Data Intrusion Detection with basic Python coding and prevention of other intrusive manifestation by the use of intrusion application
01 Nov 2018-
TL;DR: A model of an ongoing interruption location master framework fit for recognizing break-ins, entrances and different types of PC mishandle is portrayed, based on the speculation that security infringement can be identified by checking a framework's review records for unusual examples of framework utilization.
Abstract: A model of an ongoing interruption location master framework fit for recognizing break-ins, entrances and different types of PC mishandle is portrayed. The model depends on the speculation that security infringement can be identified by checking a framework's review records for unusual examples of framework utilization. The program on interruption discovery will have the capacity to distinguish whether a site, which for instance requires our client ID and secret word, is dependable or not. The model is autonomous of a specific framework, application condition. Framework defenselessness, or sort of interruption, in this manner giving a structure to a broadly useful interruption recognition master framework.
Citations
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21 Apr 2022
TL;DR: This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features.
Abstract: This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boost algorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.
1 citations
Dissertation•
29 Jan 2020
TL;DR: The implemented algorithm can be used for high-security cloud environment that is developed for army and banking purposes to monitor the network's activities effectively and offers clear potential for any further research work in the cloud-based Intrusion Detection System.
Abstract: The growing smartphone technology and emerging mobile cloud technology are the latest wireless technology. Mobile cloud computing has many of the advantages that look forward to the future and it's also simple for hackers to take full control of many other users Privacy of Data. While data security is expected to be secured, the main drawback for users when the computer is connected to the internet it's not that difficult for an intruder to engage in a data theft on the required target. So, for providing better security the combination of Hybrid Intrusion Detection System (HyInt) and Honeypot networks is thus implemented into Mobile Cloud Environment with the significant purpose of mitigating unidentified and known attacks in order to provide security. Execution of the research work provides a pure perspective of the security and quality products of the algorithm that was not included in the previous research work. As part of the research work, intensive statistical analysis was performed to prove the consistency of the proposed algorithm. The implementation and evaluation outcome offers clear potential for any further research work in the cloud-based Intrusion Detection System. The implemented algorithm can be used for high-security cloud environment that is developed for army and banking purposes to monitor the network's activities effectively.
Cites background from "Data Intrusion Detection with basic..."
...The main constraints to previous solutions were that they could not be fully designed to handle new types of attacks, where this is also a time-consuming task that requires too much time to examine suspicious attacks [11]...
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21 Apr 2022
TL;DR: In this paper , the authors presented the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features.
Abstract: This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boost algorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.
References
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25 Feb 2011
TL;DR: This paper proposes architecture capable of detecting intrusions in a distributed cloud computing environment, and safeguarding it from possible security breaches, that deploys a separate instance of IDS for each user and uses a single controller to manage the instances.
Abstract: In recent years, with the growing popularity of cloud computing, security in cloud has become an important issue. As "Prevention is better than cure", detecting and blocking an attack is better than responding to an attack after a system has been compromised. This paper proposes architecture capable of detecting intrusions in a distributed cloud computing environment, and safeguarding it from possible security breaches. It deploys a separate instance of IDS for each user and uses a single controller to manage the instances. IDS in this architecture can use signature based as well as learning based method.
93 citations
"Data Intrusion Detection with basic..." refers background in this paper
...[3] There are two types of IDS available for commercial purposes, Host-based Intrusion Detection System (HIDS) and Network- based Intrusion Detection System (NIDS)....
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TL;DR: DM and NBA approaches for network intrusion detection are discussed and it is suggested that a combination of both approaches has the potential to detect intrusions in networks more effectively.
Abstract: Intrusion detection has become a critical component of network administration due to the vast number of attacks persistently threaten our computers. Traditional intrusion detection systems are limited and do not provide a complete solution for the problem. They search for potential malicious activities on network traffics; they sometimes succeed to find true security attacks and anomalies. However, in many cases, they fail to detect malicious behaviours (false negative) or they fire alarms when nothing wrong in the network (false positive). In addition, they require exhaustive manual processing and human expert interference. Applying Data Mining (DM) techniques on network traffic data is a promising solution that helps develop better intrusion detection systems. Moreover, Network Behaviour Analysis (NBA) is also an effective approach for intrusion detection. In this paper, we discuss DM and NBA approaches for network intrusion detection and suggest that a combination of both approaches has the potential to detect intrusions in networks more effectively.
38 citations
TL;DR: The results of this paper clearly indicate lazy algorithms as a viable solution for real-world network intrusion detection.
16 citations