Biblio

Underwater wireless sensor network(UWSN) is an emerging technology for exploring and research inside the ocean. Since it is somehow similar to the normal wireless network, which uses radio signals for communication purposes, while UWSN uses acoustic for communication between nodes inside the ocean and sink nodes. Due to unattended areas and the vulnerability of acoustic medium, UWNS are more prone to various malicious attacks like Sybil attack, Black-hole attack, Wormhole attack, etc. This paper analyzes blackhole attacks in UWSN and proposes an algorithm to mitigate blackhole attacks by forming clusters of nodes and selecting coordinator nodes from each cluster to identify the presence of blackholes in its cluster. We used public-key cryptography and the challenge-response method to authenticate and verify nodes.

The internet of things (IoT) is the popular wireless network for data collection applications. The IoT networks are deployed in dense or sparse architectures, out of which the dense networks are vastly popular as these are capable of gathering the huge volumes of data. The collected data is analyzed using the historical or continuous analytical systems, which uses the back testing or time-series analytics to observe the desired patterns from the target data. The lost or bad interval data always carries the high probability to misguide the analysis reports. The data is lost due to a variety of reasons, out of which the most popular ones are associated with the node failures and connectivity holes, which occurs due to physical damage, software malfunctioning, blackhole/wormhole attacks, route poisoning, etc. In this paper, the work is carried on the new routing scheme for the IoTs to avoid the connectivity holes, which analyzes the activity of wireless nodes and takes the appropriate actions when required.

Low power and lossy network (LLN) comprising of constrained devices like sensors and RFIDs, is a major component in the Internet of Things (IoT) environment as these devices provide global connectivity to physical devices or “Things”. LLNs are tied to the Internet or any High Performance Computing environment via an adaptation layer called 6LoWPAN (IPv6 over Low power Personal Area Network). The routing protocol used by 6LoWPAN is RPL (IPv6 Routing Protocol over LLN). Like many other routing protocols, RPL is susceptible to blackhole attacks which cause topological isolation for a subset of nodes in the LLN. A malicious node instigating the blackhole attack drops received packets from nodes in its subtree which it is supposed to forward. Thus, the malicious node successfully isolates nodes in its subtree from the rest of the network. In this paper, we propose an algorithm based on the concept of exponential smoothing to detect the topological isolation of nodes due to blackhole attack. Exponential smoothing is a technique for smoothing time series data using the exponential window function and is used for short, medium and long term forecasting. In our proposed algorithm, exponential smoothing is used to estimate the next arrival time of packets at the sink node from every other node in the LLN. Using this estimation, the algorithm is designed to identify the malicious nodes instigating blackhole attack in real time.

Mobile Ad hoc Network (MANET for short) is a new art of wireless technology that connect a group of mobile nodes in a dynamically decentralized fashion without the need of a base station, or a centralized administration, whereas each mobile node can work as a router. MANET topology changes frequently, because of the MANET dynamically formation nature, and freely to move randomly. MANET can function as standalone or can be connected to external networks. Mobile nodes are characterized with minimal human interaction, weight, less memory, and power. Despite all the pros of MANET and the widely spreading in many and critical industries, MANET has some cons and suffers from severe security issues. In this survey we emphasize on the different types of attacks at MANET protocol stack, and show how MANET is vulnerable to those attacks.

In this work we utilize a Reputation Routing Model (RRM), which we developed in an earlier work, to mitigate the impact of three different control message based blackhole attacks in Optimized Link State Routing (OLSR) for Mobile Ad Hoc Networks (MANETs). A malicious node can potentially introduce three types of blackhole attacks on OLSR, namely TC-Blackhole attack, HELLO-Blackhole attack and TC-HELLO-Blackhole attack, by modifying its TC and HELLO messages with false information and disseminating them in the network in order to fake its advertisement. This results in node(s) diverting their messages toward the malicious node, therefore posing great security risks. Our solution reduces the risk posed by such bad nodes in the network and tries to isolate such links by feeding correct link state information to OLSR. We evaluate the performance of our model by emulating network scenarios on Common Open Research Emulator (CORE) for static as well as dynamic topologies. From our findings, it is observed that our model diminishes the effect of all three blackhole attacks on OLSR protocol in terms of packet delivery rates, especially at static and low mobility.

A mobile ad hoc network (MANET) is vulnerable to many types of attacks. Thus, security has turned out to be an important factor to facilitate secured communication between mobile nodes in a wireless environment. In this paper we propose a new approach to provide reliable and secure data transmission in MANETs under possible blackhole attacks based on ad hoc on-demand multipath distance vector (AOMDV) protocol and homomorphic encryption scheme for security. The performance of the proposed scheme is stable but that of AOMDV is found to be degrading with the intrusion of malicious nodes in the network. Simulation results show the improvement of packet delivery ratio and network throughput in the presence of blackhole nodes in our proposed scheme.