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2021-11-08
Sharma, Nisha, Sharma, Manish, Sharma, Durga Prasad.  2020.  A Trust Based Scheme for Spotting Malicious Node of Wormhole in Dynamic Source Routing Protocol. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :1232–1237.
The exceptional attributes of impromptu network of being framework less, self-composed and unconstrained make the task more challenging to secure it. In mobile Ad-hoc network nodes reliant on one another for transmitting information, that make MANET helpless against different sorts of security attacks. These security attacks can be arranged as Passive and Active attacks. Wormhole is an Active attack and considered generally risky as it can make significant harm routing. Various secure routing mechanism has been created are based on cryptography mechanism, need pre-organized structure, centralized authority, or need external hardware, etc. These components are unreasonable due to restricted accessible assets in MANET. In this paper, we are proposing an effective trust-based mechanism based on the concept of Node to Node packet delay for the detection of the malevolent node of wormhole. The trust value of each node is calculated by observing the packet transaction among adjacent nodes and later this trust value is used for identification of malevolent node. Based on the trust values, further routing decisions and selecting a secured route can be perform.
2021-08-02
Sharma, Nisha, Sharma, Durga Prasad, Sharma, Manish.  2020.  Wormhole Formation and Simulation in Dynamic Source Routing Protocol using NS3. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). :318–322.
Mobile Ad hoc networks (MANET) are becoming extremely popular because of the expedient features that also make them more exposed to various kinds of security attacks. The Wormhole attack is considered to be the most unsafe attack due to its unusual pattern of tunnel creation between two malevolent nodes. In it, one malevolent node attracts all the traffic towards the tunnel and forwards it to another malevolent node at the other end of the tunnel and replays them again in the network. Once the Wormhole tunnel is created it can launch different kind of other attacks such as routing attack, packet dropping, spoofing etc. In past few years a lot of research is done for securing routing protocols. Dynamic Source Routing (DSR) protocol is considered foremost MANET routing protocols. In this paper we are forming the wormhole tunnel in which malevolent nodes use different interfaces for communication in DSR protocol. NS3 simulator is being used for the analysis of the DSR routing protocol under the wormhole attack. This paper provides better understanding of the wormhole attack in DSR protocol which can benefit further research.
2018-11-19
Rabie, R., Drissi, M..  2018.  Applying Sigmoid Filter for Detecting the Low-Rate Denial of Service Attacks. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). :450–456.

This paper focuses on optimizing the sigmoid filter for detecting Low-Rate DoS attacks. Though sigmoid filter could help for detecting the attacker, it could severely affect the network efficiency. Unlike high rate attacks, Low-Rate DoS attacks such as ``Shrew'' and ``New Shrew'' are hard to detect. Attackers choose a malicious low-rate bandwidth to exploit the TCP's congestion control window algorithm and the re-transition timeout mechanism. We simulated the attacker traffic by editing using NS3. The Sigmoid filter was used to create a threshold bandwidth filter at the router that allowed a specific bandwidth, so when traffic that exceeded the threshold occurred, it would be dropped, or it would be redirected to a honey-pot server, instead. We simulated the Sigmoid filter using MATLAB and took the attacker's and legitimate user's traffic generated by NS-3 as the input for the Sigmoid filter in the MATLAB. We run the experiment three times with different threshold values correlated to the TCP packet size. We found the probability to detect the attacker traffic as follows: the first was 25%, the second 50% and the third 60%. However, we observed a drop in legitimate user traffic with the following probabilities, respectively: 75%, 50%, and 85%.