Introduction: The nature of warfare is rapidly changing. Drone swarms that integrate artificial intelligence (AI) and autonomous flight technology are no longer merely means to enhance military power, but have become key threats reshaping warfare patterns. This technology enables hundreds or even thousands of drones to simultaneously detect, track, and strike targets, with operational effectiveness validated on Ukrainian battlefields. Major military powers worldwide are investing massively in this technology, making it a core component of next-generation military capabilities. Particularly noteworthy is that drone swarms possess overwhelming cost-effectiveness. Small drones costing less than one million won can destroy tanks, aircraft, and command centers worth billions to trillions of won, overturning existing concepts of military superiority centered on high-cost weapon systems. This threat transcends nation-states. The rapid proliferation of commercial technology enables even financially constrained countries, non-state actors, and terrorist organizations to operate small drone swarms. This exposes national infrastructure, major cities, and civilian areas to comprehensive threats. The problem is that existing traditional air defense systems are extremely ineffective against these multiple and simultaneous threats. Intercepting hundreds of low-cost drones with air defense missiles costing hundreds of millions of won per launch is tactically and economically unsustainable. Therefore, to adapt to new battlefields that existing methods cannot address, we must rethink our entire technological, tactical, and operational concepts. Against this backdrop, South Korea is developing next-generation counter-unmanned aircraft system (C-UAS) strategies based on its own technology. Particularly, the "Wolfpack Attack" and "WALL" technologies developed by Professor Han Seung-yeol's research team at Ulsan National Institute of Science and Technology (UNIST) transcend existing response methods focused on simple interception. "Wolfpack Attack" is an aggressive strategy targeting collaborative intelligence itself - the core advantage of drone swarms - disrupting enemy AI clusters' collective decision-making and collaborative structures from within. "WALL" is a defensive framework designed to train friendly AI clusters to maintain autonomy and cooperation even in unpredictable environments, preparing for such intelligent attacks. In other words, the strategic concept ensures tactical advantage by targeting the cluster's "brain" rather than destroying individual drones' "bodies." Keywords: Drone swarm threats, global drone technology trends, South Korean innovative drone clusters, "Wolfpack Attack," "WALL"

I. Introduction: The Drone Swarm Era: New Turning Point of Threats and Responses

Future drone swarm responses will likely evolve beyond simple hardware competition into intelligent confrontations between artificial intelligence systems - battles between adversarial AI (malicious attacks inducing AI models to make incorrect decisions) and AI robustness (AI models' ability to maintain consistent stable performance under malicious attacks like adversarial AI or unexpected external interference). On this battlefield, victory will no longer depend solely on existing weapon system performance; algorithmic robustness, adaptability, and real-time learning and response capabilities will become key indicators of national security. Therefore, this research comprehensively analyzes technological and strategic threats posed by drone swarms and deeply explores future development directions of AI-based response technologies like "Wolfpack Attack" and "WALL." Additionally, this research proposes a roadmap for building national counter-unmanned aircraft system (C-UAS) ecosystems, covering technology development, legal and institutional reforms, military-civilian cooperation, and verification and deployment, aiming to establish strategic foundations for actively responding to the intelligent battlefield era.

II. Nature and Vulnerabilities of Drone Swarm Threats

2.1 Drone Swarm Operation Case Analysis

The Russia-Ukraine war demonstrates drone swarm "quantity + economy" tactics. Ukrainian forces simultaneously deployed hundreds of FPV drones and suicide drones, each costing less than $1,000, to destroy Russian aircraft, tanks, armored vehicles, and air defense systems. By deploying some drones for interception guidance while allowing remaining drones to autonomously attack targets, Ukrainian forces destroyed Russian air defense systems by forcing expensive interceptor missile launches. Notably, in June 2025, Ukraine deployed long-range drone swarms, destroying strategic bombers at Russian rear ground air bases, demonstrating capabilities to destroy strategic assets thousands of kilometers away.

