Draft:Nature-Inspired CyberSecurity

Nature-inspired cybersecurity (NICS) is the strategic use of computer techniques that are based on the defensive behaviours of different species and natural events. NICS has gained traction as a formidable approach to crafting adaptive and efficient security measures against evolving cyber threats. Its methodologies encompass multi-objective optimisation, deceptive information deployment, and data integration to fortify security implementations. When coupled with artificial intelligence (AI), machine learning (ML), and computational data science, NICS offers a potent defence against sophisticated cyberattacks. These techniques bolster adaptability, self-organisation, resilience, and overall robustness within security solutions.

Nature-Inspired Cybersecurity, or NICS for brevity, constitutes a distinct domain within the computer security landscape. This specialised field revolves around formulating countermeasures and computational strategies rooted in the mechanisms observed in the natural world. NICS draws insights from phenomena such as the collective behaviour of animal swarms and the intricate dynamics of chaotic systems that exhibit sensitivity to initial conditions.

The ambit of threats addressed by NICS spans a wide spectrum, encompassing unauthorised data dissemination, data repudiation, privilege escalation, theft or compromise of hardware and software, as well as service disruption and redirection. The foundational principle of NICS involves extrapolating models and concepts inherent in natural systems.

The study of nature plays a pivotal role in informing the principles of NICS. This entails comprehensive exploration, analysis, and understanding of the physical realm, spanning living organisms and their intrinsic attributes like growth patterns, energy requirements, and evolutionary mechanisms. Hierarchical, adaptive, and synchronised structures observed in nature are extrapolated to simulate analogous attributes within cybersecurity frameworks.

A gamut of biological characteristics can be effectively leveraged to model diverse aspects of cybersecurity, particularly those pertaining to network traffic regulation, governance, and security. These natural models exhibit substantial potential for ameliorating a broad array of cyber challenges. However, the efficacious utilisation of these models mandates a thorough comprehension of them. Depending on the scenario, full integration of biological attributes might be required to fully harness this approach. In other instances, metaphorical parallels with biology might be inadequate, necessitating alternative strategies.

The development of nature-inspired algorithms within the cybersecurity domain necessitates establishing meaningful analogies between specific threats and biological systems. Crafting these analogies and tailoring borrowed algorithms to suit computational requirements entails meticulous deliberation. Striking a harmonious equilibrium between automated parameterization and preserving solution functionality is pivotal. NICS is becoming a well-known part of natural computation. It combines ideas from connectionism, social behaviour in living organisms, emergence, behaviour, and metaheuristic approaches to solve the complex problems in cybersecurity. By assimilating design methodologies from bio-inspired computation, NICS adapts these approaches to cater to the dynamic and evolving cybersecurity milieu.

In tandem with the rapid evolution of cyberspace, computing, communications, and sensing technologies, both entities and individuals increasingly depend on emerging applications like fog and cloud computing, smart cities, the Internet of Things (IoT), collaborative computing, and virtual and mixed reality environments. Ensuring the security, trustworthiness, and resilience of these applications against cyber onslaughts is paramount. This impels the innovation of creative solutions within the realm of cybersecurity and resilience. In response, computing algorithms have been engineered to emulate the functioning of natural processes, phenomena, and organisms. Exemplary algorithms encompass artificial neural networks, swarm intelligence, chaotic behaviour algorithms, deep learning systems, and biomimicry, among others. These computational systems proffer distinctive characteristics that usher in novel methodologies and opportunities to effectively combat the emergent challenges within the cybersecurity sphere.

Types of NICS

Based on the inspiration source

1. Internal Mechanism Inspiration: This approach involves an in-depth study of the internal mechanisms of organisms. It seeks to identify parallels between these mechanisms and the architecture of network security systems. By analysing the distinctive attributes and defence mechanisms of organisms, valuable insights are derived. These insights serve as guidelines for designing resilient network security frameworks. For instance, the immune system's capability to detect and eliminate foreign entities shares similarities with the network security system's task of identifying and neutralising threats. Such similarities guide the advancement of sophisticated security measures.
2. External Mechanism Inspiration: Within NICS, the External Mechanism Inspiration approach revolves around drawing lessons from the external attributes and appearance-based security methods observed in organisms and natural systems. Through the examination of interactions within ecosystems, such as predator-prey relationships, cybersecurity experts enhance their grasp of predictability, probability assessment, and pattern recognition in security solutions. This approach entails incorporating insights from natural systems' interactions into cybersecurity strategies. The analysis of how these systems interact contributes to the development of strategies that bolster security measures, making them more adaptable and effective.
3. Inter-organism Interactions: An essential facet of NICS is the exploration of inter-organism interactions. This approach shifts the focus from the mechanisms of a single organism to the broader context of entire natural systems. It capitalises on the intricate dynamics between different elements within these systems. For instance, the associations between predators and prey offer valuable insights into the nuances of security threats and corresponding countermeasures. By deciphering the evasion and defence strategies employed by organisms, cybersecurity experts pioneer innovative approaches to risk mitigation and system protection. A significant advantage of investigating inter-organism interactions lies in the potential for transdisciplinary knowledge transfer. Concepts gleaned from ecological systems, social networks, and symbiotic relationships can be transposed into effective cybersecurity strategies. Consider the concept of symbiosis, where two organisms mutually benefit; this idea can inspire the creation of collaborative security measures. By leveraging cooperation and shared responsibilities, NICS elevates the resilience and robustness of security solutions.

