla, Kaur, Kaur (2016) indicates that an effective control is for the node to switch to sleep mode until the Jamming has ended. Kavitha and Sridharan (2010) also indicate that Symmetric Key Algorithms can deal with Radio Interference. Both these approaches are flawed. Frequency hopping is commonly known as Frequency Spread Spectrum, and is a common implementation of traditional 802.11 networks today. Current research being undertaken, also in light of an updated version of the 802.15.4 protocol, known as IEEE 802.15.4e-2012, also introduces the concept of Time Slotted Channel Hopping (TSCH) as well as Frequency Hopping Spread Spectrum (FHSS) (Gomes, Watteyne, Gosh, Krishnamachari, 2016). Criticism of Kavitha and Sridharan (2010) work on radio interference is that, Radio Interference for example, what you hear when GSM signals are picked up by a hi-fi speaker, is inherently a form of signal jamming., Kavita and Sridharan (2010) did not include references for Radio Interference from their referenced paper in Saxon (2007), which is also not referenced to a source. Therefore Radio Interference and Jamming is
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one and the same issue since radio must be interfered with in order to jam signals. Panic, Stecklina and Stemenkovic (2016) agrees with our conclusion on this matter. Putting a node to sleep as Chawla et al. (2016) suggests, is not an effective preventative mechanism to counteracting jamming, since the requirement for a Wireless Sensor Node is to be resilient, even when under attack. Lastly, in terms of using symmetric key’s to “handle” radio interference is flawed, since it is not confidentiality that is the issue, but rather availability of radio signal or ability to pick up radio. Symmetric keys will not be able to “handle” radio interference. Physically tampering with a WSs can reveal sensitive information. Chawla et al. (2016) provides the best solution, and that is of a tamper-proof node i.e. cannot open or break a wireless sensor node. Given the context of poachers in the African context, the risks of physical tampering of WSs as well as jamming radios is feasible. There is no doubt that organised crime will make jamming technology available if required. 3.1.2
Data Link Layer Attacks
As indicated in the previous sections, the data-link layer is responsible for the transportation of frames. One specific attack referred to as the Sybil Attack which we discuss in the following section. 3.1.2.1 Sybil Attacks Khanderiya and Panchal (2016) describes three aspects of the Sybil Attack, however all three aspects of the Sybil Attack includes introducing a malicious node into a wireless sensor network. Therefore the issue at hand is trust, in that, during a Sybil Attack, nodes to not verify the identity of the malicious nodes introduced into the network. Based on the literature, most strategies include a detection mechanism in order to detect the presence of a Sybil Node in a network, one of which is what is known as Received Signal Strength Indicator (RSSI). All nodes in a wireless sensor network participate in the calculation of certain ratio’s which indicates the presence of a malicious node. Although it is indicated that this detection scheme is fairly stable, it can still be improved upon (Marian, Mircea, 2015). Khanderiya and Panchal (2016) expanded on the original idea of Marian and Mircea (2015), and included not one, but two phases of detection and only require a single node to detect a potential Sybil attack as proved in a simulation using NS2. As with Khanderiya and Panchal (2016), Jan, Nanda, He and Liu (2016) also proposes a two phase detection system, however, energy efficiency evaluation in the final check plays a crucial role as a determinant to calculate which node in the network will make the final decision of a false or true positive as well as centricity of a cluster head. Both approaches by Khanderiya and Panchal (2016) and Jan, Nanda, He and Liu (2016), incorporates a two stage detection mechanism, and both keep energy efficiency in mind.
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However, even as Jan et al. (2016) admits, the selection of the final node is in the second detection phase is complicated and a large overhead is caused. Conversely, Khanderiya and Panchal (2016) might have to perform more empirical testing in their model as to mathematically prove, that the most energy efficient design has been achieved. Unless organised crime has access to advanced tools to exploit a WSN in the African Bush, the likelihood of observing a Sybil attack is low. Also, given the complexity of implementing fake nodes is far more difficult than physically searching and tampering with existing nodes.
