Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Intrusion detection system

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    Extended game theoretic dirichlet based collaborative intrusion detection systems
    (Springer Verlag service@springer.de, 2016) Paul, S.; Makkar, T.; Chandrasekaran, K.
    Security has always been one of the key issues of any man-made system, this paved the way for a submodule or application or a device to monitor or system for malicious activities. This system or submodule or device is known as Intrusion Detection System (IDS). As technology evolves so does the associated threats and thus the intrusion detection system needs to evolve. Game theory throws in a different perspective which have not been looked upon much. Game theory provides a way of mathematically formalizing the decision making process of policy establishment and execution. Notion of game theory can be used in intrusion detection system in assisting in defining and reconfiguring security policies given the severity of attacks dynamically. We are trying to formulate a robust model for the theoretical limits of a game theoretic approach to IDS. The most important flaw of game theory is that it assumes the adversary’s rationality and doesn’t take into consideration multiple simultaneous attacks. Therefore, a collaborative trust and Dirichlet distribution based robust game theoretic approach is proposed which will try to resolve this issue. Reinforced learning approaches using Markov Decision Process will be utilized to make it robust to multiple simultaneous attacks. © Springer Science+Business Media Singapore 2016.
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    Optimization of adaptive resonance theory neural network using particle swarm optimization technique
    (Springer Verlag service@springer.de, 2018) Satpute, K.; Kumar, R.
    With the advancement of computers and its computational enhancement over several decades of use, but with the growth in the dependencies and use of these systems, more and more concerns over the risk and security issues in networks have raised. In this paper, we have proposed approach using particle swarm optimization to optimize ART. Adaptive resonance theory is one of the most well-known machine-learning-based unsupervised neural networks, which can efficiently handle high-dimensional dataset. PSO on the other hand is a swarm intelligence-based algorithm, efficient in nonlinear optimization problem and easy to implement. The method is based on anomaly detection as it can also detect unknown attack types. PSO is used to optimize vigilance parameter of ART-1 and to classify network data into attack or normal. KDD ’99 (knowledge discovery and data mining) dataset has been used for this purpose. © Springer Nature Singapore Pte Ltd. 2018.
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    Performance Evaluation of Signature Based and Anomaly Based Techniques for Intrusion Detection
    (Springer Science and Business Media Deutschland GmbH, 2023) Agrawal, V.K.; Rudra, B.
    In the age of information technology everything is derived using information systems and allows us to communicate with each other. Internet acts as a medium to communicate among various devices from our wrist watch to our personal computers, TVs, refrigerators, etc. all are connected. But with all this luxury of comforts comes with the cost of security threats. Hence, it becomes very important to address issues related to security. We propose a hybrid intrusion detection system that is based on signature based and anomaly based Intrusion Detection System to address the need of today. While signature based approaches are designed to classify previously known attacks, anomaly detection learn traffic profiles and detect which network packets are normal traffic and which are not. With this ability, this technique helps to identify zero day attacks also. Our approach suggests the process from dataset preprocessing to model training and testing, this will provide proper guidance for building any type of Intrusion Detection System (IDS). Our proposed model achieves a accuracy of 99.67 % for signature based approach and 96.833 % for anomaly based approach on the CICIDS2017 dataset. Results show substantial scope for real world applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.