Based on implementation

Nature-Inspired Cybersecurity (NICS) implementations are categorised based on their modes of deployment, encompassing both hardware and software approaches. These approaches leverage principles observed in natural systems to fortify security measures.

1. Hardware Implementation: Incorporating principles derived from natural systems into hardware components constitutes the essence of hardware-based NICS implementation. An illustrative instance of this concept is the Spiking Astrocyte Neural Network (SANN), an adaptation of the conventional Spiking Neural Network (SNN). SANN capitalises on the pattern recognition capabilities exhibited by astrocytes (brain cells) to identify and counter hardware Trojan attacks within electronic circuits. By embedding astrocyte-inspired mechanisms into hardware design, SANN elevates the predictability and probability of detecting and mitigating such attacks. This integration empowers electronic circuits with enhanced defensive attributes, effectively utilising natural principles to reinforce security. The hardware implementation of NICS seamlessly integrates security measures into the hardware's blueprint, harnessing nature's principles to augment its safeguarding capabilities.
2. Software Implementation: The software facet of NICS implementation involves the application of nature-inspired principles to software components to enhance their security. This approach harnesses natural models to augment pattern recognition and probabilistic analysis within software systems. The adoption of evolutionary algorithms, which are based on Darwin's evolutionary principles, is a notable example. These algorithms leverage the "survival of the fittest" concept to identify and eliminate vulnerabilities inherent in software systems. Through emulation of the evolutionary process, these algorithms heighten the predictability and probability of identifying and mitigating attacks, resulting in elevated software security.

NICS architectures

• Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security (bioHAIFCS): The Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security (bioHAIFCS) emerges as a cutting-edge solution geared towards safeguarding critical network applications, particularly in military contexts. Its core objective involves preempting and identifying intrusions while effectively isolating compromised components within the network. This framework amalgamates outcomes from multiple nature-inspired models to synergistically enhance its defensive capabilities. The ensuing sections delve into the three pivotal models constituting bioHAIFCS.
• Hybrid Evolving Spiking Anomaly Detection Model (HESADM): The Hybrid Evolving Spiking Anomaly Detection Model (HESADM) is different from passive security measures like firewalls because it is the first line of defence in the bioHAIFCS framework. HESADM proactively identifies and thwarts attacks with both timeliness and precision. This is done by combining techniques that are based on biological neural networks and spiking models. This makes it easier to find things that aren't normal.
• Evolving Computational Intelligence System for Malware Detection (ECISMD): Functioning as a dual-purpose entity within bioHAIFCS, the Evolving Computational Intelligence System for Malware Detection (ECISMD) undertakes detection and isolation roles. Traditional antivirus software often falters in identifying malware hidden within packed executables. ECISMD overcomes this challenge by harnessing computational intelligence and evolving algorithms to effectively detect and isolate evasive malware strains. The dynamic adaptation to evolving threats equips ECISMD with superior performance in upholding network integrity.
• Evolutionary Prevention System from SQL Injection (ePSSQLI): The Evolutionary Prevention System from SQL Injection (ePSSQLI) constitutes the second facet of bioHAIFCS's prevention component. Specialised measures are essential given the serious threat that SQL injection attacks pose. ePSSQLI employs evolutionary algorithms to predict and preemptively prevent SQL injection attacks. This anticipatory approach enhances the framework's resilience by proactively countering this specific category of cyber threats.
• Nature-Inspired Network Security Architecture: Drawing inspiration from the immune system, the nature-inspired network security architecture adopts a dynamic defence model. This model promptly responds to attacks, gathering crucial insights for assertive countermeasures. Similar to the immune system's immune memory, this architecture exhibits adaptive behaviour by cataloguing attack details. This adaptation enhances defense against similar future intrusions, effectively thwarting threats.
• Bio-Inspired Multidimensional Network Security Model (B-MNS): The Bio-Inspired Multidimensional Network Security Model (B-MNS) offers a novel paradigm as a result of the remarkable security abilities shown by biological systems. B-MNS leverages a multi-network parallel structure, distributing distinct subnets across transient states. The model's mathematical foundation incorporates parameter estimation techniques and modified algorithms based on the Hidden Markov Model (HMM). Rigorous simulations substantiate the model's effectiveness and efficiency, establishing it as a robust network security solution.
• AI-assisted Computer Network Operations testbed for Nature-Inspired Cyber Security based adaptive defense simulation and analysis: The established standard testbed introduces a comprehensive framework tailored for simulating and benchmarking cyber defense strategies. This modular and extensible testbed empowers users to customize network parameters, including network load, cluster quantities, home network types, and node quantities within clusters and networks. Within this testbed, an extensive array of nature-inspired and AI-based techniques are integrated, enabling the seamless design, implementation, and evaluation of proactive cyber defense mechanisms. The efficacy of this testbed is rigorously evaluated across three distinct network scenarios: normal conditions, attack-induced circumstances, and situations featuring adaptive defense mechanisms. The results affirm the testbed's adeptness in faithfully replicating network conditions with minimal configuration demands. Furthermore, simulation outcomes showcase substantial performance enhancements when applying the proposed defense mechanisms. The authors extend the testbed's applicability to contemporary technological domains, including Fog Computing and Edge Computing, positing it as a versatile tool for crafting and assessing pioneering cyber defense strategies. This study underscores the testbed's value in facilitating the design, implementation, and evaluation of proactive cyber defense mechanisms. Its modular and extensible nature renders it adaptable to a wide range of network scenarios. The incorporation of a diverse library of nature-inspired and AI-based techniques empowers the exploration and formulation of innovative defense strategies. The testbed's demonstrated capacity to precisely simulate network conditions and effectively evaluate defense mechanisms solidifies its practical utility. In essence, this testbed emerges as a pertinent resource for cutting-edge technologies like Fog Computing and Edge Computing.

