GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

Secure Irrigation System for Olive Orchards Using Internet of Things

Smart irrigation system,also referred as precision irrigation system,is an attractive solution to save the limited water resources as well as to improve crop productivity and quality.In this work,by using Internet of things(IoT),we aim to design a smart irrigation system for olive groves.In such IoT system,a huge number of low-power and low-complexity devices(sensors,actuators)are interconnected.Thus,a great challenge is to satisfy the increasing demands in terms of spectral efficiency.Moreover,securing the IoT system is also a critical challenge,since several types of cybersecurity threats may pose.In this paper,we address these issues through the application of the massive multiple-input multiple-output(M-MIMO)technology.Indeed,M-MIMO is a key technology of the fifth generation(5G)networks and has the potential to improve spectral efficiency as well as the physical layer security.Specifically,by exploiting the available M-MIMO channel degrees of freedom,we propose a physical layer security scheme based on artificial noise(AN)to prevent eavesdropping.Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.

An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

The success of Internet of Things(IoT)deployment has emerged important smart applications.These applications are running independently on different platforms,almost everywhere in the world.Internet of Medical Things(IoMT),also referred as the healthcare Internet of Things,is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems.Indeed,with the impact of the COVID-19 pandemic,a large number of interconnected devices designed to create smart networks.These networks monitor patients from remote locations as well as tracking medication orders.However,IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks.In this paper,we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes.For this purpose,we develop a new algorithm for Intrusion Detection System(IDS)to detect malicious activities in the Internet of Medical Things.The proposed scheme minimizes as possible the number of attacks to ensure data security,and preserve confidentiality of gathered data.In order to check the viability of our approach,we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.