The conflict between Israel and Iran is gradually escalating from proxy wars to direct attacks. In June 2025, Israel deployed dozens of drone swarms equipped with missiles and aircraft for precision strikes against Iranian nuclear facilities and air defense installations, even assassinating key figures. In response, Iran launched large numbers of "Shahed" suicide drones at low altitude, attacking Israeli military and civilian infrastructure. The Israel Defense Forces (IDF) defended against these attacks using systems like the "Iron Beam" laser interception system.

Drone swarms utilize multi-sensor fusion technology to identify and track targets, coordinating with electronic warfare drones to destroy air defense systems before launching autonomous strikes. Both cases demonstrate that drone swarms are key capabilities in modern warfare, capable of striking strategic targets based on low cost, AI autonomy, and survivability. The Ukraine-Russia conflict showcased "quantity warfare and long-range penetration" operational patterns, while the Israel-Iran conflict demonstrated "composite missions and precision strikes" operational patterns, both having important implications for future military strategy and air defense system development.

2.2 Drone Swarm Advantages: Strategic Combination of Scale, Autonomy, and Survivability

Drone swarms represent more than technological advancement; they represent an entirely new operational paradigm threatening traditional concepts of power. Their advantages can be summarized in three key elements: overwhelming numerical superiority, AI-driven autonomy, and extremely high survivability. These three elements complement each other, making drone swarms the ultimate asymmetric force on battlefields.

First, scale effects. Individual drone prices reaching millions of won create enormous economic asymmetry compared to existing combat assets costing hundreds of billions to trillions of won. This enables large-scale drone swarm deployment based on attrition warfare, where even high-precision air defense systems may fail due to physical saturation and cumulative fatigue. Additionally, providing strategic strike capabilities even to countries or terrorists with limited resources and technology may create new fissures in international security structures. This is accelerating fundamental transformations in defense procurement systems: shifting from focus on expensive custom platforms to systems centered on low-cost, mass production, and rapid deployment.

Second, autonomy enhances drone swarms' tactical flexibility and real-time response capabilities. Using AI-based autonomous flight technology and reinforcement learning algorithms, thousands of drones can automatically identify targets, set paths, and execute strikes with minimal human intervention. Particularly noteworthy is that mesh networks allow direct communication and decision-sharing between individual drones without centralized control, enhancing electronic warfare evasion capabilities and operational continuity. Even if individual command centers or nodes are attacked, drone swarms can maintain missions based on collective intelligence.

Third, regarding survivability, drone swarms have significant advantages over existing force systems. Drone swarms composed of multiple distributed units can smoothly execute missions without major disruption even if some drones are lost. In fact, some latest drones are equipped with terminal evasion maneuvering algorithms, enabling them to evade missile interception and execute tactical missions until destruction. This effectively breaks existing "one-shot kill" air defense patterns.

Maximizing these advantages requires strategic approaches. In other words, building resilient cluster systems requires not only robust AI algorithms but also redundancy and cyber-physical resilience. This challenge cannot be solved merely by advancing drone platforms; it requires integrated design capabilities across communication architectures, security protocols, and electronic warfare systems. The cluster drone era is no longer "possible" but "reality." Future battlefield outcomes will no longer depend on a few expensive weapon systems but on the agility of thousands of intelligent small drones and the distributed intelligent systems supporting them.

2.3 Drone Swarm Vulnerabilities: Fissures from Electromagnetic Attacks and Communication Interference

While drone swarms have opened new battlefields with their "collaborative autonomy" capabilities, their inherent structural advantages may also bring serious vulnerabilities. Particularly, electromagnetic-based attacks and communication interference technologies directly affect core systems that drone swarms depend on - communication, GPS, and AI sensor networks.

Drone swarms rely on continuous communication networks and GPS signals to exchange data, coordinate operations, and strike targets. Therefore, powerful RF jammers or electromagnetic pulse (EMP) attacks can disrupt connections between drones, weakening swarm autonomy and coordination capabilities. Disrupting drone position perception through GPS jamming or spoofing makes precise, synchronized strikes or coordinated maneuvers nearly impossible. Simultaneously jamming multiple drones' GPS may cause entire swarms to derail, collide, and fail missions.

