Intelligent Network Management and Control - Badr Benmammar

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their location in the network. The intrusion detection system can be deployed as a network- or host-based system in order to detect the anomalies. Abusive use is detected based on the correspondence between known models of hostile activities and the database of previous attacks. These models are very effective for identifying known attacks and vulnerabilities, but less relevant in identifying new security threats. Anomaly detection looks for something rare or uncommon, applying statistical or intelligent measurements to compare the current activity to previous knowledge. Intrusion detection systems rely on the fact that they often need many data for the artificial learning algorithms. They generally require more computer resources, as several metrics are often preserved and must be updated for each system activity (Ahmad et al. 2016). The intrusion detection expert system (IDES) (Lunt 1993) developed by Stanford Research Institute (SRI) formulates expert knowledge on the known models of attack and vulnerabilities of the system in the form of if–then rules. The time-based inductive machine (Teng and Chen 1990) learns several sequential models to ensure the detection of anomalies in a network. Several approaches using the artificial neural networks for intrusion detection systems have been proposed (Kang and Kang 2016; Kim et al. 2016; Vinayakumar et al. 2017; Hajimirzaei and Navimipour 2019). AI-based techniques are categorized in various classes (Mukkamala and Sung 2003a; Novikov et al. 2006).

      1.3.1. Techniques based on decision trees

      Decision trees are powerful and widespread nonparametric learning tools used for classification and prediction problems. Their purpose is to create a model that predicts the values of the target variable, relying on a set of sequences of decision rules deduced from learning data. Rai et al. (2016) have developed an algorithm based on the C4.5 decision tree approach. The most relevant characteristics are selected by means of information gain and the fractional value is selected so that it renders the classifier unbiased with respect to the most frequent values. In the work of Sahu and Babu (2015), a database referred to as ”Kyoto 2006+” is used for the experiments. In Kyoto 2006+, each instance is labeled as “normal” (no attack), “attack” (known attack) and “unknown attack”. The Decision Tree algorithm (J48) is used to classify the packets. Experiments confirm that the generated rules operate with 97.2% accuracy. Moon et al. (2017) proposed an intrusion detection system based on decision trees using packet behavior analysis to detect the attacks. Peng et al. (2018) proposed a technique that involves a preprocessing for data digitization, followed by their normalization, in order to improve detection efficiency. Then a method based on decision trees is used.

      1.3.2. Techniques based on data exploration

      Data exploration aims to eliminate the manual elements used for the design of intrusion detection systems. Various data exploration techniques have been developed and widely used. The main data exploration techniques are presented in the following sections.

      Fuzzy logic has been used in the field of computer networks security, particularly for intrusion detection (Idris and Shanmugam 2005; Shanmugavadivu and Nagarajan 2011; Balan et al. 2015; Kudłacik et al. 2016; Sai Satyanarayana Reddy et al. 2019), for two main reasons. First, several quantitative parameters used in the context of intrusion detection, for example processor use time and connection interval, can be potentially considered as fuzzy variables. Second, the security concept is itself fuzzy. To put it differently, the fuzzy concept helps in preventing a sharp distinction between normal and abnormal behaviors. Kudłacik et al. (2016) have applied fuzzy logic for intrusion detection. The proposed solution analyzes the user activity over a relatively short period of time, creating a local user profile. A more in-depth analysis involves the creation of a more general structure based on a defined number of local user profiles, known as a “fuzzy profile”. The fuzzy profile represents the behavior of the computer system user. Fuzzy profiles are directly used in order to detect user behavior anomalies, and therefore potential intrusions. Idris and Shanmugam (2005) proposed a modified FIRE system. It is a mechanism for the automation of the fuzzy rule generation process and the reduction of human intervention making use of AI techniques.

      1.3.2.2. Genetic algorithms

      Genetic algorithms are techniques derived from genetics and natural evolution, which have been used to find approximate solutions to optimization and search problems. The main advantages of genetic algorithms are their flexibility and robustness as global search method. As for drawbacks, they are computationally time-consuming, as they handle several solutions simultaneously. Genetic algorithms have been used in various manners in the field of intrusion detection (Hoque et al. 2012; Aslahi-Shahri et al. 2016; Hamamoto et al. 2018). Hoque et al. (2012) presented an intrusion detection system using a genetic algorithm to effectively detect anomalies in the network. Aslahi-Shahri et al. (2016) proposed a hybrid method that uses support vector machines and genetic algorithms for intrusion detection. The results indicate that this algorithm can reach a 97.3% true positive rate and a 1.7% false positive rate.

      1.3.3. Rule-based techniques

      Turner et al. (2016) developed an algorithm for monitoring the enabled/disabled state of the rules of an intrusion detection system based on signatures. The algorithm is implemented in Python and runs on Snort (Roesch 1999). Agarwal and Joshi (2000) proposed a general framework in two stages for learning a rule-based model (PNrule) in order to learn classifier models on a set of data. They extensively used various distributions of classes in the learning data. The KDD Cups database was used for learning and testing their system.

      1.3.4. Machine learning-based techniques

      Machine learning can be defined as the capacity of a program to learn and improve the performances of a series of tasks in time. Machine learning techniques focus on the creation of a system model that improves its performances relying on the previous results. Furthermore, it can be said that machine learning–based systems have the capacity to handle the execution strategy depending on the new inputs. The main machine learning techniques are presented in the following sections.

      1.3.4.1. Artificial neural networks

      Artificial neural networks learn to predict the behavior of various system users. If correctly designed and implemented, neural networks can potentially solve several problems encountered by rule-based approaches. The main advantage of neural networks is their tolerance to inaccurate data and uncertain information and their capacity to deduce solutions without previous knowledge on data regularities. Cunningham and Lippmann (2000) of MIT Lincoln Laboratory conducted a number of tests using neural networks. The system searched for attack-specific key words specific in the network traffic. In Ponkarthika and Saraswathy (2018), a model of intrusion detection system is explored as a function of deep learning. Long–short term memory (LSTM) architecture was applied to a recurrent neural network for the learning of an intrusion detection system using the KDD Cup 1999 dataset.

      1.3.4.2. Bayesian networks

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