5 research outputs found
Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network
Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks
Developing secure machine learning models from adversarial examples is
challenging as various methods are continually being developed to generate
adversarial attacks. In this work, we propose an evolutionary approach to
automatically determine Image Processing Techniques Sequence (IPTS) for
detecting malicious inputs. Accordingly, we first used a diverse set of attack
methods including adaptive attack methods (on our defense) to generate
adversarial samples from the clean dataset. A detection framework based on a
genetic algorithm (GA) is developed to find the optimal IPTS, where the
optimality is estimated by different fitness measures such as Euclidean
distance, entropy loss, average histogram, local binary pattern and loss
functions. The "image difference" between the original and processed images is
used to extract the features, which are then fed to a classification scheme in
order to determine whether the input sample is adversarial or clean. This paper
described our methodology and performed experiments using multiple data-sets
tested with several adversarial attacks. For each attack-type and dataset, it
generates unique IPTS. A set of IPTS selected dynamically in testing time which
works as a filter for the adversarial attack. Our empirical experiments
exhibited promising results indicating the approach can efficiently be used as
processing for any AI model
Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence
The evolution of cybersecurity has spurred the emergence of autonomous threat
hunting as a pivotal paradigm in the realm of AI-driven threat intelligence.
This review navigates through the intricate landscape of autonomous threat
hunting, exploring its significance and pivotal role in fortifying cyber
defense mechanisms. Delving into the amalgamation of artificial intelligence
(AI) and traditional threat intelligence methodologies, this paper delineates
the necessity and evolution of autonomous approaches in combating contemporary
cyber threats. Through a comprehensive exploration of foundational AI-driven
threat intelligence, the review accentuates the transformative influence of AI
and machine learning on conventional threat intelligence practices. It
elucidates the conceptual framework underpinning autonomous threat hunting,
spotlighting its components, and the seamless integration of AI algorithms
within threat hunting processes.. Insightful discussions on challenges
encompassing scalability, interpretability, and ethical considerations in
AI-driven models enrich the discourse. Moreover, through illuminating case
studies and evaluations, this paper showcases real-world implementations,
underscoring success stories and lessons learned by organizations adopting
AI-driven threat intelligence. In conclusion, this review consolidates key
insights, emphasizing the substantial implications of autonomous threat hunting
for the future of cybersecurity. It underscores the significance of continual
research and collaborative efforts in harnessing the potential of AI-driven
approaches to fortify cyber defenses against evolving threats