Additionally, high-power microwave (HPM) weapons cause irreversible physical damage to sensitive components inside drones, including electronic circuits, sensors, and control units. This method can permanently "immobilize" drones, with advantages of lower cost and less collateral damage than high-speed ammunition or missiles. This approach is particularly suitable for environments requiring precision, cost-effectiveness, and minimal side effects, such as urban defense or critical infrastructure protection. This indicates future counter-drone strategies will shift from kinetic methods to electromagnetic-based "non-kinetic neutralization."

Furthermore, drone swarm AI makes judgments and decisions based on sensor data. If electromagnetic attacks cause sensor failures, AI may misjudge or behave out of control. Collaborative AI's "collective intelligence" relies on data reliability, and sensor signal interruptions or erroneous position information may disrupt AI cooperation. This may lead to cluster fragmentation, conflicts, and mission interruptions, while recovery and reorganization require substantial time and computational resources. Especially in real-time combat situations, recovery delays directly lead to tactical failures.

These vulnerabilities exist in both hardware (electronic components) and software (AI algorithms, network dependencies). Effective counter-drone defense requires not just "physical destruction" but comprehensive, integrated responses at electronic, cognitive, and network levels. Strategies disrupting AI, destroying data reliability, and breaking collaborative algorithms are more cost-effective and impactful than physical interception. Strategies disrupting drone swarm core collaborative algorithms are more cost-effective and impactful than physical interception. On future battlefields, electronic warfare is increasingly likely to attack the most vulnerable parts of AI collaborative systems. Therefore, integrated defense systems combining network, physical, and cognitive domains will become the most critical condition for drone swarm survival.

III. Global Drone Response Technology Trends

3.1 Rise of Directed Energy Weapons (DEW): New Paradigm for Large-scale Drone Swarm Threats

As large-scale, synchronized attacks by drone swarms become real threats in modern warfare, traditional kinetic-based air defense systems face severe challenges in cost-effectiveness and sustainability. Directed Energy Weapons (DEW), particularly High-Power Microwave (HPM) technology, are becoming new paradigms for addressing these challenges. The U.S. "Leonidas" system and Lockheed Martin's "MORFIUS" system are prime examples.

"Leonidas" is a land-based high-power microwave weapon system developed by Epirus. It can automatically detect incoming drones and then launch high-power microwaves to disable their internal circuits, sensors, and communication equipment. Similar to electromagnetic pulses (EMP), it can disable drones, making it ideal for countering drone swarms capable of rapidly responding to multiple simultaneous threats. Unlike existing interception systems using expensive ammunition like missiles, "Leonidas" relies on electrical power, reducing operational costs, improving repeatability, and enabling destruction of multiple drones in short periods. This makes it particularly effective in environments requiring non-destructive, precise responses, such as urban defense or critical infrastructure protection.

Meanwhile, Lockheed Martin's currently developing "MORFIUS" system is an airborne high-power microwave system serving as a mobile platform for intercepting drone swarms. MORFIUS is compact and operable on multiple platforms (ground, air, and sea), capable of directly engaging threats and intercepting from greater distances. This enables early response from the air, eliminating threats before they reach friendly territory. This airborne interception platform has significant advantages, especially in providing immediate responses to rapidly approaching drone swarms.

Directed Energy Weapon (DEW) systems operate on non-kinetic principles. This approach uses "soft kill" methods, disabling sensors and electronic equipment without physically destroying targets, minimizing collateral damage and making them suitable for use in civilian areas. Additionally, directed energy weapons require no ammunition, thus having low operational costs and being reusable as long as energy is sufficient. These characteristics enable economically efficient and sustained responses to large-scale threats like drone swarms. The capability to simultaneously destroy multiple targets is also a major advantage of directed energy weapons. However, power generation and thermal management systems are crucial for producing and controlling powerful energy. This technical challenge is currently one of the biggest obstacles in directed energy weapon system development.

