scholarly journals Threshold Based Algorithm for the Detection of DDOS Attack in Wireless Sensor Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 1869-1873
Keyword(s):  
Denial Of Service ◽  
Research Work ◽  
Industrial Applications ◽  
Wireless Sensor ◽  
Distributed Denial Of Service ◽  
Black Hole Attack ◽  
Malicious Nodes ◽  
Ddos Attack ◽  
Novel Approach ◽  
Overall Functioning

The self-configuring type of network in which the sensor node are deployed in such a manner that they can join or leave the network when they want is known as wireless sensor network. The nodes start communicating with each other in order to transmit important information within the network. As this type of network is decentralized in nature there are numerous malicious nodes which might enter the network. There are so many attacks possible on WSN, in Distributed Denial of Service (DDOS) attacks, malicious nodes adapts many attacks such as flooding attack, black hole attack and warm hole attack, to halt the overall functioning of network. The risks are even more when we talk about military and industrial applications. The DDoS is an active type of attack. When the DDoS attack occurs in the network, it minimizes the lifetime of the network and also increases the overall energy consumption of the network. In order to detect the malicious nodes from the network which cause the DDoS attack, a novel approach is to be proposed in this research work.

scholarly journals Multilevel Modeling of Distributed Denial of Service Attacks in Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Katarzyna Mazur ◽  
Bogdan Ksiezopolski ◽  
Radoslaw Nielek
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Denial Of Service ◽  
Monitoring Network ◽  
Wireless Sensor ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Service Attack

The growing popularity of wireless sensor networks increases the risk of security attacks. One of the most common and dangerous types of attack that takes place these days in any electronic society is a distributed denial of service attack. Due to the resource constraint nature of mobile sensors, DDoS attacks have become a major threat to its stability. In this paper, we established a model of a structural health monitoring network, being disturbed by one of the most common types of DDoS attacks, the flooding attack. Through a set of simulations, we explore the scope of flood-based DDoS attack problem, assessing the performance and the lifetime of the network under the attack condition. To conduct our research, we utilized the Quality of Protection Modeling Language. With the proposed approach, it was possible to examine numerous network configurations, parameters, attack options, and scenarios. The results of the carefully performed multilevel analysis allowed us to identify a new kind of DDoS attack, the delayed distributed denial of service, by the authors, referred to as DDDoS attack. Multilevel approach to DDoS attack analysis confirmed that, examining endangered environments, it is significant to take into account many characteristics at once, just to not overlook any important aspect.

Mutual authentication technique for detection of malicious nodes in wireless sensor networks

2018 ◽  
Vol 7 (2.27) ◽  
pp. 118
Author(s):  
Inderpreet Singh ◽  
Rajan Kumar
Keyword(s):  
Wireless Sensor Networks ◽  
Denial Of Service ◽  
Mutual Authentication ◽  
Research Work ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sybil Attack ◽  
Malicious Nodes ◽  
Service Type ◽  
Authentication Technique

The wireless sensor network is the decentralized type of network in which sensor nodes can join or leave the network when they want. Due to self configuring nature of network security and energy, consumption is the major issue of the network. The Sybil is the denial of service type of attack in which sensor nodes can change its identification multiple times in the network. In this research work, mutual authentication technique is proposed which detect malicious nodes from the network which is responsible to trigger Sybil attack in the network. The simulation of proposed algorithm is performed in NS2 and results shows that proposed technique performs well in terms of energy and throughput  

scholarly journals Threshold-Based Method for Detection of Distributed Denial of Service Attack in IoT

2019 ◽  
Vol 8 (4) ◽  
pp. 3002-3007
Keyword(s):  
Internet Of Things ◽  
Network Performance ◽  
Denial Of Service ◽  
Research Work ◽  
Distributed Denial Of Service ◽  
Malicious Nodes ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Malicious Node Detection ◽  
The Internet Of Things

The internet of things is the decentralized type of network in which sensor devices can join or leave the network when they want. Due to such nature of the network malicious nodes enter the network which affects network performance in terms of certain parameters. This research work is based on the detection and isolation of distributed denial of service attack in internet of things. The distributed denial of service attack is the denial of service type attack which affects network performance to large extent. In the existing techniques there are two main drawbacks. The first drawback is that the technique does not pin point malicious nodes from the network. The second drawback is that the malicious node detection time is very high. In this research, the new technique will be proposed for the isolation of malicious nodes from the network. In this technique, similarity of the traffic is analyzed using the cosine similarity. The sensor node which is generated dissimilar type of traffic is detected as malicious nodes. The proposed technique has been implemented in MATLAB and results have been analyzed in terms of certain parameters. It is expected that proposed technique detect malicious nodes in least amount of time.

scholarly journals Reducing distributed denial of service (DDoS) attacks using client puzzle mechanism

2017 ◽  
Vol 7 (1.1) ◽  
pp. 230
Author(s):  
C. Vasan Sai Krishna ◽  
Y. Bhuvana ◽  
P. Pavan Kumar ◽  
R. Murugan
Keyword(s):  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attack ◽  
Computing Power ◽  
Server Side ◽  
Ddos Attack ◽  
Client Machine ◽  
Client Side

