Malware white-box evasion attack is a serious threat to machine learning-based malware classification models, where an attacker carefully inserts perturbations into a malware executable at a test time to evade a target model. Previous research introduced different white-box evasion attacks, namely padding and slack attacks, to craft malware adversarial samples and evaluated them based on the perturbation size and their evasion rate against a target model. However, there is a lack of insights into how the malware file structure and type affect the adversarial malware sample generation and their respective evasion rate. In this work, we provide a comprehensive empirical analysis by factoring in the malware structure and the type. Our analysis quantifies slack space availability in various sections, exploring how the slack space can influence the robustness of detection techniques. We further assess the relationship between malware type and evasion rate to understand how different types of malware respond to evasion attacks. Additionally, we explore the connection between each malware type and the corresponding slack space availability, analyzing how these structural factors influence the evasion rates during adversarial attacks. In our experiments, adversarial malware samples were generated using two different algorithms: gradient descent and iterative gradient sign method. This detailed analysis enhances our understanding of evasion dynamics of adversarial attacks across malware types and different structural characteristics of binary malware files.

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it exposes them to cybersecurity threats and privacy concerns. A cyberattack on one farm can have widespread consequences, affecting the targeted farm as well as all member farms within a cooperative. For instance, a de-authentication attack prevents sensors from connecting to the network, obstructing the farming application from receiving real-time data. This disruption obstructs decision-making for all member farms that rely on this data from an attacked farm. Further, farmers have adopted digital twin (DT) technology that facilitates a virtual farm replica that encompasses vital aspects of farming, such as crop cultivation, soil composition and weather conditions. However, it’s critical to acknowledge that attackers can target these digital twins (DTs), potentially disrupting real physical farm operations.In this research, we address existing gaps by proposing a novel and secure architecture for Cooperative Smart Farming (CSF). First, we highlight the role of edge-based DTs in enhancing the efficiency and resilience of agricultural operations. To validate this, we develop a test environment for CSF, implementing various cyberattacks on both the DTs and their physical counterparts using different attack vectors. We collect two smart farming network datasets to identify potential threats. After identifying these threats, we focus on preventing the transmission of malicious data from compromised farms to the central cloud server. To achieve this, we propose a CNN-Transformer-based network anomaly detection model, specifically designed for deployment at the edge. As a proof of concept, we implement this model and evaluate its performance by varying the number of encoder layers. Additionally, we apply Post-Quantization to compress the model and demonstrate the impact of compression on its performance in edge environments. Finally, we compare the model’s performance with traditional machine learning approaches to assess its overall effectiveness.

The agriculture industry is extensive utilizing AI and data-driven systems for efficiency and automation, with the goal to meet the rising food demand. Individual farm owners can leverage agricultural cooperatives to consolidate resources, exchange data, and share domain knowledge. These cooperatives can enable the generation of AI-supported insights for their member farmers. However, this collaborative approach has raised concerns among individual smart farm owners regarding cybersecurity threats, and privacy. A cybersecurity breach not only endangers the farm attacked but can also risks the entire network of smart farms members within the cooperative. In this research, we emphasize security challenges within cooperative smart farming and introduce a multi-layered architecture incorporating Digital Twins (DT). Further, we introduce a hierarchical federated transfer learning framework designed to address and mitigate the security threats in collaborative smart farming. Our approach leverages Federated Learning (FL) based Anomaly Detection (AD), which operate on edge servers, enabling the execution of AD models locally without exposing the farm’s data. This localization also has excellent generalization ability, which can highly improve the detection of unknown cyber attacks. We employ a hierarchical FL structure that supports aggregation at various levels, fostering multi-party collaboration. Furthermore, we have devised an approach that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, complemented by transfer learning. The objective is to expedite training duration while upholding high accuracy levels. To illustrate the efficiency of our proposed architecture, we present a use case to demonstrate our model’s capabilities. Furthermore, we also present a proof-of-concept implementation of our proposed architecture within Amazon Web Services (AWS) environment, reflecting real-world feasibility.