3.2 Laser Blinding Technology: Non-kinetic Solution for Destroying Drone Swarms

As drone swarm threats become increasingly complex, traditional kinetic attack methods based on missiles and cannons are no longer economically and tactically viable solutions for defending against large-scale drone attacks. Therefore, laser-based blinding technology is receiving global attention. This non-kinetic (soft kill) weapon can use laser beams to directly interfere with drones' optical and electronic sensors, disrupting their visual perception capabilities without causing physical damage to drones.

Laser blinding technology uses high-power lasers of specific wavelengths to subject drone cameras, optical sensors, and infrared (IR) sensors to intense light exposure, preventing them from interpreting images. This causes drones to lose key functions like target tracking, autonomous flight path correction, and collaborative network maintenance. Specifically, applying area illumination techniques can form laser protective shields covering entire areas rather than targeting individual drones with narrow beams, enabling simultaneous destruction of multiple rapidly moving drone swarms.

This technology's greatest advantage lies in low cost and high efficiency. Each launch consumes no ammunition and can operate continuously using only power sources. Additionally, because it causes no destruction, it can minimize collateral damage in sensitive environments like urban defense, airports, and nuclear power plants. Extended continuous exposure permanently damages drone sensors, while brief exposure causes temporary sensor paralysis, delaying threat arrival. This may lead to mission failures or gain more time for interception.

From tactical perspectives, laser blinding technology can integrate into multi-layered defense systems, combining radar, RF analyzers, and electro-optical/infrared sensors. For example, if detection and identification systems identify drone swarm entry paths, surface laser beams can immediately cover those areas, blinding entire drone swarms. Especially when combined with AI-based attack-defense frameworks like Wolfpack Attack, comprehensive effects of disrupting drone perception, judgment, and coordination systems can be simultaneously achieved.

However, efficiency may decrease due to weather conditions (rain, fog, dust), illumination distance, and beam dispersion. Equipment miniaturization and high-power output, along with ensuring thermal management and power infrastructure safety, are crucial challenges. Therefore, future development is expected to focus on improving high-power, wide-area illumination capabilities, selecting wavelengths that minimize weather impacts, and applying them to mobile platforms.

Laser blinding technology provides entirely new response solutions of "neutralization and paralysis," transcending existing counter-drone paradigms focused on destruction. This is likely to become key technology making defense strategies more flexible and sustainable in the drone swarm era.

3.3 Rise of Integrated Counter-Drone Defense Systems: Strategic Shift Toward AI and "System-of-Systems" Approaches

The emergence of drone swarms demands thorough reconsideration of existing defense strategies on modern battlefields. To address threats characterized by numerical superiority, autonomous flight, and advanced collaborative capabilities, single technologies or individual platforms are no longer sufficient. In fact, the counter-drone market is rapidly evolving both technologically and conceptually, with integrated systems becoming core, real-time connecting and coordinating different levels of detection, tracking, and suppression technologies.

Today's counter-drone systems primarily utilize various sensor technologies for precise detection and tracking. Radar is advantageous for long-distance target acquisition, while RF analyzers can detect drone communication signals, analyzing their trajectories and intentions. Electro-optical/infrared (EO/IR) sensors provide visual and thermal imaging-based information even in low visibility or nighttime operations, while integrated sensor networks achieve multi-layered detection even in complex battlefield environments.

The core of these sensing technologies is artificial intelligence (AI). AI analyzes collected data to distinguish between drones and birds, predict drone behavior, and prioritize threats based on severity before passing them to command and control systems. Entering the suppression phase, broader countermeasures are deployed. RF jammers can disrupt drone communication and navigation systems, forcing them to land or return, while high-energy lasers can precisely destroy drones' core components. High-Power Microwave (HPM) weapons can simultaneously suppress multiple drones and are receiving increasing attention as means to minimize collateral damage, especially in sensitive areas.