3.1.3
Network Layer
The network layer is predominantly responsible for the selection of routing strategies in any network. Depending on the network architecture of wireless sensor networks, routing strategies include data centric routing, hierarchical and location based routing (Sohraby et al., 2007). The objective of routing based attacks on WSs is primarily to alter the flow of packet data and research has been primarily on energy efficient routing protocol implementation (Bartariya and Rastogi, 2016). It might be possible to introduce malicious nodes in a wireless sensor network in order fool legitimate nodes to forward their packets to the malicious node, on the basis that the malicious node is in fact the next hop, in order to send packets to the sink in both single path and multipath routing strategies. This strategy might be feasible in the African bush, since using heat detection sensors and limiting their ability to transmit the detection of a poacher to a control station, will give enough time to poach and escape. 3.1.3.1 Sinkhole Attacks In Figure 4 below, we show that a malicious node, 7, fools other nodes to think that the shortest path to the sink is achieved by routing through itself. The black node compromised routing in the figure to the right. Prajapati and Dubey (2016) literature survey included the following preventative measures that have been proposed:
Figure 4 Sinkhole Source: (Prajapati, Dubey, 2016).
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Approach
Publication Year
Data Consistency and Network Flow Information
2006
Hop Count Monitoring Scheme
2007
RSSI Based Scheme
2009
Monitoring CPU Usage
Node’s
2010
Mobile Agent Approach
Based
2011
Use Message Algorithm
Digest
2011
a
Concept A community flow graph is generated by a base station in order to identify the sinkhole 96% detection rate calculated by implementing a routing protocol that dynamically keeps a hop count parameter. Uses four Extra Monitor Nodes in order to determine the position of nodes relative to the base station. By monitoring the CPU Utilisation of each node by a base station, it is possible to distinguish between a legitimate and malicious node. A Mobile Agent is employed to acquire knowledge about every node mindful of the complete community, so that a malicious node is ignored. Utilises one-manner hash chains, which ensures data integrity of the messages being transferred.
Table 3 Literature Review on Sinkhole Attacks. Source: Prajapati and Dubey (2016)
Regardless of the strategies employed, routing security is still an energy consuming and insecure area of Wireless Networks since so many parameters are being monitored by different algorithms as indicated in Table 3. Having studied some aspects of Blockchain technology, it became apparent during this literature review, that there exists a potential application of Blockchain Technology to secure the routing tables of all nodes in a network, given that enough processing and storage resources are available on a node. Lee and Lee (2016) propose Blockchain as a mechanism to update firmware of Internet of Things devices, and have proved that a guaranteed scheme can exist to update firmware while devices are not tampered with. We also argue that the blockchain, which operates on the premise of Nakamoto’s computational puzzles i.e. proofs of work, can be applied to the paradigm of wireless sensor networks, just as with peer-to-peer bitcoin transactions, in which all nodes in a
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mesh based wireless network participates in forming the routing chain. Decentralised Routing Protocols, applied to Wireless Sensor Networks, therefore is a potential area identified for future research.
3.1.4
Transport Layer Attacks
The Transport Layer is responsible for connection orientated services such as hostto-host communication, data streaming, and flow control and multiplexing. Two types of attacks have been identified in the literature namely Flooding and Desynchronisation. 3.1.4.1 Flooding and De-Synchronisation Flooding works on the principle of exhausting available resources on a node due to a massive amount of connections being directed to a specific node, leading to a situation where no more new connections can be established by a node, since no more memory is available (Panic et al. 2016). This is achieved by not completing the TCP/IP handshake, and leaving connections “half-open”. In a de-synchronisation attack, a malicious node interrupts data transmission of a node, leading to a situation where a node is requesting re-transmission of data indefinitely (Panic et al. 2016). Raymond and Midkiff (2008) explain both scenarios, and includes SYN Cookies and Packet Authentication as mitigating controls respectively. Bernstein (no date), was the original inventor of the concept of SYN Cookies and works on the premise of calculating a TCP sequence number using a propriety or secret mathematical function in the SYN-ACK response during an initial TCP handshake, if the check is not successful a node will not create a TCP session. However, since a mathematical function is being used, there is no need to store the state of a TCP connection in a state table. There are drawbacks to using SYN Cookies on small, energy constrained WSs. They are too resource intensive and requires dedicated CPU’s and large memory, which is not available on WSs (Ferro, 2008). Abdalzaher, Seddik, Elsabrouty, Muta, Furukawa, Abdel-Rahman (2016) presents a novel approach using Game Theory to defend against both flooding and desynchronisation attacks keeping in mind the resource constraints. Their research might lead to a better trust model within a WSN in order to defend against known and unknown attacks through being aware of the interactions of WSs. 3.1.5
Application Layer Attacks
Application Layer attacks includes overwhelming network nodes with interactions to consume network bandwidth to cause a Denial of Service through this consumption or to drain battery power or it is possible to reprogram network nodes to take control of the majority of nodes in a WSN (Kavitha, Sridharan, 2010). Since we have already discussed Denial of Service, let’s consider reprogram attacks in the next section.