With the increase in machine learning (ML) applications in different domains, incentives for deceiving these models have reached more than ever. As data is the core backbone of ML algorithms, attackers shifted their interest towards polluting the training data itself. Data credibility is at even higher risk with the rise of state-of-art research topics like open design principles, federated learning, and crowd-sourcing. Since the machine learning model depends on different stakeholders for obtaining data, there are no existing reliable automated mechanisms to verify the veracity of data from each source.Malware detection is arduous due to its malicious nature with the addition of metamorphic and polymorphic ability in the evolving samples. ML has proven to solve the zero-day malware detection problem, which is unresolved by traditional signature- based approaches. The poisoning of malware training data can allow the malware files to go undetected by the ML-based malware detectors, helping the attackers to fulfill their malicious goals. A feasibility analysis of the data poisoning threat in the malware detection domain is still lacking. Our work will focus on two major sections: training ML-based malware detectors and poisoning the training data using the label-poisoning approach. We will analyze the robustness of different machine learning models against data poisoning with varying volumes of poisoning data.

The inclusion of Internet of Things (IoT) devices is growing rapidly in all application domains. Smart Farming uses IoT devices to increase efficiency and optimize farming operations. These devices can be used in a cloud or edge computing infrastructure which can provide remote control of watering and fertilization, real time monitoring of farm conditions, and provide solutions for more sustainable practices. These improvements to efficiency and ease of use come with added risks to security and privacy. Combining vulnerable IoT devices with the critical infrastructure of the agriculture domain broadens the attack surface for adversaries. Cyberattacks in a large coordinated manner could disrupt the economy of agriculture-dependent nations. To the sensors in a system, an attack may appear as anomalous behaviour. Additionally, there are possibilities of anomalies generated due to faulty hardware, issues in network connectivity (if present), or simply abrupt changes to the environment due to weather, human error, or other unforeseen circumstances. To make these systems more secure, it is imperative to detect such data discrepancies and trigger appropriate mitigation mechanisms. In this paper, we propose an anomaly detection model for Smart Farming using an unsupervised Autoencoder machine learning model. We chose to use an Autoencoder as our method of anomaly detection because it attempts to reconstruct normal data with a low reconstruction loss and anomalous data with a high loss. The high reconstruction loss value for a data point indicates that the data is not like the rest. Our model was trained and tested on data collected from our greenhouse test-bed. Our proposed Autoencoder based anomaly detection method achieved 98.98% and took 262 seconds to train and has a detection time of .0585 seconds.

In order to provide the agricultural industry with the infrastructure it needs to take advantage of advanced technology, such as big data, the cloud, and the internet of things (IoT); smart farming is a management concept that focuses on providing the infrastructure necessary to track, monitor, automate, and analyse operations. To represent the knowledge extracted from the primary data collected is of utmost importance. An agricultural ontology framework for smart agriculture systems is presented in this study. The knowledge graph is represented as a lattice to capture and perform reasoning on spatio-temporal agricultural data.

As cyber-attacks against critical infrastructure be-come more frequent, it is increasingly important to be able to rapidly identify and respond to these threats. This work investigates two independent system with overlapping electrical measurements with the goal to more rapidly identify anomalies. The independent systems include HIST, a SCADA historian, and ION, an automatic meter reading system (AMR). While prior research has explored the benefits of fusing measurements, the possibility of overlapping measurements from an existing elec-trical system has not been investigated. To that end, we explore the potential benefits of combining overlapping measurements both to improve the speed/accuracy of anomaly detection and to provide additional validation of the collected measurements. In this paper, we show that merging overlapping measurements provide a more holistic picture of the observed systems. By applying Dynamic Time Warping more anomalies were found – specifically, an average of 349 times more anomalies, when considering anomalies from both overlapping measurements. When merging the overlapping measurements, a percent change of anomalies of up to 785% can be achieved compared to a non-merge of the data as reflected by experimental results.

In recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.