Traditional kinetic weapons, such as anti-aircraft guns and missiles, remain effective defenses against single or low-density drone threats. This trend reflects the growing tendency to integrate counter-drone capabilities into existing platforms. As defense measures diversify, merely deploying individual elements in parallel is far from sufficient. To integrate and optimize each step from detection to identification to response, synchronization and coordination between sensor networks, AI-based command and control systems (C2), electronic warfare equipment, and kinetic and non-kinetic weapons are crucial.

This cannot be achieved through single devices or isolated systems but must adopt "system-of-systems" approaches integrating various defense technologies into unified ecosystems. Such systems require conceptual shifts transcending pure battlefield technology. Threats posed by drone swarms are rapidly evolving, and defense strategies responding to these threats are evolving into AI-based ecosystems capable of machine-speed judgment, real-time data fusion, and autonomous decision-making.

For example, private defense companies like DroneShield are combining proprietary signal processing technology with adaptive AI to maximize detection accuracy and resilience, likely becoming core standards for future counter-drone technology. Most importantly, these integrated technology systems cannot function merely through field deployment. To ensure actual response capabilities in battlefield environments, joint training, multi-agency cooperation, and development of simulation-based assessment systems modeling complex swarm attack scenarios are required.

Integrated thinking covering AI, sensors, communications, and weapon systems is no longer an option but a prerequisite. Ultimately, AI today serves as not only the "brain" of attacking drones but also the "brain" of defense systems. To counter increasingly intelligent drones, defense systems must react more intelligently and rapidly, inevitably leading to battlefield intelligence competition between AI systems. This clearly indicates that national investment in defense AI technologies like computer vision, predictive analytics, and autonomous decision-making, along with ecosystem building, are urgent tasks.

IV. South Korean Innovative Drone Swarm Response Technologies: "Wolfpack Attack" and "WALL"

4.1 Wolfpack Attack: Strategic Adversarial Attack Disrupting Collaborative AI

"Wolfpack Attack," developed by Professor Han Seung-yeol's research team, is a novel adversarial AI (AD) attack strategy designed to disrupt collaborative structures of AI-based drone swarms. While existing counter-drone technologies mainly rely on physical interception or communication interference, "Wolfpack Attack" takes a different approach: disrupting AI clusters' collaborative mechanisms.

This strategy draws inspiration from how wolf packs in natural ecosystems isolate and subdue prey through chain reactions. Specifically, it first deliberately induces failures or abnormal behavior in individual agents (drones). Subsequently, collaborative AI agents attempt to identify and rescue these failed entities, triggering chain destruction of the entire collaborative system's collective decision-making and collaborative structures. This overcomes limitations of existing interference methods focused on blocking communication between individual nodes.

"Wolfpack Attack" targets structural weaknesses of "mutual support behavior" between AI agents, causing functional collapse of entire systems. In other words, this method doesn't simply block communication but disrupts networks' core interdependencies. This is an evolved attack method recognizing that AI groups are not simple collections of individuals but highly interconnected, adaptive ecosystems.

Technically, this attack utilizes predictive models. AI simulates various scenarios to calculate future losses and selects the most effective initial attack timing. It then analyzes behavioral change data, selecting agents highly sensitive to cooperation and targeting them for subsequent attacks. This elevates existing adversarial AI technology to new levels by enabling automated strategic and sustained disruption rather than just random disruption.

This strategy has evolved from small-scale disruptive attacks disrupting simple system balance to adaptive disruption systems capable of predicting opponent AI system weaknesses and optimizing attacks accordingly. Therefore, battlefields where AI groups are dismantled by AI are emerging, indicating future AI warfare will increasingly evolve into autonomous AI confrontations.

The emergence of this autonomous adaptive adversarial AI raises fundamental questions transcending technical levels: controllability, unpredictability, and ethical responsibility. Successful attacks will require enhanced AI group resilience and defense technology complexity, likely leading to new arms races between AI robustness and adversarial AI.

