A Machine-Synesthetic Approach To DDoS Network Attack Detection

In the authors' opinion, anomaly detection systems, or ADS, seem to be the most perspective direction in the subject of attack detection, because these systems can detect, among others, the unknown (zero-day) attacks. To detect anomalies, the authors propose to use machine synesthesia. In this case, machine synesthesia is understood as an interface that allows using image classification algorithms in the problem of detecting network anomalies, making it possible to use non-specialized image detection methods that have recently been widely and actively developed. The proposed approach is that the network traffic data is "projected" into the image. It can be seen from the experimental results that the proposed method for detecting anomalies shows high results in the detection of attacks. On a large sample, the value of the complex efficiency indicator reaches 97%.Comment: 12 pages, 2 figures, 5 tables. Accepted to the Intelligent Systems Conference (IntelliSys) 201

A Modified K-Means with Naïve Bayes (KMNB) Algorithm for Breast Cancer Classification

Breast cancer is a second biggest cause of human death on women. The death rate caused by the breast cancer has been fallen since 1989. This downfall is believed as a result from early diagnose on breast cancer, the awareness uplift on the breast cancer, also a better medical treatment. This research proposes the Modified K-Means Naïve Bayes (KMNB) method on Breast Cancer data. The modification which has been conducted was an additional on initial centroid which has been proposed by Fang. The experiment compared the accuracy of our proposed method with the original KMNB, Original KMean, and K-Means using initial centroid by Fang. Based on the result of the experiment, the accuracy of our proposed method was 95%. The error reduction of our proposed method was about 50% compared to the original KMNB. It can be stated that our proposed method is promising and able to enhance the prediction on Breast Cancer Wisconsin data. On the other hand, the enhancement of prediction result will increase the preventive behavior on society and give a positive impact on the number downfall of breast cancer sufferers

Comparing classification algorithms for prediction on CROBEX data

The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day. If the increase between the opening price and the closing price of the same day is substantial enough to earn a profit by purchasing at the opening price and selling at the closing price, the day is considered to be favourable for intra-day trading. The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Thus, the following algorithms are used: neural network, support vector machine, random forest, as well as k-nearest neighbours and naïve Bayes classifier as classifiers that are more common. The work is an extension of authors’ previous work in which the algorithms are compared on resamples resulting from tuning the algorithms, while here, each derived model is used to make predictions on new data. The results should add to the increasing corpus of stock market prediction research efforts and try to fill some gaps in this field of research for the Croatian market, in particular by using machine learning algorithms

Infinite Virtual Stoa

Stoicism is a philosophy that considers the object of life to be ataraxia (αταραξία), a state of psychological stability which is undisturbed by exposure to phenomena and circumstances that lie outside one's control. Such circumstances may include ill health, poverty, natural disasters, corrupt social orders, unpopularity, and unrequited love, and may cause loss of composure and mental balance through feelings of pain, humiliation, insufficiency, envy or greed. Stoicism is a coherent system of powerful ideas about how to pursue a life of equanimity in the face of adversity which has inspired philosophy and psychology to this day. The founders of Cognitive Behavioural Therapy have cited Stoicism as their main inspiration. Stoicism flourished in ancient Athens and Rome at a time when ancient democracy was dying and people experienced loss of control over their lives under authoritarian and imperial regimes. In an age of serious global economic, environmental and psychological uncertainty and crisis, stoicism has still pressing and valuable lessons to teach us about calm, composure, stability and emotional resilience

Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning

Digital forensics research includes several stages. Once we have collected the data the last goal is to obtain a model in order to predict the output with unseen data. We focus on supervised machine learning techniques. This chapter performs an experimental study on a forensics data task for multi-class classification including several types of methods such as decision trees, bayes classifiers, based on rules, artificial neural networks and based on nearest neighbors. The classifiers have been evaluated with two performance measures: accuracy and Cohen’s kappa. The followed experimental design has been a 4-fold cross validation with thirty repetitions for non-deterministic algorithms in order to obtain reliable results, averaging the results from 120 runs. A statistical analysis has been conducted in order to compare each pair of algorithms by means of t-tests using both the accuracy and Cohen’s kappa metrics

