Fusion of Feature Selection and Random Forest for an Anomaly-Based Intrusion Detection System

Головною концепцією і предметом дослідження є виявлення різного типу обширних атак в інфраструктурі IoT, огляд представленої моделі, методів та існуючих передових систем виявлення вторгнень. Метою даної роботи є запропонування системи виявлення вторгнень в режимі реального часу, яка буде навчена на наборі з великим обсягом даних, за допомогою нейронної мережі з використанням ансамблевого методу машинного навчання. Предметом дослідження є огляд існуючих методів та моделей виявлення широкомасштабної атаки та запропонування власного рішення системи виявлення вторгнень, яка буде базуватися на методі виявлення аномалій та нейронної мережі. Висновок. Побудована система виявлення вторгнень, яка аналізує інтернет трафік, вилучає ознаки з пакету, обробляє їх та передбачує різні види атак, а також характеризує їх за типом. Загрозу безпеці можна вважати основною критичною проблемою для пристроїв IoT, тому використання таких систем зменшує ризики втрати даних.

Improvement of attack detection performance on the internet of things with PSO-search and random forest

The IEEE 802.11 wireless networks are organized either in infrastructure mode or ad-hoc mode. These wireless networks are very common, because of their portability and ease of access. In infrastructure mode of wireless networks, wireless clients have to be aware of the environments due to vulnerability and various types of attacks that can made by wireless intruders. Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access (WPA) security protocols are used to protect any wireless infrastructure against wireless intruders. The main problem of wireless network is to maintain the network security. Firewalls are not much capable to secure network from attacks because firewall can only detect the attacks which come from outside of the network. Therefore, Intrusion Detection System (IDS) plays an important role in network security. In general Intrusion Detection System (IDS) is used to detect unauthorized access, malicious access point and various types of wireless attacks in IEEE 802.11 infrastructure mode of wireless networks. Network based Wireless Intrusion Detection system (WIDS) is designed to detect Denial of Service (DOS) attack and Malicious Access Point (Evil Twin Attack) in Wi-Fi networks. Because DOS attack and Evil Twin Attack are the most significant attacks in IEEE 802.11 networks. In this work, a diverse detection algorithm is proposed to detect DOS attack and Evil Twin Attack which is based on number of deauthentication frames and beacon frames and some other fields. In this project, an experimental setup for wireless network infrastructure with one access point (AP) and three or four number of wireless clients are used to design Wireless Intrusion Detection System (WIDS). In that design, one client act as an attacker and another one client act as detector. In this work, Kali Linux based attack software tool is used to register wireless clients to launch attacks on the network. The set of commends are used to configure on the designed infrastructure wireless networks for testing the attacks and solutions

Intrusion detection system (IDS) using machine learning approach is getting popularity as it has an advantage of getting updated by itself to defend against any new type of attack. Another emerging technology, called internet of things (IoT) is taking the responsibility to make automated system by communicating the devices without human intervention. In IoT based systems, the wireless communication between several devices through the internet causes vulnerability for different security threats. This paper proposes a novel unified intrusion detection system for IoT environment (UIDS) to defend the network from four types of attacks such as: exploit, DoS, probe, and generic. The system is also able to detect normal category of network traffic. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 data sets which are unable to detect new type of attacks. In this paper, UNSW-NB15 data set is considered as the benchmark dataset to design UIDS for detecting malicious activities in the network. The performance analysis proves that the attack detection rate of the proposed model is higher compared to two existing approaches ENADS and DENDRON which also worked on UNSW-NB15 data set.

The intrusion detection system (IDS) deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. To overcome these limitations, we introduce the concept of lightweight IDS. The lightweight IDSs are small, powerful, and flexible enough to be used as permanent elements of the network security infrastructure. In this paper, we propose a novel concept for building lightweight IDS based on two different approaches. The first approach is using a features selection approach by applying fuzzy enhanced support vector decision function (Fuzzy ESVDF) algorithm. This approach is able to improve system efficiency. The second approach is using IDS classification scheme. The IDS classification scheme divides the detection process into four types according to the TCP/IP network model (application layer IDS, transport layer IDS, network layer IDS, and link layer IDS). This IDS classification scheme enhances an organizationpsilas ability to detect most types of attack (improving system accuracy and generality). Also, it improves IDS scalability and extendibility. To design the proposed system, several experiments have been conducted, and they indicate that the proposed lightweight IDS can deliver satisfactory system performance.

Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks especially a multi-step attack. In this paper, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced. MSCAD includes two multi-step scenarios; the first scenario is a password cracking attack, and the second attack scenario is a volume-based Distributed Denial of Service (DDoS) attack. The MSCAD was assessed in two manners; firstly, the MSCAD was used to train IDS. Then, the performance of IDS was evaluated in terms of G-mean and Area Under Curve (AUC). Secondly, the MSCAD was compared with other free open-source and public datasets based on the latest keys criteria of a dataset evaluation framework. The results show that IDS-based MSCAD achieved the best performance with G-mean 0.83 and obtained good accuracy to detect the attacks. Besides, the MSCAD successfully passing twelve keys criteria.