This research has been accepted by the 2025 International Conference on Machine Learning (ICML) and has attracted widespread attention from academic and defense technology communities for its sophisticated implementation of many-to-many adversarial strategies targeting individual agents while disrupting entire collaborative structures.

4.2 "WALL": Defense Framework Building Collaborative AI Resilience

The "WALL" framework is a defensive AI learning structure designed to directly counter "Wolfpack Attack's" offensive strategies. This framework's core involves training AI systems in chaotic hostile environments similar to real battlefields, thereby cultivating more robust collaborative mechanisms.

"WALL" is not a simple defense tool; it incorporates complex interference strategies similar to "Wolfpack Attack" from the beginning of training. Therefore, AI groups can cultivate resilience for reliable cooperation even in crisis situations. This resembles how soldiers train in the most challenging combat environments, embodying the "train hard, fight easy" future AI design philosophy.

Experimental results show that AI trained using the WALL framework maintains high collaborative performance even under abnormal variables like position errors, communication delays, and temporary member departures, achieving objectives without collisions, jointly moving objects, and maintaining alignment. Notably, this robustness includes not just simple avoidance or survival but the ability to execute missions.

From technical perspectives, WALL aims to promote system-wide collaboration while reducing excessive dependence on specific agent subsets. By restructuring collaborative structures into more flexible, decentralized forms, WALL eliminates single points of failure, enabling entire clusters to rapidly recover and reorganize even during partial disruptions.

For example, even if certain agents exit or transmit erroneous information, entire systems remain unaffected, instead dynamically recalibrating and continuing mission execution. These characteristics transcend simple "defense," aiming to enhance resilience enabling AI clusters to autonomously survive, make decisions, and recover. This promises to become core capabilities for drone swarm tactics where autonomy and stability are crucial in future military operations.

Additionally, WALL symbolizes evolution of complex AI strategies, blurring attack-defense boundaries. While "Wolfpack Attack" targets AI clusters' "cooperation," "WALL" ensures "cooperation" no longer depends on specific individuals or fixed patterns. This will enable AI systems to adapt to increasingly complex and unpredictable attack scenarios, ultimately giving AI groups self-repair capabilities.

These advances are not limited to military domains. The WALL framework can serve as core safety enhancements for AI systems operating in high-risk environments, such as smart factories, autonomous driving, disaster rescue, and space exploration. WALL demonstrates future AI design directions where "recoverable performance" becomes more important than "optimized performance," especially in unpredictable and potentially fatal consequence situations.

V. Future Battlefield Paradigm Shifts and South Korea's Response Strategies

The "Wolfpack Attack" and "WALL" frameworks developed by the research team are recognized as practical military tools capable of ensuring strategic advantages in AI-based battlefield environments, transcending mere AI technological advancement. These technologies have received global attention for their dual-use potential combining offensive and defensive capabilities and their direct response to essential characteristics of intelligent swarm drone warfare.

5.1 Offensive Applications: Maximizing Strategic Effectiveness Through Disrupting Collective Intelligence

"Wolfpack Attack" is a strategic interference technology disrupting enemy operations from within by directly attacking enemy AI clusters' greatest advantage - collaborative intelligence. Unlike physical strikes or communication interference, this technology operates through cognitively interfering with AI clusters' decision-making processes.

Specifically, it uses AI predictive models to identify sensitive agents in enemy clusters and precisely attacks key nodes inducing cooperation or exhibiting behavioral changes. This can distort or disrupt entire clusters' decision-making flows, ultimately causing operational paralysis.

Considering actual battlefield applications, this technology can integrate into electronic warfare or specialized cyber warfare units' tactical assets. For example, reconnaissance drones can collect enemy group activity patterns and correlate them with AI analysis platforms, then "Wolfpack Attack" algorithms will automatically determine attack timing and methods. For countries with disadvantaged defense budgets or force density, this strategy may be a practical method for ensuring asymmetric power using AI technology.

