ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems

We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network services. The false positives raised by the NIDS analyzing the incoming traffic (which can be either signature- or anomaly-based) are reduced by correlating them with the output anomalies. We designed our architecture for TCP-based network services which have a client/server architecture (such as HTTP). Benchmarks show a substantial reduction of false positives between 50% and 100%

APHRODITE: an Anomaly-based Architecture for False Positive Reduction

We present APHRODITE, an architecture designed to reduce false positives in network intrusion detection systems. APHRODITE works by detecting anomalies in the output traffic, and by correlating them with the alerts raised by the NIDS working on the input traffic. Benchmarks show a substantial reduction of false positives and that APHRODITE is effective also after a "quick setup", i.e. in the realistic case in which it has not been "trained" and set up optimall

ATLANTIDES: An Architecture for Alert Verification in Network Intrusion Detection Systems

We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network services. The false positives raised by the NIDS analyzing the incoming traffic (which can be either signature- or anomaly-based) are reduced by correlating them with the output anomalies. We designed our architecture for TCP-based network services which have a client/server architecture (such as HTTP). Benchmarks show a substantial reduction of false positives between 50% and 100%

Data Mining Techniques Used in Cyber Security

Data mining is the way toward identifying patterns in big datasets. Data mining methods are vigorously utilized in logical research and additionally in business, generally to accumulate measurements and profitable data to upgrade client relations and marketing techniques. Data mining has likewise demonstrated a helpful apparatus in cyber security for finding vulnerabilities and social affair pointers for base lining

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

Intrusion Detection System with Data Mining Approach: A Review

Despite of growing information technology widely, security has remained one challenging area for computers and networks. Recently many researchers have focused on intrusion detection system based on data mining techniques as an efficient strategy. The main problem in intrusion detection system is accuracy to detect new attacks therefore unsupervised methods should be applied. On the other hand, intrusion in system must be recognized in realtime, although, intrusion detection system is also helpful in off-line status for removing weaknesses of network2019;s security. However, data mining techniques can lead us to discover hidden information from network2019;s log data. In this survey, we try to clarify: first,the different problem definitions with regard to network intrusion detection generally; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of erent approaches; and how several prominent solutions tackle different problems

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules