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Showing 4 results for Machine Learning
Narges Salehpour, Mohammad Nazari Farokhi, Ebrahim Nazari Farokhi,
Volume 3, Issue 2 (3-2015)
Abstract
Abstract One of the most important issues in securing computer networks is an Intrusion Detection System. Intrusion detection systems are searching for malicious behavior, deviation normal patterns and attacks on computer networks are discovered. This system recognizes the type of traffic allowed for unauthorized traffic. Since the today's data mining techniques to intrusion detection in computer networks are used. In this research is provided, a method for designing an intrusion detection system based on machine learning. One of the features of neural networks and machine learning systems, training is based on the training data. In this research is used for detecting the intrusion of machine learning to learn the features of the theory of Rough property that has a higher correlation coefficient is used. To train and evaluate has used the proposed approach the KDD CUP 99 dataset. This study, the accuracy of our method compares with feature-based learning algorithm, neural network self and decision tree. The simulation results show that the proposed system has high accuracy and speed of detection based on rough theory is right
Ms Maryam Taebi, Dr. Ali Bohlooli, Dr. Marjan Kaedi,
Volume 6, Issue 2 (3-2018)
Abstract
In Website Fingerprinting (WFP) Attacks, clients’ destination webpages are identified using traffic analysis techniques, without any need to decrypt traffic contents. Typically, clients make use of the privacy enhancing technologies (e.g., VPNs, proxies, and anonymity networks) to browse webpages. These technologies allow clients to hide traffic contents and their real destinations. To perform an attack, features are extracted from the input packet sequence. Next, the data is pre-processed and finally, client’s real destination is revealed by means of a machine learning algorithm. Various studies have utilized statistical methods or classification approaches to infer the client’s visited webpages. In this paper, a comprehensive overview of WFP techniques is performed, in which previous studies are categorized based on the features they use for webpages identification. This is a new approach for categorizing previous works on WFP attacks and to the best of our knowledge, this viewpoint has not been applied so far.
Fariba Sadeghi, Amir Jalaly Bidgoly,
Volume 8, Issue 1 (9-2019)
Abstract
Rumors, are unverified and often erroneous news that are widely propagated at the community level, discrediting or falsely increasing the trust of nodes in a network to an entity or subject. With the rise social networks in recent years, despite their positive uses, propagating rumors have become easier and more common. Rumors are a class of security challenges on social media, since a malicious node can easily disparage or isolate its goals by spreading a rumor. Therefore, rumors detection is an important challenge in soft security mechanisms such as trust and reputation. Researchers have come up with different methods for modeling, detecting and preventing rumors. In this study, rumor detection methods in social networks will be reviewed. First, we will briefly review the features used in previous research, then we will examine the approaches used and introduce the most commonly used Dataset. Finally, the challenges that exist for the future research in exploring social media to identify and resolve rumors are presented.
Farnoosh Karimi, Behrouz Tork Ladani, Behrouz Shahgholi Ghahfarokhi,
Volume 13, Issue 2 (12-2024)
Abstract
As the intensity of global cybersecurity threats continues to rise, the need for training security professionals has gained greater significance. Educational programs, complemented by laboratories and the execution of cybersecurity exercises, play a fundamental role in enhancing both offensive and defensive capabilities. The execution of such exercises is particularly crucial in operational networks, where testing cyberattacks may not be feasible. Cyber ranges offer an appropriate platform for conducting these exercises. A primary challenge in cybersecurity education is aligning training programs with the diverse skill levels of learners. Adaptive learning, powered by artificial intelligence and recommendation systems, can provide an effective solution for delivering personalized instruction. This study focuses on the KYPO Cyber Range to examine the potential of substituting or augmenting the role of the instructor with an AI-based recommendation agent. The objective of this research is to minimize human intervention and improve the efficiency of the training process. To this end, data collected from the KYPO Cyber Range, developed by Masaryk University, has been utilized, and various machine learning models have been applied to automate and optimize the training process. The results of this research indicate that the integration of artificial intelligence can enhance the performance of educational systems and reduce evaluation time.
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