Additionally, future technology may merge with cyber attack tools, developing into digital-physical fusion attacks.

5.2 Defensive Applications: Ensuring Sustained Combat Capability Through Enhanced Resilience and Distributed Cooperation

The "WALL" framework is a defense-focused AI reinforcement training system designed to train AI to resist and adapt to adversarial AI attacks like "Wolfpack Attack." It is not merely technical assurance but a prerequisite for deploying AI clusters on battlefields.

Experimental results show that AI trained through "WALL" can systematically compensate for individual agent damage and demonstrate dynamic reorganization and flexible role allocation capabilities, maintaining overall mission execution even when facing threats like sensor failures, communication delays, data errors, and deliberate hacking attacks.

In practical applications, the WALL framework can serve as standard training models for defense department autonomous weapon system development and as foundational AI adversarial training protocols applicable not only to drone swarms but also autonomous surface vessels, unmanned ground vehicles, and all types of manned-unmanned composite combat systems.

Additionally, WALL applications are not limited to military domains but include disaster response robot clusters, autonomous logistics systems, and intelligent disaster prevention infrastructure. It can also expand as general defense algorithms applicable to all civilian systems requiring maintained collaboration during communication interruptions and dangerous situations.

5.3 Practical Strategic Implementation: Trinity Strategy of Technology, Force, and Training

For South Korea to strategically deploy "Wolfpack Attack" and "WALL" operations, three pillars must be simultaneously advanced: technology R&D, operational concept development, and training system construction.

Beyond simply delegating technology development to research institutions or private enterprises, technology must integrate into actual military structures and mission plans, incorporating data linkage systems and situational awareness tools enabling field commanders to operate.

Additionally, designing joint training plans around this point is crucial. Practical training is needed where friendly AI systems experience simulated wolfpack attack scenarios to respond to real-world drone swarm attacks, while lunar AI systems repeatedly learn to continue executing missions under various interference conditions.

This training should not be limited to simple technical demonstrations but understood as processes for verifying reliability and internalizing combat capabilities before combat deployment.

VI. Strategic Impacts on Industry and Security: Multi-layered Value as Dual-Use Technology

"Wolfpack Attack" and "WALL" are clearly dual-use technologies transcending military domains. They serve not only as strategic tools for destroying or recovering drone swarm intelligence and coordination capabilities, but their underlying technological elements - collective intelligence control, autonomous system resilience, and maintaining communication in hostile environments - also have broad potential applications in civilian industry and society as a whole.

For example, in smart factories where multiple collaborative robots maintain production lines, autonomous recovery algorithms can be applied to prevent entire process paralysis even if individual robots or networks fail. Autonomous logistics systems operating in large logistics hubs or ports can also respond to sudden network failures or external attacks by reconfiguring routes and continuing mission execution, directly contributing to supply chain stability.

Additionally, at large-scale disaster sites like wildfires, earthquakes, and floods, numerous autonomous robots and drones share information in real-time while executing rescue missions. Resilience frameworks like "WALL" can maintain collaborative networks even during partial network interruptions, maximizing rescue efficiency.

These technologies not only expand application scopes but are core foundational technologies ensuring robustness, resilience, and collaborative stability of national-level AI systems. In other words, they strengthen AI ecosystems' "immune systems," becoming foundational assets supporting national security and critical infrastructure security in finance, energy, transportation, and communications.

From security perspectives, AI-based cluster capabilities have become core components of future weapon systems. Multi-platform cluster capabilities controlled by AI are becoming central axes for integrated operations across sea, land, air, and space domains. Countries possessing leading technology in this field can simultaneously enhance defense autonomy and strategic deterrence.

Through "Wolfpack Attack" and "WALL" programs, South Korea has secured offensive and defensive cluster AI technology, establishing foundations for gaining competitive advantages in global defense markets beyond merely possessing technology.

