ApriorC4.5 data mining algorithm for enhance the network-based intrusion detection in financial data

The most important cause for the introduction regarding an attack on the law is the Internet's recognition. Economic data safety has become an important issue, an urgent want in imitation of pick out and detects attacks. Intrusion Detection is described as much a pc network in imitation of diagnosing signs about attacks yet malicious endeavor thru a provision over continuous assessment methods. The software program does operate its duties are defined as much intrusion discovery structures (IDS) the need because of economic data. The system advanced separate algorithm provides excellent discovery quantity yet means counterfeit fear rate, certain as an array and shallow learning. Recent research exhibit, as in contrast, including structures using a variety concerning Cascade Algorithm instruction algorithm Shallow development, presents an awful lot better performance. The intrusion detection system, correct detection algorithm using the ratio used to be much less marked. False funk quantity also increased. The algorithm is according to clear up this problem. This dissertation describes the twain hybrid algorithm because of the improvement of intrusion discovery systems. C4.5 selection creeper yet supports the aggregate concerning shallow lessons by maximizing accuracy, a competency regarding C4.5, decreasing the bad alarm rate, and shallow learning talents. The effects showed as the expansion into accuracy, the discovery dimensions then ignoble counterfeit scare rate.&nbsp

BUDOWA SYSTEMÓW WYKRYWANIA ATAKÓW NA PODSTAWIE METOD INTELIGENTNEJ ANALIZY DANYCH

Nowadays, with the rapid development of network technologies and with global informatization of society problems come to the fore ensuring a high level of information system security. With the increase in the number of computer security incidents, intrusion detection systems (IDS) started to be developed rapidly.Nowadays the intrusion detection systems usually represent software or hardware-software solutions, that automate the event control process, occurring in an information system or network, as well as independently analyze these events in search of signs of security problems. A modern approach to building intrusion detection systems is full of flaws and vulnerabilities, which allows, unfortunately, harmful influences successfully overcome information security systems. The application of methods for analyzing data makes it possible identification of previously unknown, non-trivial, practically useful and accessible interpretations of knowledge necessary for making decisions in various spheres of human activity. The combination of these methods along with an integrated decision support system makes it possible to build an effective system for detecting and counteracting attacks, which is confirmed by the results of imitation modeling.W chwili obecnej szybki rozwój technologii sieciowych i globalnej informatyzacji społeczeństwa uwypukla problemy związane z zapewnieniem wysokiego poziomu bezpieczeństwa systemów informacyjnych. Wraz ze wzrostem liczby incydentów komputerowych związanych z bezpieczeństwem nastąpił dynamiczny rozwój systemów wykrywania ataków. Obecnie systemy wykrywania włamań i ataków to zazwyczaj oprogramowanie lub sprzętowo-programowe rozwiązania automatyzujące proces monitorowania zdarzeń występujących w systemie informatycznym lub sieci, a także samodzielnie analizujące te zdarzenia w poszukiwaniu oznak problemów bezpieczeństwa. Nowoczesne podejście do budowy systemów wykrywania ataków na systemy informacyjne jest pełne wad i słabych punktów, które niestety pozwalają szkodliwym wpływom na skuteczne pokonanie systemów zabezpieczania informacji. Zastosowanie metod inteligentnej analizy danych pozwala wykryć w danych nieznane wcześniej, nietrywialne, praktycznie użyteczne i dostępne interpretacje wiedzy niezbędnej do podejmowania decyzji w różnych sferach ludzkiej działalności. Połączenie tych metod wraz ze zintegrowanym systemem wspomagania decyzji umożliwia zbudowanie skutecznego systemu wykrywania i przeciwdziałania atakom, co potwierdzają wyniki modelowania

Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems

This research presents an IDS prototype in Matlab that assess network traffic connections contained in the NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior results applying dimension reduction technique ISOMAP. The classification process used a supervised learning technique called Support Vector Machines (SVM). The comparative analysis related to detection rates by attack category are conclusive that MRMR+PCA+SVM (selection, reduction and classification techniques) combined obtained more promising results, just using 5 of 41 available features in the dataset. The results obtained were: 85.42% normal traffic, 80.77% DoS, 90.41% Probe, 91.78% U2R and 83.25% R2L

Different approaches for the detection of SSH anomalous connections

The Secure Shell Protocol (SSH) is a well-known standard protocol, mainly used for remotely accessing shell accounts on Unix-like operating systems to perform administrative tasks. As a result, the SSH service has been an appealing target for attackers, aiming to guess root passwords performing dictionary attacks or to directly exploit the service itself. To identify such situations, this article addresses the detection of SSH anomalous connections from an intrusion detection perspective. The main idea is to compare several strategies and approaches for a better detection of SSH-based attacks. To test the classification performance of different classifiers and combinations of them, SSH data coming from a real-world honeynet are gathered and analysed. For comparison purposes and to draw conclusions about data collection, both packet-based and flow data are analysed. A wide range of classifiers and ensembles are applied to these data, as well as different validation schemes for better analysis of the obtained results. The high-rate classification results lead to positive conclusions about the identification of malicious SSH connections

Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold

[[abstract]]Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120610~20120615[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australi

A New Multivariate Correlation Study for Detection of Denial-of-Service Attack

We present a attack detection system that utilizes Multivariate Correlation Analysis (MCA) for precise system traffic portrayal by removing the geometrical relationships between's system traffic highlights. Our MCA-based DoSattack identification framework utilizes the rule of abnormality based detection in attack acknowledgment. This makes our answer equipped for distinguishing known and obscure DoSattacks adequately by learning the examples of real system traffic as it were. Besides, a triangle-zone based system is proposed to upgrade and to accelerate the procedure of MCA. The adequacy of our proposed location framework is assessed utilizing KDD Cup 99 dataset, and the impacts of both non-standardized information and standardized information on the execution of the proposed identification framework are analyzed

Improved Mca Based Dos Attack Detection

A denial of service (DoS) attack is a malicious attempt to make a server or a network resource unavailable to users, usually by temporarily interrupting or suspending the services of a host connected to the Internet Interconnected systems, such as Web servers, database servers, cloud computing servers etc, are now under threads from network attackers. As one of most common and aggressive means, Denial-of-Service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses Multivariate Correlation Analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly-based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 dataset, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy

Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).&nbsp