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3.1.5.1 Reprogram Attacks The ability to reprogram WSs is important, since in many practical implementations their might be thousands of nodes, which are remotely inaccessible, which creates the need to remotely and centrally deploy new images for WSs. TinyOS as mentioned earlier in this paper, is the main component that is vulnerable to Deluge attacks. Becher, Benenson, Dornseif (2006) describes the process of physically hacking a wireless sensor to gain access to the EEPROM, and includes steps to add additional microcontrollers to a sensor, or just removing the contents of a micro-controller to replace with ones’ own code. There are a number of existing reprogramming schemes proposed for WSs, some include a multi-hop reprogramming approach as proposed by Hulhami, Wang (2009). Although many proposals have been provided to secure the Deluge Network Programming System, the issue at hand is that the current schemes are not cryptographically strong enough (Dutta, Hui, Chu, Culler, 2006) and presents a weak protocol. Dutta, Hui, Chu, Culler (2006) argues that their proposed approach solves this issue through a concept of advertisement, in which hashing is utilised and digital signatures of the programs being updated over the air. The advertisement is an authentication and mechanism which subsequently validates each program segment being transmitted on a per message basis. Even though there are resource constraints, the method by Dutta et al. (2006) shows that the introduction of Public Key Cryptography, strong integrity verification does indeed provide for a secure and efficient design. Given the complexity of remote execution of malicious updates, it makes more sense to physically tamper with a WSs in the bush, than remotely updating WSs with malicious code. This can be easily mitigated by enabling some form of detection when a WSs is tampered with.
4.
Conclusion
Any new technology is subject to a risk assessment, as it is defined in ISO27002. Within the African conservation context, we argue that physical attacks on WSs and WSN would be more of a concern, than attacks on the upper layers of the IEEE 802.15.4/ZigBee stack. Since Rhino horns will have WSs installed, means to hide or jam the radio frequency from transmission would be key, in order to escape without capture. If used in conjunction with routing attacks, poachers might easily escape without any form of detection. However, depending on the scenario in which WSs and WSN are deployed, for example for military applications or cyber physical systems, the upper layer attacks will be a concern and the risk needs to be managed appropriately for those systems. This research paper provided a literature review of the security issues, in terms of integrity, availability and confidentiality of WSs and WSN. One area that needs more attention, would be the AES cryptography implemented on Layer 2 MAC of IEEE 802.15.4 since we have not discussed those features and how they will be able to mitigate some of the known attacks on the protocol stack.
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Lastly, a new emerging technology known as blockchain has been mentioned in the network layer section of this research paper. We argue that this area needs to be investigated for possible future research, especially in light of mesh-based WSs. Overall, WSs and WSN are still vulnerable today and the issue of resource constraints on the physical devices, is still inherently an issue, which can lead to less secure designs. References Abdalzaher, M.S., Seddik, K., Elsabrouty, M., Muta, O., Furukawa, H. and Abdel-Rahman, A., 2016. Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey. Sensors, 16(7), p.1003. Bartariya, S. and Rastogi, A., 2016. Security in Wireless Sensor Networks: Attacks and Solutions. In: International Journal of Advanced Research in Computer and Communications Engineering. Vol. 5, Issue 3. pp. 214-220. Becher, A., Benenson, Z. and Dornseif, M., 2006. Tampering with motes: Real-world physical attacks on wireless sensor networks. In: International Conference on Security in Pervasive Computing (pp. 104-118). Springer Berlin Heidelberg. Bernstein, D, J. No Date. Syn-Cookies. Available online. https://cr.yp.to/syncookies.html Accessed: 22 September 2016. Chawla, H., Kaur, H. and Kaur, C., 2016. Review on Security Issues in Wireless Sensor Networks. In: International Journal of Current Engineering and Technology. Volume 6. No. 3. Cisco. 2016. Available online. http://www.cisco.com/c/m/en_us/never-better/csr-1.html. Accessed 14 September 2016. Dutta, P., Hui, J., Chu, D., Culler, D., 2006. Securing the Deluge Network Programming System. In: Proceedings of the 5th international conference on Information processing in sensor networks. ACM. Ferro, G. 2008. TCP SYN Cookies – DDoS defense. Available online. http://etherealmind.com/tcp-syn-cookies-ddos-defence/ Accessed: 22 September 2016. Gomes, P.H., Watteyne, T., Gosh, P. and Krishnamachari, B., 2016, February. Competition: Reliability through Time slotted Channel Hopping and Flooding-based Routing. In Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks (pp. 297-298). Junction Publishing. Hulhami, S., Wang, L. 2009. Energy-efficient multi-hop reprogramming for sensor networks. In: Computer-Communication Networks. ACM Transactions on Sensor Networks. Vol.5, No.2. pp. 16:1-16:40. Jan, M.A., Nanda, P., He, X. and Liu, R.P., 2016. A Sybil attack detection scheme for a forest wildfire monitoring application. Future Generation Computer Systems.
