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

NAVI: Novel authentication with visual information

Text-based passwords, despite their well-known drawbacks, remain the dominant user authentication scheme implemented. Graphical password systems, based on visual information such as the recognition of photographs and / or pictures, have emerged as a promising alternative to the aggregate reliance on text passwords. Nevertheless, despite the advantages offered they have not been widely used in practice since many open issues need to be resolved. In this paper we propose a novel graphical password scheme, NAVI, where the credentials of the user are his username and a password formulated by drawing a route on a predefined map. We analyze the strength of the password generated by this scheme and present a prototype implementation in order to illustrate the feasibility of our proposal. Finally, we discuss NAVI’s security features and compare it with existing graphical password schemes as well as text-based passwords in terms of key security features, such aspassword keyspace, dictionary attacks and guessing attacks. The proposed scheme appears to have the same or better performance in the majority of the security features examined

Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200

ForChaos: Real Time Application DDoS detection using Forecasting and Chaos Theory in Smart Home IoT Network

Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose we propose ForChaos, a lightweight detection algorithm for IoT devices, that is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calcualted. In order to assess the error of the forecasting from the actual value, the lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in Flooding and Slow-Rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness

SAnoVs: Secure Anonymous Voting Scheme for clustered ad hoc networks

In this paper we propose a secure anonymous voting scheme (SAnoVS) for re-clustering in the ad-hoc network. SAnoVS extends our previous work of degree-based clustering algorithms by achieving anonymity and confidentiality of the voting procedure applied to select new cluster heads. The security of SAnoVS is based on the difficulty of computing discrete logarithms over elliptic curves, the intractability of inverting a one-way hash function and the fact that only neighboring nodes contribute to the generation of a shared secret. Furthermore, we achieve anonymity since our scheme does not require any identification information as we make use of a polynomial equation system combined with pseudo-random coordinates. The security analysis of our scheme is demonstrated with several attacks scenarios.examined with several attack scenarios and experimental results

A two‐step authentication framework for Mobile ad hoc networks

The lack of fixed infrastructure in ad hoc networks causes nodes to rely more heavily on peer nodes for communication. Nevertheless, establishing trust in such a distributed environment is very difficult, since it is not straightforward for a node to determine if its peer nodes can be trusted. An additional concern in such an environment is with whether a peer node is merely relaying a message or if it is the originator of the message. In this paper, we propose an authentication approach for protecting nodes in mobile ad hoc networks. The security requirements for protecting data link and network layers are identified and the design criteria for creating secure ad hoc networks using several authentication protocols are analyzed. Protocols based on zero knowledge and challenge response techniques are presented and their performance is evaluated through analysis and simulation

Network attack detection at flow level

In this paper, we propose a new method for detecting unauthorized network intrusions, based on a traffic flow model and Cisco NetFlow protocol application. The method developed allows us not only to detect the most common types of network attack (DDoS and port scanning), but also to make a list of trespassers' IP-addresses. Therefore, this method can be applied in intrusion detection systems, and in those systems which lock these IP-addresses

Detecting unauthorized and compromised nodes in mobile ad hoc networks

Security of mobile ad-hoc networks (MANET) has become a more sophisticated problem than security in other networks, due to the open nature and the lack of infrastructure of such networks. In this paper, the security challenges in intrusion detection and authentication are identified and the different types of attacks are discussed. We propose a two-phase detection procedure of nodes that are not authorized for specific services and nodes that have been compromised during their operation in MANET. The detection framework is enabled with the main operations of ad-hoc networking, which are found at the link and network layers. The proposed framework is based on zero knowledge techniques, which are presented through proofs