Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Computer controlled intrusion-detector and automatic firing-unit for border security(2010) Vittal, K.P.; Ajay, P.P.; Shenoy, S.B.; Srinivas Rao, C.H.S.This paper describes a novel computer-controlled intrusion-detector and automatic firing unit, which may be used for the surveillance of borders, either of a country, or of areas requiring high security, especially in regions of extreme climatic conditions, where it is difficult to deploy personnel. This system not only detects intrusion but also provides a video-coverage of the suspicious area, for remote vigilance, via a satellite based communication system. It is also provided with automatic firing mechanisms which can be used to automatically locate and fire at the target. Thus, several kilometres of the borders, which would have otherwise required several hundred personnel, can be effortlessly monitored with this system, with only a few personnel. Since, the actual firing occurs only after an authoritative personnel has doubly confirmed the presence of an intruder, chances of firing at innocent people are completely ruled out. As thermal cameras are used for imaging, this system is immune to changes in ambient conditions, and therefore, is equally suited for operation during the night. This paper also throws light on the prototype of this system, which has been successfully developed. © 2010 IEEE.Item A framework to monitor cloud infrastructure in service oriented approach(2013) Veigas, J.P.; Chandra Sekaran, K.Cloud computing processes and stores the organization's sensitive data in the third party infrastructure. Monitoring these activities within the cloud environment is a major task for the security analysts and the cloud consumer. The cloud service providers may voluntarily suppress the security threats detected in their Infrastructure from the consumers. The goal is to decouple Intrusion Detection System (IDS) related logic from individual application business logic and adhere to the Service Oriented Architecture Standards. This paper provides a framework for Intrusion Detection and reporting service to the cloud consumers based on the type of applications and their necessary security needs. Cloud consumers can choose the desired signatures from this framework to protect their applications. The proposed technique is deployed in existing open source cloud environment with minimum changes. A proof-of-concept prototype has been implemented based on Eucalyptus open source packages to show the feasibility of this approach. Our results show that this framework provides effective way to monitor the cloud infrastructure in service oriented approach. © 2013 IEEE.Item Feature selection using fast ensemble learning for network intrusion detection(Springer Verlag service@springer.de, 2020) Pasupulety, U.; Adwaith, C.D.; Hegde, S.; Patil, N.Network security plays a critical role in today’s digital system infrastructure. Everyday, there are hundreds of cases of data theft or loss due to the system’s integrity being compromised. The root cause of this issue is the lack of systems in place which are able to foresee the advent of such attacks. Network Intrusion detection techniques are important to prevent any system or network from malicious behavior. By analyzing a dataset with features summarizing the method in which connections are made to the network, any attempt to access it can be classified as malicious or benign. To improve the accuracy of network intrusion detection, various machine learning algorithms and optimization techniques are used. Feature selection helps in finding important attributes in the dataset which have a significant effect on the final classification. This results in the reduction of the size of the dataset, thereby simplifying the task of classification. In this work, we propose using multiple techniques as an ensemble for feature selection. To reduce training time and retain accuracy, the important features of a subset of the KDD Network Intrusion detection dataset were analyzed using this ensemble learning technique. Out of 41 possible features for network intrusion, it was found that host-based statistical features of network flow play an import role in predicting network intrusion. Our proposed methodology provides multiple levels of overall selected features, correlated to the number of individual feature selection techniques that selected them. At the highest level of selected features, our experiments yielded a 6% increase in intrusion detection accuracy, an 81% decrease in dataset size and a 5.4× decrease in runtime using a Multinomial Naive Bayes classifier on the original dataset. © Springer Nature Switzerland AG 2020.Item Intrusion Detection Techniques for Detection of Cyber Attacks(Springer Science and Business Media Deutschland GmbH, 2021) Ahmed, S.S.; Kankar, M.; Rudra, B.Intrusion detection system (IDS) is a software-related application where we can detect the system or network activities and notice if any suspicious task happens. Excellent broadening and the use of the Internet lift examine the communication and save the digital information securely. Nowadays, attackers use variety of attacks for fetching private data. Most of the IDS techniques, algorithms, and methods assist to find those various attacks. The central aim of the project is to come up with an overall study about the intrusion detection mechanism, various types of attacks, various tools and techniques, and challenges. We used various machine learning algorithms and found performance metrics like accuracy, recall, and F-measure and compared with the existing work. After this research, we got good results that can help to detect the cyber attacks being performed in the network. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.