اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدNetwork Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naive Bayes Algorithm
75
0
0.0
(
0
)
تأليف
Fan Huang
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Naive Bayes algorithms are introduced and evaluated.
قيم البحث
اقرأ أيضاً
Website Fingerprinting (WF) attacks raise major concerns about users privacy. They employ Machine Learning (ML) to allow a local passive adversary to uncover the Web browsing behavior of a user, even if she browses through an encrypted tunnel (e.g. T
or, VPN). Numerous defenses have been proposed in the past; however, it is typically difficult to have formal guarantees on their security, which is most often evaluated empirically against state-of-the-art attacks. In this paper, we present a practical method to derive security bounds for any WF defense, which depend on a chosen feature set. This result derives from reducing WF attacks to an ML classification task, where we can determine the smallest achievable error (the Bayes error); such error can be estimated in practice, and is a lower bound for a WF adversary, for any classification algorithm he may use. Our work has two main consequences: i) it allows determining the security of WF defenses, in a black-box manner, with respect to the state-of-the-art feature set and ii) it favors shifting the focus of future WF research to the identification of optimal feature sets. The generality of the approach further suggests that the method could be used to define security bounds for other ML-based attacks.
Human Activity Recognition (HAR) is considered a valuable research topic in the last few decades. Different types of machine learning models are used for this purpose, and this is a part of analyzing human behavior through machines. It is not a trivi
al task to analyze the data from wearable sensors for complex and high dimensions. Nowadays, researchers mostly use smartphones or smart home sensors to capture these data. In our paper, we analyze these data using machine learning models to recognize human activities, which are now widely used for many purposes such as physical and mental health monitoring. We apply different machine learning models and compare performances. We use Logistic Regression (LR) as the benchmark model for its simplicity and excellent performance on a dataset, and to compare, we take Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). Additionally, we select the best set of parameters for each model by grid search. We use the HAR dataset from the UCI Machine Learning Repository as a standard dataset to train and test the models. Throughout the analysis, we can see that the Support Vector Machine performed (average accuracy 96.33%) far better than the other methods. We also prove that the results are statistically significant by employing statistical significance test methods.
In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature,
and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.
In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, or network, can cause huge damage for the companies. However, when this
attack detection encounters the Artificial Intelligence paradigm, it can be addressed using high-quality classifiers which often need high resource demands in terms of computation or memory usage. This situation has a high impact when the attack classifiers need to be used with limited resourced devices or without overloading the performance of the devices, as it happens for example in IoT devices, or in industrial systems. For overcoming this issue, NBcoded, a novel light attack classification tool is proposed in this work. NBcoded works in a pipeline combining the removal of noisy data properties of the encoders with the low resources and timing consuming obtained by the Naive Bayes classifier. This work compares three different NBcoded implementations based on three different Naive Bayes likelihood distribution assumptions (Gaussian, Complement and Bernoulli). Then, the best NBcoded is compared with state of the art classifiers like Multilayer Perceptron and Random Forest. Our implementation shows to be the best model reducing the impact of training time and disk usage, even if it is outperformed by the other two in terms of Accuracy and F1-score (~ 2%).
As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic catego
rization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Naive Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Naive Bayes Classifier is then used on derived features for final categorization.
الأسئلة المقترحة
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