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

Cloud computing is a framework that enables end users to connect highly effective services and applications over the internet effortlessly. In the world of cloud computing, it is a critical problem to deliver services that are both safe and dependable. The best way to lessen the damage caused by entry into this environment is one of the primary security concerns. The fundamental advantage of a cooperative approach to intrusion detection system (IDS) is a superior vision of an action of network attack. This paper proposes a random forest (RF) algorithm with a hill-climbing algorithm (RFHC) to improve intrusion detection at the endpoint and network. Initially, it is used for feature selection, and the next process is to separate the intrusions detection. The feature selection is maintained by the hill climbing (HC) algorithm that chooses the best features. Then, we utilize the RF algorithm to separate the intrusion efficiently. The experimental results depict that the RFHC mechanism reached more acceptable results regarding recall, precision, and accuracy than a baseline mechanism. Moreover, it minimizes the miss detection ratio and enhances the intrusion detection ratio.

Cyber attacks have become increasingly dangerous and quite common in world of internet.so the demand of cyber security has been increased to safeguard the information personal data and computer network .now a days everything has been computerized and transaction are done online thus economic reimbursement are increasing due to cyber attacks as the technological evolution comes the progress of cyber-crime increases thus develops the new types of attacks are reported throughout the study the exact number of attack type determination is quite difficult however the most common cyber attacks are described in this article .the aim of this study is to survey and convincing manner review the strength and weakness presented in the field of cyber security. the article aim is to analyze various type of attacks existing to understand the state of being exposed to the possibility of being attacked. The main goal is to handle cyber attacks.

Intrusion Detection System (IDS) has been a challenging area to be developed in recent years since there are so many kinds of network attacks that are difficult to be classified by system. Moreover, there is always the possibility of the upcoming new attack. Therefore, Intrusion detection system must be improved by new classifiers to obtain the best results not only for classifying attack but also recognizing a new type of attack in an advanced way. These developments will be beneficial for managing security systems in an upcoming era. There are several classifier algorithms for Intrusion Detection System such as Support Vector Machine (SVM) and Fuzzy Kernel C-Means (FKCM). SVM classifier is known as one of the techniques to improve efficiency in attack detection while FKCM is famous because it provides better results to reduce the number of selected data features. Each of classifiers is using kernel trick to improve the classifying matter. In this study, we will compare proposed model which are FKCM with rbf, FKCM with polynomial kernel, SVM with rbf, and SVM with polynomial kernel to find a better result that could increase the accuracy of classifying network attacks. KDD Cup 1999 will be used to evaluate each model. In this study, the use of SVM with polynomial kernel brings the best result with 100 % accuracy can be obtained in 1.9 second.

Wireless sensor networks (WSNs) are gaining more and more interest in the research community due to their unique characteristics and have a lots of interesting applications. Besides energy consumption security of WSN is being a critical issue nowadays. This is because WSNs are easily effected by various types of attacks and node compromises so they require security mechanisms to defend against them. An intrusion detection system (IDS) is one such solution to the problem. There are many research works focused in the area of signature based anomaly based IDS. Based on these malicious activities there is a need of addressing a new or modified versions of IDS algorithm. The proposed algorithm, abbreviated as AD(anomaly detection) algorithm has dedicated procedures for secure cluster formation, periodic re-clustering, and efficient cluster member monitoring and then the detection of different attacks. The performance of AD algorithm in identifying and detecting intrusions using a rule-based anomaly detection scheme is studied via simulations.

Attack-aware Security Function Management

<p class="Abstract">Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.</p>

The world has experienced a radical change due to the internet. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. This safety is handled using systems to detect network intrusion called Intrusion Detection Systems (IDS). Machine learning techniques are being implemented to improve these systems. In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a good one for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. Nevertheless, most researchers have focused on the confusion matrix as measurements of classification performance. Therefore, many papers use this matrix to present a detailed comparison with the dataset, data preprocessing, feature selection technique, algorithms classification and performance evaluation. The goal of this paper is to present a comparison of application of different Machine Learning algorithms used to build and improve intrusion detection systems in terms of confusion matrix, accuracy, recall, precision, FAR, specificity and sensitivity using the UNSW-NB15 Dataset. Furthermore, we introduce some lesson learnt to shoot more researchers in their future works.

In this paper we study the influence of key-scheduling algorithms on the strength of blockciphers. We show that the key-scheduling algorithms of many blockciphers inherit obvious relationships between keys, and use these key relations to attack the blockciphers. Two new types of attacks are described: New chosen plaintext reductions of the complexity of exhaustive search attacks (and the faster variants based on complementation properties), and new low-complexity chosen key attacks. These attacks are independent of the number of rounds of the cryptosystems and of the details of the F-function and may have very small complexities. These attacks show that the key-scheduling algorithm should be carefully designed and that its structure should not be too simple. These attacks are applicable to both variants of LOKI and to Lucifer. DES is not vulnerable to the related keys attacks since the shift pattern in the key-scheduling algorithm is not the same in all the rounds.