Looking Forward, Will Machine Intelligence Be Able to Outsmart Hackers?

GPTs and Human Development

Technologies are an extension of human capabilities, and the invention of technology is the greatest ability of humans. Even before the emergence of civilization, humans already invented various technologies to give them a competitive edge over other animals in the fight for survival. In human history, the core driver of increased productivity and economic development has been the invention of General Purpose Technologies (GPTs). By influencing existing economic and social structures, GPTs have profoundly influenced human development.

  • Ubiquitous: GPTs have a wide range of uses and are used in a wide range of scenarios.
  • Continuous improvement: GPTs continue to advance with time through increased usefulness and lower costs of use.
  • Drivers of innovation: GPTs facilitate technological innovations and inventions, giving rise to many new products.

The History of Machine Intelligence

Among all GPTs, machine intelligence is the most distinctive. This is the first technology that humans have invented to enable machines to acquire knowledge independently. It also marks the first time that humans have the ability to build a non-carbon based intelligent entity.

From Data-Driven to Intelligence-Driven

Surely, you have heard of “business intelligence and smart business”, “security intelligence and smart security”, and many other such terms. The main differences between the first and second terms are that the former is single-instance intelligence and the latter is global intelligence, and the former is data-driven and the latter is intelligence-driven. Data-driven and intelligence-driven operations look similar but have fundamental differences. The most essential difference is the difference between decision-making entities. Data-driven decision-making ultimately relies on humans to make decisions. Data only provides information that helps you make better decisions. Intelligence-driven decision-making allows machines to replace humans in online decision-making.

Intelligent System Paradigm

Ture intelligent systems must contain the following components: a perception system, cognitive system, decision-making system, and action system. At the same time, an intelligent system cannot be separated from its interaction with its environment. In the past, we always focused too much on the internal operations of systems and ignored their interactions with the environment.

From Single-instance Intelligence to Multi-instance Intelligence

Today, most smart systems are isolated individual intelligent instances that solve single isolated problems. The essence of cloud computing is online computing, while the essence of big data is online data. Machine intelligence eventually needs to achieve online intelligence so that intelligent instances can autonomously interact with each other online.

Four Quadrants of Intelligence and Security

Security is a special type of technology. Strictly speaking, security cannot even be called a technology. Security has been a component in various human activities long before we invented any technology. So far, no technology is exclusive to the security field or has emerged from the security field. Rather, security has always accompanied and complemented other technologies.

Security Difficulties in Machine Intelligence

Go is a simple but complex game, while security is a complex but simple game. In 1994, cognitive scientist Steven Pinker wrote in his book “The Language Instinct” that “for machine intelligence, hard problems are easy and the easy problems are hard.” Simple but complex problems refer to problems with a closed problem space, but that have a high complexity. Complex but simple problems refer to problems with infinitely open problem spaces, but that are not themselves very complex. Today, machine intelligence technology generally exceeds human capabilities in simple but complex problems. However, for complex but simple problems, machine intelligence often fails due to dimensional disasters arising from generalized boundaries.

True Intelligent Security Systems

First, let’s take a look at the general data paradigm in general security scenarios. Plato once wrote that the world we perceive is a projection on the wall of a cave. By this, he meant that the phenomenal world is a reflection of the rational world, which is the true world. The analogy of the cave points to the existence of an external and objective system of knowledge that is not dependent on the cognition or even the existence of humans. Humans gain knowledge by constantly observing the phenomena of the real world and imperfectly trying to understand this objective system of knowledge. Aristotle went further by establishing ontology, the science of existence, as a basic branch of metaphysics. In the 17th century, the philosopher Rudolph Goclenius first used the term “ontology.” By the 1960s, the concept of ontology began to be introduced in the field of machine intelligence, diving into the further evolution of semantic networks and knowledge maps.

  • Entity: An entity is an objectively existing object and can be distinguished from other objects.
  • Property: A property is a tag that describes an entity and depicts the abstract aspects of the entity.
  • Behavior: A behavior is the action of an entity at a specific time and in a specific space.
  • Event: An event is an identifiable situation that occurs at a specific time and space or under specific conditions.
  • Relationship: A relationship expresses the type and degree of association between one entity and another entity.

Machine Intelligence Is Reshaping Security

So far, whenever we find a way to eliminate problems in the security field, the solution produces new problems. Currently, we need to use new technologies to truly solve old problems. The popularity of machine intelligence in various industries has attracted the attention of the security industry. However, the capabilities of intelligent technologies in the security field vary greatly, and it is difficult to separate the true from the false. At present, any security system that uses algorithms is inevitably called an AI-based security system. As in the early years of intelligent driving, today’s intelligent security also requires a unified grading standard to clarify the differences between different levels of intelligent security technologies. Security is essentially a confrontation between intelligent entities. Therefore, we have divided intelligent security technologies into six levels (L0-L5) based on their degree of autonomous confrontation.

  • L0 is manual confrontation. The attacker and defender have no machine intelligence capabilities. The attacker and defender confront each other manually, and their operations, perceptual judgments, and task support are all performed manually.
  • L1 is assisted confrontation. Machines can detect and defend against known attacks. Other operations, such as perception of unknown threats, false negatives, and false positives, are performed manually.
  • L2 is low confrontation automation. Machines detect and defend against attacks, and can detect unknown threats and false positives and negatives. All other operations are performed manually.
  • L3 is moderate confrontation automation. Machines are responsible for all defense operations, including attack detection, attack defense, proactive unknown threat perception, proactive false positive and negative perception, and automatic learning of attack upgrades. Based on system requirements, humans respond when appropriate. For example, intermediate processes require human intervention.
  • L4 is high confrontation automation. Machines perform all defense operations. According to system requirements, humans do not have to provide all responses. Though, intermediate processes do not necessarily involve human intervention. However, the system can only be used in specified security scenarios, such as network domains and host domains.
  • L5 is complete confrontation automation. Machines perform all defense operations. According to system requirements, humans do not have to provide all responses. In addition, the system is not restricted to specific scenarios, but can operate on a global scope.
  • Our LTD attack detection algorithm was selected for inclusion in the IJCAI 2019 AI conference “Locate Then Detect: Web Attack Detection via Attention-Based Deep Neural Networks”.
  • Due in part to its AI kernel, Alibaba Cloud Web Application Firewall was included in the 2019 Gartner Web Application Firewall Magic Quadrant.
  • Due in part to its Anti-Bot AI kernel, Alibaba Cloud Anti-Bot Service was included in the competitor quadrant in 2018 Forrester Bot Management.
  • Due in part to its content security algorithms, Alibaba Cloud has smoothly handled major national activities without any risk or data leakage.
  • We launched a series of security data platform services, including the XDtata security data kernel, XID core data assets, XService smart security service, and String+ security knowledge engine. We also launched complex network and graph computing applications with tens of billions of nodes and hundreds of billions of edges, and complex stream computing applications with a QPS in the tens of millions.

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