A hybrid intrusion detection system

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors\u27 accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors.;Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed.;This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks

A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.publishedVersio

Novel Tsetlin Machine Mechanisms for Logic-based Regression and Classification with Support for Continuous Input, Clause Weighting, Confidence Assessment, Deterministic Learning, and Convolution

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Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for  Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison

Towards privacy preserving cooperative cloud based intrusion detection systems

Les systèmes infonuagiques deviennent de plus en plus complexes, dynamiques et vulnérables aux attaques. Par conséquent, il est de plus en plus difficile pour qu'un seul système de détection d'intrusion (IDS) basé sur le cloud puisse repérer toutes les menaces, en raison des lacunes de connaissances sur les attaques et leurs conséquences. Les études récentes dans le domaine de la cybersécurité ont démontré qu'une coopération entre les IDS d'un nuage pouvait apporter une plus grande efficacité de détection dans des systèmes informatiques aussi complexes. Grâce à cette coopération, les IDS d'un nuage peuvent se connecter et partager leurs connaissances afin d'améliorer l'exactitude de la détection et obtenir des bénéfices communs. L'anonymat des données échangées par les IDS constitue un élément crucial de l'IDS coopérative. Un IDS malveillant pourrait obtenir des informations confidentielles d'autres IDS en faisant des conclusions à partir des données observées. Pour résoudre ce problème, nous proposons un nouveau système de protection de la vie privée pour les IDS en nuage. Plus particulièrement, nous concevons un système uniforme qui intègre des techniques de protection de la vie privée dans des IDS basés sur l'apprentissage automatique pour obtenir des IDS qui respectent les informations personnelles. Ainsi, l'IDS permet de cacher des informations possédant des données confidentielles et sensibles dans les données partagées tout en améliorant ou en conservant la précision de la détection. Nous avons mis en œuvre un système basé sur plusieurs techniques d'apprentissage automatique et de protection de la vie privée. Les résultats indiquent que les IDS qui ont été étudiés peuvent détecter les intrusions sans utiliser nécessairement les données initiales. Les résultats (c'est-à-dire qu'aucune diminution significative de la précision n'a été enregistrée) peuvent être obtenus en se servant des nouvelles données générées, analogues aux données de départ sur le plan sémantique, mais pas sur le plan synthétique.Cloud systems are becoming more sophisticated, dynamic, and vulnerable to attacks. Therefore, it's becoming increasingly difficult for a single cloud-based Intrusion Detection System (IDS) to detect all attacks, because of limited and incomplete knowledge about attacks and their implications. The recent works on cybersecurity have shown that a co-operation among cloud-based IDSs can bring higher detection accuracy in such complex computer systems. Through collaboration, cloud-based IDSs can consult and share knowledge with other IDSs to enhance detection accuracy and achieve mutual benefits. One fundamental barrier within cooperative IDS is the anonymity of the data the IDS exchanges. Malicious IDS can obtain sensitive information from other IDSs by inferring from the observed data. To address this problem, we propose a new framework for achieving a privacy-preserving cooperative cloud-based IDS. Specifically, we design a unified framework that integrates privacy-preserving techniques into machine learning-based IDSs to obtain privacy-aware cooperative IDS. Therefore, this allows IDS to hide private and sensitive information in the shared data while improving or maintaining detection accuracy. The proposed framework has been implemented by considering several machine learning and privacy-preserving techniques. The results suggest that the consulted IDSs can detect intrusions without the need to use the original data. The results (i.e., no records of significant degradation in accuracy) can be achieved using the newly generated data, similar to the original data semantically but not synthetically