Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models

A toolbox for Artificial Intelligence Algorithms in Cyber Attacks Prevention and Detection

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis Thesis provides a qualitative view on the usage of AI technology in cybersecurity strategy of businesses. It explores the field of AI technology today, and how it is a good technology to implement into Cyber Security. The Internet and Informational technology have transformed the world of today. There is no doubt that it has created huge opportunities for global economy and humanity. The fact that Businesses of today is thoroughly dependent on the Internet and Information Systems has also exposed new vulnerabilities in terms of cybercrimes performed by a diversity of hackers, criminals, terrorists, the state and the non-state actors. All Public, private companies and government agencies are vulnerable for cybercrimes, none is left fully protected. In the recent years AI and machine learning technology have become essential to information security, since these technologies can analyze swiftly millions of datasets and tracking down a wide range of cyber threats. Alongside With the increasingly growth of automation in businesses, is it realistic that cybersecurity can be removed from human interaction into fully independent AI Applications to cover the businesses Information System Architecture of businesses in the future? This is a very interesting field those resources really need to deep into to be able to fully take advantage of the fully potential of AI technology in the usage in the field of cybersecurity. This thesis will explore the usage of AI algorithms in the prevention and detection of cyberattack in businesses and how to optimize its use. This knowledge will be used to implement a framework and a corresponding hybrid toolbox application that its purpose is be to be useful in every business in terms of strengthening the cybersecurity environment

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table