DPRAODV: A Dynamic Learning System Against Blackhole Attack In AODV Based MANET

Security is an essential requirement in mobile ad hoc networks to provide protected communication between mobile nodes. Due to unique characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a multifence security solution that achieves both broad protection and desirable network performance. MANETs are vulnerable to various attacks, blackhole, is one of the possible attacks. Black hole is a type of routing attack where a malicious node advertise itself as having the shortest path to all nodes in the environment by sending fake route reply. By doing this, the malicious node can deprive the traffic from the source node. It can be used as a denial-of-service attack where it can drop the packets later. In this paper, we proposed a DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver-2.33) demonstrate that our protocol not only prevents blackhole attack but consequently improves the overall performance of (normal) AODV in presence of black hole attack

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics

A Survey of Security Challenges and Issues in Manet

Nodes intriguing element in Mobile Ad-hoc Networks (MANET) are predictable to hold to the rules stated by the routing protocol utilized in the network. Safe routing protocols endeavor to decrease the ill-effect of nodes under the control of malicious entities who intentionally violate the protocol.. There are so many generic tools which are universal for individual as well as organizations for customers to offer protection which comprises Antivirus, Ant spam, etc., and network securities have turn into important issue in MANET. Security is one of the major issues in the MANET particularly w.r.t. complexity and size of the network. The main focus of this survey is to discuss & represent special characteristics of security in MANET and also apply several of the solutions security threats within MANET network similar to intruder activities, tapping and integrity, MANET link layer and network layer operations w.r.t. information security etc) w.r.t. MANET network. This Survey paper also discusses different number of security scenarios of MANET, Attacks in MANET and IDS in MANET. Keywords: AODV, MANET, Network Security, IDS, Attack

A Survey on Intrusion Detection System in MANET

A mobile ad hoc network is an infrastructure less network which is prone to various malicious attacks when incorporated in applications. It is a dreadful task for attaining security to the greatest degree in MANET. This is awaited to the diverse characteristics of mobile ad hoc networks which unlike from well-established infrastructure network. In order to overcome this security challenges the Intrusion detection systems have been deployed in the ad hoc network. In this paper we focus on surveying heterogeneous intrusion detection systems used in MANET for defending various attacks

Black Hole attack Detection using fuzzy based IDS

In the past few years, an evolution in the wireless communication has been emerged, along with the evolution a new type with large potential application of wireless network appears, which is the Mobile Ad-Hoc Network (MANET). Black hole attack consider one of the most affected kind on MANET. Therefore, the use of intrusion detection system (IDS) has a major importance in the MANET protection. In this paper, an optimization of a fuzzy based intrusion detection system is proposed which automate the process of producing a fuzzy system by using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the initialization of the FIS and then optimize this initialized system by using Genetic Algorithm (GA). In addition, a normal estimated fuzzy based IDS is introduces to see the effect of the optimization on the system. From this study, it is proven that the optimized proposed IDS perform better that the normal estimated systems

A hybrid method of genetic algorithm and support vector machine for intrusion detection

With the development of web applications nowadays, intrusions represent a crucial aspect in terms of violating the security policies. Intrusions can be defined as a specific change in the normal behavior of the network operations that intended to violate the security policies of a particular network and affect its performance. Recently, several researchers have examined the capabilities of machine learning techniques in terms of detecting intrusions. One of the important issues behind using the machine learning techniques lies on employing proper set of features. Since the literature has shown diversity of feature types, there is a vital demand to apply a feature selection approach in order to identify the most appropriate features for intrusion detection. This study aims to propose a hybrid method of Genetic Algorithm and Support Vector Machine. GA has been as a feature selection in order to select the best features, while SVM has been used as a classification method to categorize the behavior into normal and intrusion based on the selected features from GA. A benchmark dataset of intrusions (NSS-KDD) has been in the experiment. In addition, the proposed method has been compared with the traditional SVM. Results showed that GA has significantly improved the SVM classification by achieving 0.927 of f-measure