Biblio

Underwater Wireless Sensor Networks (UWSN) has vast application areas. Due to the unprotected nature, underwater security is a prime concern. UWSN becomes vulnerable to different attacks due to malicious nodes. Sybil attack is one of the major attacks in UWSN. Most of the proposed security methods are based on encryption and decryption which consumes resources of the sensor nodes. In this paper, a simple authentication mechanism is proposed for securing the UWSN from the Sybil attack. As the nodes have very less computation power and energy resources so this work is not followed any kind of encryption and decryption technique. An authentication process is designed in such a way that node engaged in communication authenticate neighboring nodes by node ID and the data stored in the cluster head. This work is also addressed sensor node compromisation issue through Hierarchical Fuzzy System (HFS) based trust management model. The trust management model has been simulated in Xfuzzy-3.5. After the simulation conducted, the proposed trust management mechanism depicts significant performance on detecting compromised nodes.

Routing protocol for low power and lossy networks (RPL) is a fundamental routing protocol of 6LoWPAN, a centre correspondence standard for the Internet of Things. RPL outplay other wireless sensor and ad hoc routing protocols in the aspect of service (QoS), device management, and energy-saving performance. The Rank definition in RPL addresses several issues, such as path optimization, loop avoidance, and power overhead management. RPL rank and version number attacks are two types of the most common forms of RPL attacks, may have crucial ramification for RPL networks. The research directed upon these attacks includes considerable vulnerabilities and efficiency issues. The rank attack on sensor networks is perhaps the utmost common, posing a challenge to network connectivity by falling data or disrupting routing routes. This work presents a rank attack detection system focusing on RPL. Considering many of such issues a method has been proposed using spatial correlation function (SCF) and Dijkstra's algorithm considering parameters like energy and throughput.

Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.

The advancement in technologies like IoT, device-to-device communication lead to concepts like smart home and smart cities, etc. In smart home architecture, different devices such as home appliances, personal computers, surveillance cameras, etc. are connected to the Internet and enable the user to monitor and control irrespective of time and location. IPv6-enabled 6LoWPAN is a low-power, low-range communication protocol designed and developed for the short-range IoT applications. 6LoWPAN is based on IEEE 802.15.4 protocol and IPv6 network protocol for low range wireless applications. Although 6LoWPAN supports different routing protocols, RPL is the widely used routing protocol for low power and lossy networks. In this work, we have taken an IoT enabled smart home environment, in which 6LoWPAN is used as a communication and RPL as a routing protocol. The performance of this proposed network model is analyzed based on the different performance metrics such as latency, PDR, and throughput. The proposed model is simulated using Cooja simulator running over the Contiki OS. Along with the Cooja simulator, the network analyzer tool Wireshark is used to analyze the network behaviors.

Mobile Adhoc networks are wireless adhoc networks that have property of self organizing, less infrastructure, multi hoping, which are designed to work under low power vulnerable environment. Due to its very unique characteristics, there is much chances of threat of malicious nodes within the network. Blackhole attack is a menace in MANETs which redirects all traffic to itself and drops it. This paper’s objective is to analyze the effects of blackhole attack under reactive routing protocol such as Adhoc on Demand Distance Vector routing (AODV). The performance of this protocol is assessed to find the vulnerability of attack and also compared the impact of attack on both AODV, AODV with blackhole and proposed AODV protocols. The analysis is done by simulated using NS- 2.35 and QoS parameters such as Throughput, PDR, and Average Energy Consumed are measured further.

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.

The 6L0WPAN adaptation layer is widely used in many Internet of Things (IoT) and vehicular networking applications. The current IoT framework [1], which introduced 6LoWPAN to the TCP/IP model, does not specif the implementation for managing its received-fragments buffer. This paper looks into the effect of current implementations of buffer management strategies at 6LoWPAN's response in case of fragmentation-based, buffer reservation Denial of Service (DoS) attacks. The Packet Drop Rate (PDR) is used to analyze how successful the attacker is for each management technique. Our investigation uses different defence strategies, which include our implementation of the Split Buffer mechanism [2] and a modified version of this mechanism that we devise in this paper as well. In particular, we introduce dynamic calculation for the average time between consecutive fragments and the use of a list of previously dropped packets tags. NS3 is used to simulate all the implementations. Our results show that using a ``slotted'' buffer would enhance 6LoWPAN's response against these attacks. The simulations also provide an in-depth look at using scoring systems to manage buffer cleanups.