In a typical DoS attack, the attacker tries to bring the server down. In this case, the attacker sends a lot of bogus queries to the server to consume its computing power and bandwidth. As the server’s bandwidth and computing power are always greater than attacker’s client machine, He seeks help from a group of connected computers. DDoS attack involves a lot of client machines which are hijacked by the attacker (together called as botnet). As the server handles all these requests sent by the attacker, all its resources get consumed and it cannot provide services. In this project, we are more concerned about reducing the computing power on the server side by giving the client a puzzle to solve. To prevent such attacks, we use client puzzle mechanism. In this mechanism, we introduce a client-side puzzle which demands the machine to perform tasks that require more resources (computation power). The client’s request is not directly sent to the server. Moreover, there will be an Intermediate Server to monitor all the requests that are being sent to the main server. Before the client’s request is sent to the server, it must solve a puzzle and send the answer. Intermediate Server is used to validate the answer and give access to the client or block the client from accessing the server.

scholarly journals A swift unrest horde system for curtail SDO hit in cloud computing

2018 ◽  
Vol 7 (1.7) ◽  
pp. 156
Author(s):  
S Ravikumar ◽  
E Kannan
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Water Drop ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Motion System ◽  
Novel Approach

One of the immense risk to benefit accessibility in distributed computing is Distributed Denial of Service. Here a novel approach has been proposed to limit SDO [Strewn Defiance of Overhaul] assaults. This has been wanted to accomplish by a canny quick motion horde organize. An astute horde arrange is required to guarantee independent coordination and portion of horde hubs to play out its handing-off tasks. Clever Water Drop calculation has been adjusted for appropriated and parallel advancement. The quick motion system was utilized to keep up availability between horde hubs, customers, and servers. We have intended to reproduce this as programming comprising of different customer hubs and horde hubs

Cluster-Based Countermeasures for DDoS Attacks

2016 ◽  
pp. 206-227
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Dileep Kumar G
Keyword(s):  
Unsupervised Learning ◽  
Network Traffic ◽  
Denial Of Service ◽  
Security Threats ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Real Progress ◽  
Bandwidth Capacity

Distributed Denial of Service (DDoS) attack is considered one of the major security threats in the current Internet. Although many solutions have been suggested for the DDoS defense, real progress in fighting those attacks is still missing. In this chapter, the authors analyze and experiment with cluster-based filtering for DDoS defense. In cluster-based filtering, unsupervised learning is used to create profile of the network traffic. Then the profiled traffic is passed through the filters of different capacity to the servers. After applying this mechanism, the legitimate traffic will get better bandwidth capacity than the malicious traffic. Thus the effect of bad or malicious traffic will be lesser in the network. Before describing the proposed solutions, a detail survey of the different DDoS countermeasures have been presented in the chapter.

Detecting DDoS Attacks on Multiple Network Hosts

2019 ◽  
pp. 121-139 ◽  
Author(s):  
Konstantinos F. Xylogiannopoulos ◽  
Panagiotis Karampelas ◽  
Reda Alhajj
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Detection Methods ◽  
Distributed Denial Of Service ◽  
Secondary Sources ◽  
Ddos Attack ◽  
Timely Fashion ◽  
Multiple Network ◽  
Ddos Attack Detection ◽  
Low Traffic

The proliferation of low security internet of things devices has widened the range of weapons that malevolent users can utilize in order to attack legitimate services in new ways. In the recent years, apart from very large volumetric distributed denial of service attacks, low and slow attacks initiated from intelligent bot networks have been detected to target multiple hosts in a network in a timely fashion. However, even if the attacks seem to be “innocent” at the beginning, they generate huge traffic in the network without practically been detected by the traditional DDoS attack detection methods. In this chapter, an advanced pattern detection method is presented that is able to collect and classify in real time all the incoming traffic and detect a developing slow and low DDoS attack by monitoring the traffic in all the hosts of the network. The experimental analysis on a real dataset provides useful insights about the effectiveness of the method by identifying not only the main source of attack but also secondary sources that produce low traffic, targeting though multiple hosts.

Defending against Distributed Denial of Service

2011 ◽  
pp. 121-129
Author(s):  
Yang Xiang ◽  
Wanlei Zhou
Keyword(s):  
Web Sites ◽  
Credit Card ◽  
Denial Of Service ◽  
Online Gambling ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Web Traffic ◽  
Internet Applications ◽  
Ddos Attack ◽  
Interactive Advertising

Recently the notorious Distributed Denial of Service (DDoS) attacks made people aware of the importance of providing available data and services securely to users. A DDoS attack is characterized by an explicit attempt from an attacker to prevent legitimate users of a service from using the desired resource (CERT, 2006). For example, in February 2000, many Web sites such as Yahoo, Amazon.com, eBuy, CNN.com, Buy. com, ZDNet, E*Trade, and Excite.com were all subject to total or regional outages by DDoS attacks. In 2002, a massive DDoS attack briefly interrupted Web traffic on nine of the 13 DNS “root” servers that control the Internet (Naraine, 2002). In 2004, a number of DDoS attacks assaulted the credit card processor Authorize. net, the Web infrastructure provider Akamai Systems, the interactive advertising company DoubleClick (left that company’s servers temporarily unable to deliver ads to thousands of popular Web sites), and many online gambling sites (Arnfield, 2004). Nowadays, Internet applications face serious security problems caused by DDoS attacks. For example, according to CERT/CC Statistics 1998-2005 (CERT, 2006), computer-based vulnerabilities reported have increased exponentially since 1998. Effective approaches to defeat DDoS attacks are desperately demanded (Cisco, 2001; Gibson, 2002).

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