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Intrusion detection systems (IDSs) are currently drawing a great amount of interest as a key part of system defense. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. To distinguish the activities of the network traffic that the intrusion and normal is very difficult and to need much time consuming. An analyst must review all the data that large and wide to find the sequence of intrusion on the network connection. Therefore, it needs a way that can detect network intrusion to reflect the current network traffics. In this study, a novel method to find intrusion characteristic for IDS using genetic algorithm machine learning of data mining technique was proposed. Method used to generate of rules is classification by Genetic
algorithm of decision tree. These rules can determine of intrusion characteristics then to implement in the genetic algorithm as that besides detecting the existence of intrusion also can execute by doing deny of intrusion as prevention.

Approaches for intrusion detection can be broadly divided into two types: misuse detection and anomaly detection. In misuse detection system, all known types of attacks (intrusions) can be detected by looking into the predefined intrusion patterns in system audit traffic. In case of anomaly detection, the system first
learns a normal activity profile and then flags all system events that do not match with the already established profile. The main advantage of the misuse detection is its capability for high detection rate with a difficulty in finding the new or unforeseen attacks. The advantage of anomaly detection lies in the ability to identify the novel (or unforeseen) attacks at the expense of high false positive rate. Network monitoring-based machine learning techniques have been involved in diverse fields. Using bi-directional long-short-term-memory neural networks, a social media network monitoring system is proposed for analyzing and detecting traffic accidents. The proposed method retrieves traffic-related information from social media (Facebook and Twitter) using query-based crawling: this process collects sentences related to any traffic events, such as jams, road closures, etc. Subsequently, several pre-processing techniques are carried out, such as steaming, tokenization, POS tagging and segmentation, in order to transform the
retrieved data into structured form. Thereafter, the data are automatically labeled as ‘traffic‘ or ‘non-traffic‘, using a latent Dirichlet allocation (LDA) algorithm. Traffic- labeled data are analyzed into three types; positive, negative, and neutral. The output from this stage is a sentence labeled according to whether it is traffic or non-traffic, and with the polarity of that traffic sentence (positive, negative or
neutral). Then, using the bag-of-words (BoW) technique, each sentence is transformed into a one-hot encoding representation in order to feed it to the Bi-directional LSTM neural network (Bi-LSTM). After the learning process, the neural networks perform multi-class classification using the softmax layer in order to
classify the sentence in terms of location, traffic event and polarity types. The proposed method compares different classical machine learning and advanced deep learning approaches in terms of accuracy, F-score and other criteria.

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