Internet of Things (IoTs) are set to revolutionize our lives and are widely being adopted nowadays. The IoT devices have a range of applications including smart homes, smart industrial networks and healthcare. Since these devices are responsible for generating and handling large amounts of sensitive data, the security of the IoT devices always poses a challenge. It is observed that a security breach could effect individuals and eventually the world at large. Artificial intelligence (AI), on the other hand, has found many applications and is widely being explored in providing security specifically for IoT devices. Malicious insider attack is the biggest security challenge associated with the IoT devices. Although, most of the research in IoT security has pondered on the means of preventing illegal and unauthorized access to systems and information; unfortunately, the most destructive malicious insider attacks that are usually a consequence of internal exploitation within an IoT network remains unaddressed. Therefore, the focus of this research is to detect malicious insider attacks in the IoT environment using AI. This research presents a lightweight approach for detecting insider attacks and has the capability of detecting anomalies originating from incoming data sensors in resource constrained IoT environments. The results and comparison show that the proposed approach achieves better accuracy as compared to the state of the art in terms of: a) improved attack detection accuracy; b) minimizing false positives; and c) reducing the computational overhead.

Internet of Things (IoT) is a system of interconnected devices that have the ability to monitor and transfer data to peers without human intervention. Authentication, Authorization and Audit Logs (AAA) are prime features of Network Security and easily attained in legacy systems, however, remains unachieved in IoT. The IoTs require due security considerations as the conventional security mechanisms are not optimized for such devices due to various aspects such as heterogeneity, resource constrained processing, storage and multiple factors. Additionally, the legacy systems are mostly centralized and thus introduce a single point of failure. In this research, a novel framework, FBASHI is presented that is based on fuzzy logic and blockchain technology to achieve AAA services. The proposed system is developed using Hyperledger that is a blockchain platform providing privacy and fast response capability, therefore, it is best suited for the healthcare IoT environments. This work proposes behavior driven adaptive security mechanism for healthcare IoTs and networks based on blockchain by utilizing fuzzy logic and presents a heuristic approach towards behavior driven adaptive security providing AAA services. FBASHI is implemented to analyze its security and practicality. Furthermore, a comparison is drawn with other blockchain-based solutions.

Intrusion Prevention Systems (IPS), capable of preventing the organizational network from a cyber-attack in addition to detecting it, are widely adopted by organizations to protect their networks from unauthorized access, attacks, and malicious activities. Similarly, Snort an open-source IPS is extensively used for effective network security monitoring and analysis. When functioning as an IPS, Snort can be deployed in inline mode within an organizational network, so that all the organizational network traffic travels through it, hence actively blocking or preventing malicious traffic in real-time. This requires Snort to process the network traffic fast enough to match the network traffic line rate. But the Snort IPS default data acquisition module i.e. advanced packet filtering (AF_PACKET) cannot process network traffic at the line rate that causes packet loss and network services disturbance. This research work discusses the technologies available to make Snort IPS process network traffic at line rate. Packet filtering framework (PF_RING) and data plane development kit (DPDK) are the most effective and widely used software technologies, whereas the Napatech smart network interface card (smartNIC) is a very efficient hardware technology for achieving line rate traffic processing. A throughput comparison shows that PF_RING and DPDK achieve a throughput close to 1G with 100% CPU utilization whereas Napatech smartNIC achieves full 1G throughput with CPU utilization of less than 5%. Furthermore, the integration of Snort IPS with the security information and event management (SIEM) system has been discussed for better attack detection in an organizational network.

Searchable Encryption is an emerging cryptographic technique that enables searching capabilities over the encrypted data on the cloud. In this paper, a novel searchable encryption scheme for the client-server architecture has been presented. The scheme exploits the properties of modular inverse to generate a probabilistic trapdoor which facilitates the searching over the secure inverted index table. We propose indistinguishability that is achieved by using the property of a probabilistic trapdoor. We design and implement a proof of concept prototype and test our scheme onto a real dataset of files. We analyze the performance of our scheme against our claim of the scheme being light weight. The security analysis yields that our scheme assures higher level of security as compared to other existing schemes.

The application of machine learning in healthcare, financial, social media, and other sensitive sectors not only involves high accuracy but privacy as well. Due to the emergence of the Cloud as a computation and one-to-many access paradigm; training and classification/inference tasks have been outsourced to Cloud. However, its usage is limited due to legal and ethical constraints regarding privacy. In this work, we propose a privacy-preserving neural networks-based classification model based on Homomorphic Encryption (HE) where the user can send an encrypted instance to the cloud and receive an encrypted inference from it to preserve the user’s query privacy. In contrast to existing works, we demonstrate the realistic limitations of HE for privacy-preserving machine learning by changing its parameters for enhanced security and accuracy. We showcase scenarios where the choice of HE parameters impedes accurate classification and present an optimized setting for achieving reliable classification. We present several results to demonstrate its effectiveness using MNIST dataset with highly improved inference time for a query as compared to the state of the art.

The Internet of Vehicles (IoV) is an incipient topic within the wider domain of the Internet of Things (IoT). Using this technology intelligent transportation systems (ITS) can be developed, whose main purpose is to ensure more safety, faster travel, reduced energy consumption and improved vehicle upkeep. Devices connected within the IoV transmit a vast amount of data, which leads to additional costs on the communication network along with data security concerns. This research considers the potential uses of blockchain technology for improving communication between independent vehicles. In this paper, a decentralized system is proposed to enhance the security and performance of financial transactions in the network of autonomous cars based on the Ethereum blockchain. Our system is divided into two modules: car registration and message alert generating. New vehicles are allowed on the network using the smart contract that has been written and published on the Ethereum Remix IDE. This is the contract code attached to the Metamask wallet. Another module, the message alert-generating module, allows an administrator to send alert messages to all registered cars via a web-based system to their wallet for an Ethereum gas cost. It ensures the reliability of data transferred between the self-driving cars through the use of blockchain-based systems. The data is, therefore, confirmed and vetted using the proof of work and stake consensus algorithm to bring out the truth. The transaction being made, the system will see to it that the transaction made is of integrity and trustable. By synching all these technologies and means of reaching an agreement, the blockchain secures not only the means of keeping and retaining the information of the kept within self-driving vehicles but also establishes a strong foundation of highest trust and transparency in the interaction between the vehicles on the network. Accordingly, a well-defined and reliable conceptual design for the communication systems of autonomous vehicles has been proposed. Future work and the unresolved issues of using blockchain for autonomous vehicles have also been deliberated over in detail.