Applications

• Malware Detection: Within the realm of malware detection, the Spiking Astrocyte Neural Network (SANN) has emerged as a promising adaptation of the Spiking Neural Network (SNN). This advanced network architecture holds potential for effectively identifying hardware Trojans within electronic circuits. Addressing the challenge of detecting Android malware, which exhibits an elusive nature, necessitates innovative solutions. Leveraging nature-inspired methodologies can offer viable approaches to tackling this intricate problem. Drawing inspiration from natural systems, these methods present innovative techniques for the effective detection of Android malware.
• Anomaly Detection and Mitigation: The complexity of networks and the vast amount of data passing through them present a formidable challenge for anomaly detection and mitigation. Nature-inspired approaches prove to be ideal candidates for analyzing data packets traversing networks, enabling the identification of peculiar activities and facilitating activity recognition. Additionally, these methodologies can be used to create subsystems independent of central control that automatically detect and mitigate faults and incorrect functionalities. Furthermore, they demonstrate adaptability in dynamic threat landscapes, setting up regular and automatic routine scans to detect anomalies and differentiate between attacked nodes and nodes experiencing failures. Nature-Inspired Classification Systems (NICS) significantly enhance the efficiency of Intrusion Detection Systems (IDS)^[18] by reducing false positives resulting from massive network traffic
• Intrusion Prevention: Nature-inspired algorithms exhibit the capability to predict future attack patterns and threats, enabling the development of proactive prevention mechanisms. By leveraging these algorithms, security systems can anticipate potential threat and take preemptive measures to safeguard against them, bolstering overall intrusion prevention capabilities.
• Secure Data Transmission: Nature-inspired steganography provides a novel application for secure data transmission by allowing the covert transfer of confidential information undetected. Additionally, encryption algorithms inspired by natural phenomena can be utilized to ensure the secure transmission of data. By harnessing the principles derived from nature, these encryption algorithms provide robust protection for data confidentiality and integrity.
• Cloud Security:With the increasing migration of data to cloud-based storage, ensuring the security of cloud infrastructures becomes a paramount concern. Nature-Inspired Classification Systems (NICS) prove invaluable in addressing the intricate and multifaceted nature of cloud security challenges. Leveraging the inherent strengths of nature-inspired approaches, these systems offer effective solutions to secure cloud environments against evolving threats.

• Dataset for deep packet inspection and analysis: A comprehensive collection of real-time network data during various cyber attack scenarios, providing valuable insights into the effectiveness of defense mechanism. It captures network traffic from virtual machines using Wireshark and tcpdump, focusing on ten interconnected computers attempting to exploit each other using different attack methods. The dataset includes a range of attack types such as Distributed Denial of Service, SQL Injection, Account Takeover, Service Exploitation, DNS and ARP Spoofing, Scanning, Firewall Searching, Brute-Force Attacks, Malware, Spoofing, and Man-in-the-Middle Attacks. Researchers can analyze this dataset to assess the impact of these attacks and evaluate the efficacy of defense measures, emphasizing the significance of system updates and patching to enhance security.