Additionally, these technological capabilities are powerful negotiating assets in international military and technological diplomacy. They can ensure South Korea's leadership and agenda-setting capabilities in advanced weapon system development alliances, joint defense industry projects, and multinational military exercises, generating long-term strategic benefits transcending short-term weapon exports.

If South Korea plays "standard-setter" roles in joint development and operational cooperation with allies, it can ensure leadership in technical specifications and operational theories.

Ultimately, "Wolfpack Attack" and "WALL" are strategic assets with multifaceted value covering industry, security, and diplomacy, transcending pure defense and offensive technologies. The fundamental advantages of AI ecosystems constructed by these technologies will become key foundations for South Korea's sustainable national competitiveness, not only in the intelligent battlefield era but in AI-driven global order.

VII. Conclusion: South Korea's Strategic Leap and Comprehensive Ecosystem Establishment

The intelligent battlefield era has arrived. Drone swarms are no longer merely technological advances but have become key threats changing modern warfare landscapes. The capability of thousands of drones operating autonomously and striking targets structurally transcends existing high-cost, high-precision defense systems, demonstrating true asymmetric power.

To address this challenge, intelligent, strategic, AI-based comprehensive response systems are crucial rather than simple interception or detection. South Korea's "Wolfpack Attack" and "WALL" technologies are innovative solutions meeting these needs.

"Wolfpack Attack," inspired by wolf pack hunting strategies, is an active adversarial AI strategy capable of disrupting enemy group AI collaborative systems from within. "WALL" is a defense framework that can train friendly AI to maintain autonomy and cooperation even in unpredictable threat environments, preparing for such intelligent attacks.

Both technologies are designed around clusters' core - "collaborative intelligence" - for attack and defense, clearly indicating that future military advantages will no longer depend on simple hardware performance but on AI algorithms' robustness and adaptability.

Current challenges involve ensuring these achievements are not limited to laboratories but expand to national-level counter-unmanned aircraft (C-UAS) ecosystems. For this, governments should establish multi-year projects lasting three to five years, investing in short-term integrated detection and neutralization systems and next-generation AI-to-AI response technologies.

Regular realistic large-scale demonstration competitions should be held to verify performance, establishing mechanisms to rapidly link excellent results with procurement. Additionally, legal and institutional improvements should be simultaneously advanced, such as revising Radio Law, directed energy weapon (DEW) operation standards, data standardization, and AI training data sharing systems.

Regarding testing and evaluation infrastructure, national-level test sites capable of simulating long-range, low-altitude, and maritime environments should be established, along with standing red teams modeling adversarial AI scenarios like "wolfpack" attacks.

Sensors, jammers, lasers, and high-power microwave weapons should achieve interoperability through modular, scalable (MOSA) designs, with standardized common operational pictures and warning systems between military, police, fire departments, nuclear power plants, and airports.

Private sectors should integrate "Wolfpack Attack" and "WALL" technologies as modules into counter-unmanned aircraft system (C-UAS) command and control systems, enhancing sensor performance to distinguish low-cost small drones from birds, and advancing anti-jamming Global Navigation Satellite System (GNSS) and signal analysis technologies.

Comprehensive solutions should be proposed covering directed energy weapon (DEW) power and thermal management, beam control technology development, platform diversification, enhanced interoperability through open data formats, and providing customized defense suites and maintenance services.

By establishing national counter-unmanned aircraft system alliances with government and private sector participation, organically combining technology development, standardization, and procurement, research results can immediately transform into operational capabilities.

Particular attention should be paid to achieving concrete results in the short term, such as revising Radio Law, conducting national demonstrations, opening test sites, publicizing data standards, standardizing "wolfpack" attack scenarios, conducting joint tactical tests, and conducting digital twin-based military exercises.

If this strategy is implemented, South Korea will become a strategic leader rather than merely a responder in the AI-versus-intelligence battlefield era. This will be a decisive leap completing the transformation from traditional security to intelligent security.