Intrusion detection based on bidirectional long short-term memory with attention mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score

An Adaptive Blockchain based Three-Tier Architecture in Fog based IoT for Personal Healthcare Data Application

To protect patient health data (PHD) and ensure the security of healthcare IoT devices, this paper presents an Advanced Signature-Based Encryption algorithm (ASE), a blockchain analytical model, a mathematical framework, and an Adaptive Fog Computing based Three-tier Architecture (AFCTTA). The aim is to enable safe access to real-time services and IoT for end users. This AFCTTA was constructed on a blockchain platform, providing trustworthy data transmission between patients, clinicians, fog nodes, and IoT. Additionally, a decentralized fog computing-based blockchain analytical model along with a mathematical framework were produced to ensure secure transfer of data and transactions within healthcare IoT. To ensure secure communication between devices and fog nodes, a private block chain was implemented in order to validate certificates and keys. As an added security measure, an ASE method was devised. This algorithm utilizes War Optimization Strategy (WOA) to select optimal keys for securing data from heterogeneous and homogeneous IoT healthcare equipment. Through its encryption process utilizing various cryptographic techniques, all traffic is encrypted before being decrypted once it reaches its intended destination. To validate its proposed approach, UCI machine library is collecting health care data. To execute this method, Python is utilized and compared to traditional algorithms such as Rivest-Shamir-Adleman (RSA), Elliptical Curve Cryptography (ECC), and Tiny Lightweight Symmetric Encryption-Aquila Optimization Algorithm (TLSE-AOA)

Collusion attacks mitigation in internet of things: a fog based model

© 2017, Springer Science+Business Media, LLC. Collusion attacks are among the major security concerns nowadays due to the growth exposure in networks and communications. Internet of Things (IoT) environments are an attractive target for such type attacks. This paper discusses the problem of collusion attacks in IoT environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detection collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based on Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers. The model uses fog computing layer for realtime monitoring and detection of collusion attacks in IoT environments. Moreover, the model uses a software defined systems layer to add a degree of flexibility for configuring Fog nodes to enable them to detect various types of collusion attacks. The paper provides algorithms, theorems, lemmas and mathematical proofs of the proposed model. Furthermore, the it highlights the possible overhead on fog nodes and network when applying the proposed model, and claims that fog layer infrastructure can provide the required resources for the scalability of the model. The experiments show how the proposed model can keep track of malicious nodes while moving from one cluster to other clusters in IoT environments in contrary to the models used in WSNs. Moreover, the experiments show that the proposed model can bear the computation overhead effectevilly, and reduces the power consumption of aggregator nodes in comparison to the models used in WSNs

VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

Detecting IoT Attacks Using an Ensemble Machine Learning Model

Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve

An Evolutionary-Based Algorithm for Smart-Living Applications Placement in Fog Networks

Fog computing is an emerging model, complementing the cloud computing platform, introduced to support the Internet of Things (IoT) processing requests at the edge of the network. Smart-living IoT scenarios require the execution of multiple processing tasks at the edge of the network and leveraging on the Fog Computing approach results to be a worthwhile solution. Genetic Algorithms (GA) are a heuristic search and optimization class of techniques inspired by natural evolution. We propose two GA-based approaches for optimizing the processing task placement in a fog computing edge infrastructure aiming to support the Smart-living IoT nodes requests. The numerical results obtained in Matlab show that both GA-based approaches allow to maximize the covered areas while minimizing the resource wastage through the minimization of the overlapping area

Simulating IoT Workflows in DISSECT-CF-Fog

The modelling of IoT applications utilising the resources of cloud and fog computing is not straightforward because they have to support various trigger-based events that make human life easier. The sequence of tasks, such as performing a service call, receiving a data packet in the form of a message sent by an IoT device, and managing actuators or executing a computational task on a virtual machine, are often associated with and composed of IoT workflows. The development and deployment of such IoT workflows and their management systems in real life, including communication and network operations, can be complicated due to high operation costs and access limitations. Therefore, simulation solutions are often applied for such purposes. In this paper, we introduce a novel simulator extension of the DISSECT-CF-Fog simulator that leverages the workflow scheduling and its execution capabilities to model real-life IoT use cases. We also show that state-of-the-art simulators typically omit the IoT factor in the case of the scientific workflow evaluation. Therefore, we present a scalability study focusing on scientific workflows and on the interoperability of scientific and IoT workflows in DISSECT-CF-Fog