TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

A Novel Secure Image Hashing based on Reversible Watermarking for Forensic Analysis

Abstract. Nowadays, digital images and videos have become increasingly popular over the Internet and bring great social impact to a wide audience. In the meanwhile, technology advancement allows people to easily alter the content of digital multimedia and brings serious concern on the trustworthiness of online multimedia information. In this paper, we propose a new framework for multimedia forensics by using compact side information based on reversible watermarking to reconstruct the processing history of a multimedia data. Particularly, we focus on a secure reversible watermarking to make the image hash more secure and robust. Moreover, we introduce an algorithm based on Radon transform and scale space theory to effectively estimate the parameters of geometric transforms and to detect local tampering. The experimental results show that the quality of the embedded image is very high and the positions of the tampered parts are identified correctly

An Improved Greedy Forwarding Routing Protocol for Cooperative VANETs

Part 2: Asian Conference on Availability, Reliability and Security (AsiaARES)International audienceMost researches in the VANETs domain concentrate on the development of communication routing protocols. However, it is not effective to apply the existing routing protocols of MANETs to those of VANETs. In this paper, we propose a new greedy forward routing protocol which leverages real time traffic flow information to create a routing policy. Based on this routing policy, the proposed protocol alleviates the influence of high dynamic topology and decreases the average delivery delay on VANETs

Blockchain technology for providing an architecture model of decentralized personal health information

The personal health information (PHI) is an activity among the health-care providers and the patients in terms of managing the data which is sensitive to the parties. The PHI data have been maintained by multiple health-care providers, thus resulting in separated data. Moreover, the PHI data are stored in the provider’s database, hence the patients have no authority to manage their own information. Therefore, in this article, we propose a conceptual model for managing the PHI data which is derived from several health-care providers by relying on the blockchain technology in the peer-to-peer overlay network. In addition, we elaborate the security analysis that might be occurring in the proposed model. By leveraging on our model, it allows the patients and the providers to collect effectively the PHI data onto a single view as well guarantee of data integrity. The blockchain offers an immutable of the data record without having to trust a third party. The experimental results show that the proposed approach is promising to be developed due to the high success rate in terms of data dissemination

Design and Analysis of a Fragile Watermarking Scheme Based on Block-Mapping

Part 2: WorkshopInternational audienceDue to the wide variety of attacks and the difficulties of developing an accurate statistical model of host features, the structure of the watermark detector is derived by considering a simplified channel model. In this paper, we present a fragile watermarking based on block-mapping mechanism which can perfectly recover the host image from its tampered version by generating a reference data. By investigating characteristics of watermark detector, we make an effective analysis such as fragility against robustness measure and distinguish its property. In particular, we derive a watermark detector structure with simplified channel model which focuses on the error probability versus watermark-to-noise-ratio curve and describes a design by calculating the performance of technique, where attacks are either absent or as noise addition

Toward Privacy-Preserving Shared Storage in Untrusted Blockchain P2P Networks

The shared storage is essential in the decentralized system. A straightforward storage model with guaranteed privacy protection on the peer-to-peer network is a challenge in the blockchain technology. The decentralized storage system should provide the privacy for the parties since it contains numerous data that are sensitive and dangerous if misused by maliciously. In this paper, we present a model for shared storage on a blockchain network which allows the authorized parties to access the data on storage without having to reveal their identity. Ring signatures combined with several protocols are implemented to disguise the signer identity thereby the observer is unlikely to determine the identity of the parties. We apply our proposed scheme in the healthcare domain, namely, decentralized personal health information (PHI). In addition, we present a dilemma to improve performance in a decentralized system

On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks

The conventional architecture of vehicular ad hoc networks (VANETs) with a centralized approach has difficulty overcoming the increasing complexity of intelligent transportation system (ITS) applications as well as challenges in providing large amounts of data storage, trust management, and information security. Therefore, vehicular edge computing networks (VECNets) have emerged to provide massive storage resources with powerful computing on network edges. However, a centralized server in VECNets is insufficient due to potential data leakage and security risks as it can still allow a single point of failure (SPoF). We propose consortium blockchain and smart contracts to ensure a trustworthy environment for secure data storage and sharing in the system to address these challenges. Practical byzantine fault tolerance (PBFT) is utilized because it is suitable for consortium blockchain to audit publicly, store data sharing, and records the whole consensus process. It can defend against system failures with or without symptoms to reach an agreement among consensus participants. Furthermore, we use an incentive mechanism to motivate the vehicle to contribute and honestly share their data. The simulation results satisfy the proposed model’s design goals by increasing vehicular networks’ performance in general

An Integration of PSO-based Feature Selection and Random Forest for Anomaly Detection in IoT Network

The most challenging research topic in the field of intrusion detection system (IDS) is anomaly detection. It is able to repeal any peculiar activities in the network by contrasting them with normal patterns. This paper proposes an efficient random forest (RF) model with particle swarm optimization (PSO)-based feature selection for IDS. The performance model is evaluated on a well-known benchmarking dataset, i.e. NSL-KDD in terms of accuracy, precision, recall, and false alarm rate(FAR) metrics. Furthermore, we evaluate the significance differencesbetween the proposed model and other classifiers, i.e. rotation forest (RoF)and deep neural network (DNN) using statistical significance test. Basedon the statistical tests, the proposed model significantly outperforms otherclassifiers involved in the experiment