An Empirical Model for Thyroid Disease Classification using Evolutionary Multivariate Bayseian Prediction Method

Thyroid diseases are widespread worldwide. In India too, there is a significant problems caused due to thyroid diseases. Various research studies estimates that about 42 million people in India suffer from thyroid diseases [4]. There are a number of possible thyroid diseases and disorders, including thyroiditis and thyroid cancer. This paper focuses on the classification of two of the most common thyroid disorders are hyperthyroidism and hypothyroidism among the public. The National Institutes of Health (NIH) states that about 1% of Americans suffer from Hyperthyroidism and about 5% suffer from Hypothyroidism. From the global perspective also the classification of thyroid plays a significant role. The conditions for the diagnosis of the disease are closely linked, they have several important differences that affect diagnosis and treatment. The data for this research work is collected from the UCI repository which undergoes preprocessing. The preprocessed data is multivariate in nature. Curse of Dimensionality is followed so that the available 21 attributes is optimized to 10 attributes using Hybrid Differential Evolution Kernel Based Navie Based algorithm. The subset of data is now supplied to Kernel Based NaEF;ve Bayes classifier algorithm in order to check for the fitness

Архитектура системы мониторинга мобильных гетерогенных объектов

REMOTE MONITORING ARCHITECTURE FOR MOBILE GETEROGENOUS OBJECTS / A. KUZMICH, V. KRASNOPROSHIN.Рассматриваются вопросы синтеза типовой архитектуры для систем мониторинга мобильных гетерогенных объектов. Сформулирована общая задача и представлено ее решение на основе синтеза методов теории принятия решений, процессного и многоагентного подходов. Обоснована целесообразность стандартизации систем мониторинга на основе архитектуры, включающей шесть программных агентов, обеспечивающих формализацию предметной области, идентификацию, оценку и синтез управляющих решений для мобильных объектов на основе собственной базы знаний для априори известных ситуаций и опыта экспертов для новых ситуаций.= Issues of a typical architecture synthesis for mobile heterogeneous objects monitoring are discussed. The general problem and its solution are presented on the basis of the decision-making theory methods synthesis, process- and agent-based approaches. The reasonability of monitoring systems standardization on the basis of architecture consisting of six program agents providing subject area formalization, identification, evaluation and control solution synthesis for mobile objects on the basis of our own knowledge base for a priori known situations and experts know-how in new situations is justified

IMPLEMENTASI TEXT MINING MENGGUNAKAN NAIVE BAYES UNTUK PENENTUAN KATEGORI TUGAS AKHIR MAHASISWA BERDASARKAN ABSTRAKSINYA

Dengan semakin meningkatnya penggunaan aplikasi sistem informasi di berbagai bidang, turut berdampak pada kebutuhan untuk peningkatan kecepatan pemrosesan data. Pemrosesan data yang menggunakan proses semi manual, mempunyai beberapa kendala, diantaranya : waktu proses lebih lama dan besaran data yang diproses menjadi relatif sedikit. Oleh karena itu dalam penelitian ini dikembangkan penggunaan naive bayes untuk membantu bagian koordinator tugas akhir dalam melakukan pengelompokan proposal tugas akhir. Metode naive bayes yang akan diimplementasi ke dalam sistem informasi proposal tugas akhir dapat memberikan sebuah solusi baru untuk menentukan kategori proposal tugas akhir berdasarkan abstraksi yang dibuat mahasiswa. Dalam hasil uji coba metode ini, dapat disimpulkan cukup berhasil dan secara garis besar dapat dijadikan sebagai perangkat bantu dalam melakukan klasifikasi dokumen tugas akhir. Tingkat akurasi berdasarkan pengujian untuk kategori hardware dan networking mencapai 86%, kategori sistem informasi tingkat akurasi mencapai 80% dan kategori sistem informasi akuntansi mencapai 89%. Secara keseluruhan, berdasarkan jumlah dataset yang diujikan dan tingkat keberhasilan yang dicapai, maka sistem ini mempunyai tingkat akurasi 87%