Due to the increased sophistication of cyber attacks over the last few decades, there has been an exponential rise in data exfiltration incidents worldwide. Cyber attackers often remain undetected in enterprise networks for a significant amount of time (312 days for a zero-day attack), sufficient to compromise sensitive, business-critical, or mission-critical data such as customer data, scientific documents, trade secrets, proprietary research, etc. Protecting such information is paramount, necessitating techniques to secure it even after theft. Recent research suggests the automatic generation of fake documents to increase burden on the attacker, who needs to correctly identify the correct document from a set of legitimate and counterfeit documents. Numerous works have been proposed in the literature for automatically generating fake documents by manipulating text, equations, images, tables, circuit diagrams, etc. Among all types of equations present in the document, Integral equations are one of the core components of novel innovation and play a crucial role in a diverse set of domains such as risk management, stock market prediction, weather forecast, or even the prediction of natural disasters, etc. In this work, we introduce the Fake Integral Equation Generation Engine (FIEGE) to produce many plausible decoy documents by manipulating the integral equations present in the scientific document. The generated fake document can confuse and mislead the potential attackers if stolen, slow them down, waste their time and resources in identifying the original document, and thereby increase the overall cost of attack. The system employs algorithms for efficient and practical fake integral equation generation, while human evaluation studies demonstrate its effectiveness in deceiving experts. Future research directions are discussed to enhance the performance and resilience of the FIEGE system, making it a promising tool for improving cybersecurity in organizations.

The increase in sophistication of attacker due to advancement in technology has increased the risk of intellectual property (IP) theft. Malicious attacker increasingly aim to steal the sensitive information, being undetectable from the existing security controls. According to Symantec report, the dwell time in a typical organization is 312 days, sufficient enough for an adversary to exfiltrate a large amount of IP documents. To enhance the security recent research suggests the use of data-level cyber deception wherein multiple believable fake versions of IP documents were generated to slow down the adversary who needs to correctly identify the legitimate document hidden among the set of fake documents. Essentially, the scientific and technical document consists of diverse components such as, text, tables, chart, images, circuit diagrams, equations algorithms etc,. Among all, the Boolean logic circuits are the core component of novel innovation or innovative technologies in scientific and engineering domains that provides a competitive advantage over other companies. Theft and abuse of such critical document affect the reputation of organization besides financial loss. Therefore, it is crucial for organizations to protect IP documents. In this paper, we focus on technical, scientific documents or patent that often contain the logical circuits. We present a Fake Boolean Logic Circuit Generation Engine (FBLCGE) to generate fake version of logical design. Multiple believable decoy documents can be generated by replacing the original logic circuit with a believable fake version. The generated fake documents can confuse and mislead the potential attackers if stolen, slow them down, waste their time and resource in identifying the original document, and thereby increases the overall cost of attack. We have used two well-known similarity coefficients to evaluate the similarity of logic circuits by comparing the fake Boolean equations and the original equation. Our experiment suggests that FBLCGE generate believable fake Boolean logic circuit, which can be used to create counterfeit documents for Cyber deception. The applicability of our proposition is validated through the case study.

Internet of Things (IoT) and cloud-based technologies offer a new way of delivering traditional information and communications technology (ICT) services to organizations and governments by combining platforms, operating systems, storage elements, databases, and other ICT equipment. Such Cloud of Things (CoT) are increasingly used in homes and workplaces to improve service delivery and productivity. Early adopters of IoT and cloud technologies include various sectors but not limited to fitness [43], healthcare [34], telecommunication industry [64], retail[23], manufacturing industries [63], real estate [52], transport [7], governments [4], life-sustaining critical infrastructures such as power plant [36], water treatment plants [40], defense (e.g., Internet of Battle things [37]), etc. While CoT infrastructure offers numerous benefits to organizations, governments, and end users, it presents unique security and privacy challenges.

Protection of scientific documents from unauthorized access is crucial as they usually contain mission or business-critical information such as proprietary research data, innovative ideas, novel discoveries, data about industry collaboration and commercial interests, etc. Existing security controls are insufficient for protecting such sensitive documents from sophisticated cyberattacks such as Advanced Persistent Threats (APTs). Recent security solutions focus on data-level Cyber deception wherein multiple believable fake versions of intellectual property (IP) documents were generated and deployed throughout the enterprise network to slow down the adversary who needs to correctly identify the legitimate document hidden among the set of fake documents. As an integral component of scientific documents, images or figures convey critical information and complement textual content. Therefore, scientific images must also be faked while generating believable fake documents. These images may be different types but are not limited to diagrams, schematics, graphs, charts, simulation outputs, plots, flowcharts, and medical illustrations. These images need to be accurately classified before creating their believable fakes. However, the diversity and complexity of scientific images or charts complicate their accurate classification. This paper has tested several image classification models, such as SVM, Decision Tree, Random Forest, CNN, VGG16, InceptionV3, ResNet-50, and ResNet-101, to classify scientific images extracted from technical scientific documents. We have chosen DocFigure - a benchmark dataset of scientific annotated images for the training and testing of selected models. Our experiment illustrates that ResNet-101 is suitable for classifying scientific images or charts.