Khanderiya, M. and Panchal, M., 2016. A Novel Approach for Detection of Sybil Attack in Wireless Sensor Networks. Available online: http://s3.amazonaws.com/academia.edu.documents/45824285/1371.pdf?AWSAccessKeyId =AKIAJ56TQJRTWSMTNPEA&Expires=1474456764&Signature=LkjtTcqSjnLhbnG2Va DGotm7a%2Bs%3D&response-contentdisposition=inline%3B%20filename%3DA_Novel_Approach_for_Detection_of_Sybil.pdf. Accessed: 21 September 2016
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Lee, B. and Lee, J.H., 2016. Blockchain-based secure firmware update for embedded devices in an Internet of Things environment. The Journal of Supercomputing, pp.1-16. Lopez, J., Roman, R. and Alcaraz, C., 2009. Analysis of security threats, requirements, technologies and standards in wireless sensor networks. In: Foundations of Security Analysis and Design V (pp. 289-338). Springer Berlin Heidelberg. Marian, S., Mircea, P., 2015, Sybil Attack Type Detection in Wireless Sensor Networks based on Received Signal Strength Indicator detection scheme, IEEE Conference Publications Prajapati, R., Dubey, R., 2016. Review on the detection of Sinkhole Attack in WSN. In: Asian Journal of Science and Technology. Vol. 7. Issue 03. pp 6553 – 2657. . Kavitha, T. and Sridharan, D., 2010. Security vulnerabilities in wireless sensor networks: A survey. Journal of information Assurance and Security, 5(1), pp.31-44. Panic, G., Stecklina, O., Stemenkovic, Z., 2016. An Embedded Sensor Node Microcontroller with Crypto-Processors. Available online. http://www.mdpi.com/1424-8220/16/5/607/htm. Accessed: 22 September 2016. Raymond, D.R. and Midkiff, S.F., 2008. Denial-of-service in wireless sensor networks: Attacks and defenses. IEEE Pervasive Computing, 7(1), pp.74-81. Rault, T., Bouabdallah, A. and Challal, Y., 2014. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, pp.104-122. Saxena, M. 2007. Security in Wireless Sensor Networks. Cerias Techn Report. Available online. https://www.cerias.purdue.edu/assets/pdf/bibtex_archive/2007-04.pdf. Accessed 19 September 2016. Sohraby, K., Minoli, D. and Znati, T., 2007. Wireless sensor networks: technology, protocols, and applications. John Wiley & Sons. Tennina, S., Koubâa, A., Daidone, R., Alves, M., Jurčík, P., Severino, R., Tiloca, M., Hauer, J.H., Pereira, N., Dini, G. and Bouroche, M., 2013. IEEE 802.15. 4 and ZigBee as enabling technologies for low-power wireless systems with quality-of-service constraints. Springer Science & Business Media. TinyOS. 2013. TinyOS Overview. TinyOS Wiki. Available online. http://tinyos.stanford.edu/tinyos-wiki/index.php/TinyOS_Overview. Accessed: 21 September 2016. ZigBee. 2016. ZigBee Alliance. Available online. http://www.zigbee.org/. Accessed: 14 September 2016.
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