Obstacle Collision Prediction Model for Path Planning Using Obstacle Trajectory Clustering

Ajani, Samir N.; Amdani, Salim Y.

doi:10.1007/978-981-19-4687-5_8

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…To sort and stop cyber risks, these studies use machine learning algorithms and behavioral analysis methods. Using Bayesian networks for breach detection has also shown promise in making detections more accurate through statistical reasoning [8]. Bayesian networks make danger assessment and decision-making more complex by describing how network events depend on each other.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Rohit Krishna Murti

2024

cana

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Cyberattacks are a big problem in today's world because everything is linked online. They can damage the security, privacy, and access of private information. Intrusion Detection Systems (IDS) are very important for keeping networks safe because they quickly find and stop harmful activity. To successfully find and stop attacks, however, we need more advanced methods because online risks are always changing. This paper suggests a new mathematical approach for improving IDS that is based on random models. Traditional ways of finding intrusions often use signature- or anomaly-based methods, which might not be able to keep up with how attackers' strategies change all the time. By using random processes, our method provides a more flexible and adaptable way to find online threats. IDS can tell the difference between normal network traffic and hostile activities because stochastic models use a statistical framework to capture the uncertainty that comes with cyberattack behaviors. We use methods from probability theory, Markov chains, and queue theory to make models of how network traffic and possible cyberattacks might behave. Our random models can find differences that could mean someone is doing something bad by looking at the statistical features of different network factors, like the rate at which packets arrive, the length of a link, and the size of the payloads. Using Markov models, the IDS can also guess how likely it is that an attack will happen in the future based on past data, which helps with strategic strategies for reducing threats. Along with monitoring, our system includes ways to respond to cyberattacks and lessen their effects. By using stochastic optimization methods, we can change how resources are used and how reactions are prioritized based on how likely and how bad the threats are to be. This flexible method makes the network infrastructure more resistant to complex attack routes, which lowers the damage and downtime that can happen during cyber events. We show that our stochastic models can improve the performance of IDS in real-world situations by analyzing them theoretically and running simulations. Adopting a scientific approach to cyber security makes it possible for stronger and smarter defenses against cyber risks that are always changing.

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Section: Related Workmentioning

confidence: 99%

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Rohit Krishna Murti

2024

cana

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“…On the other hand, hydro-optical communication makes use of the characteristics of light and water to produce effective data transfer via optical signals. For some light wavelengths, water has relatively low absorption and scattering coefficients, enabling larger data rates and further communication ranges than with conventional techniques [17]. Numerous research investigations have demonstrated the potential of hydro-optical communication, with applications ranging from underwater sensor networks to underwater robots.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Hydro-Optical Communication-Based Invasive Wireless Sensor Networks

Gandhi,

Patil

2023

IJRITCC

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The selection and implementation of a hydro-optical communication connection for real-time position tracking of remotely operated vehicles (ROVs) in underwater settings are the main topics of this research article. For a variety of applications, including pipeline maintenance, environmental research, and undersea infrastructure inspection, effective monitoring and control of ROVs is essential. Due to signal attenuation and interference, conventional communication techniques are difficult to use in aquatic environments. To get over these restrictions, hydro-optical communication, which sends light messages through water, presents a viable option. The paper examines current hydro-optical communication methods and technology, pointing out their benefits and drawbacks. It deals with the technical needs of setting up a trustworthy communication channel for monitoring the real-time location of ROVs. To ensure accurate and timely data delivery, an appropriate communication protocol is created. The research also demonstrates the smooth operation of hydro-optical communication with the ROV's navigation system. The suggested method is put into practise and tested in safe underwater environments in order to assess how well it works at pinpointing the position of ROVs. Among the performance indicators evaluated are data rate, communication range, and energy efficiency. The results of this study help to advance the area of underwater communication and make it easier to monitor and manage ROVs in real-time for a variety of underwater applications.

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“…This may be thought of as looking for anything that deviates from the norm. A method known as anomaly-based detection [4] is a vital tool that must be used in order to identify zero-based attacks. In order to properly detect new threats, a huge amount of data has to be collected in order to develop a model that specifies what constitutes normal behaviour and what constitutes an aberration.…”

mentioning

confidence: 99%

Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Ugale

Potgantwar

2023

IJEER

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Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.

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Obstacle Collision Prediction Model for Path Planning Using Obstacle Trajectory Clustering

Cited by 4 publications

References 13 publications

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Design and Development of Hydro-Optical Communication-Based Invasive Wireless Sensor Networks

Anomaly Based Intrusion Detection through Efficient Machine Learning Model

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