Detecting End-Point (EP) Man-In-The-Middle (MITM) Attack based on ARP Analysis: A Machine Learning Approach

End-Point (EP) Man-In-The-Middle (MITM) attack is a well-known threat in computer security. This attack targets the flow of information between endpoints. An attacker is able to eavesdrop on the communication between two targets and can either perform active or passive monitoring; this affects the confidentiality and integrity of the data flow. Several techniques have been developed by researchers to address this kind of attack. With the current emergence of machine learning (ML) models, we explore the possibility of applying ML in EP MITM detection. Our detection technique is based on Address Resolution Protocol (ARP) analysis. The technique combines signal processing and machine learning in detecting EP MITM attack. We evaluated the accuracy of the proposed technique using linear-based ML classification models. The technique proved itself to be efficient by producing a detection accuracy of 99.72%

DESIGN OF A MINIMAL OVERHEAD CONTROL TRAFFIC TOPOLOGY DISCOVERY AND DATA FORWARDING PROTOCOL FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS

Software-defined networking is a novel concept that is ported into wireless sensor networks to make them more manageable and customizable. unfortunately, the topology discovery and maintenance processes generate high overhead control packet exchange between the sensor nodes and the central controller leading to a deterioration of the network's performance. In this paper, a novel minimal overhead control traffic topology discovery and data forwarding protocol is proposed and detailed. The proposed protocol requires some changes to the topology discovery protocol implemented in SDN-WISE to improve its performance. The proposed protocol has been implemented within the IT-SDN framework for evaluation. The results show reduced overhead control traffic and increase, of about 20%, data packet delivery rate over the protocol in SDN-WISE

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment

A Lightweight Messaging Protocol for Internet of Things Devices

The move towards intelligent systems has led to the evolution of IoT. This technological leap has over the past few years introduced significant improvements to various aspects of the human environment, such as health, commerce, transport, etc. IoT is data-centric; hence, it is required that the underlying protocols are scalable and sufficient to support the vast D2D communication. Several application layer protocols are being used for M2M communication protocols such as CoAP, MQTT, etc. Even though these messaging protocols have been designed for M2M communication, they are still not optimal for communications where message size and overhead are of much concern. This research paper presents a Lightweight Messaging Protocol (LiMP), which is a minified version of CoAP. We present a detailed protocol stack of the proposed messaging protocol and also perform a benchmark analysis of the protocol on some IoT devices. The proposed minified protocol achieves minimal overhead (a header size of 2 bytes) and has faster point-to-point communication from the benchmark analysis; for communication over LAN, the LiMP-TCP outperformed the CoAP-TCP by an average of 21% whereas that of LiMP-UDP was over 37%. For a device to remote server communication, LiMP outperformed CoAP by an average of 15%