PROTECTION MODEL AGAINST DISTRIBUTED GRADUAL DEGRADATION ATTACKS BASED ON STATISTICAL AND SEMANTIC APPROACHES

Abstract

B a c k g r o u n d . Nowadays, every critical sector of social institutions conducts its operations on top of distributed processing systems. Contemporary digital infrastructure heavily relies on user-provided data in its operation. As a result, distributed attacks based on botnets are in a continuous state of arms race with the protection methods that filtrate malicious data influx. A common method to do so often relies on heuristics and human-oriented verifications. As the new advancements in the field of artificial intelligence emerge, such attacks adopt new oblique paths towards achieving their goals. The successful execution of the said plan could lead to a gradual resource depletion on the target system. The purpose of this research is to address such threats with a combination of statistical and semantic approaches.

M e t h o d s . The following research conducts a theoretical analysis and systematization of the distributed gradual degradation attack in distributed systems and its implication in the context of the evolving technologies of artificial intelligence. Mathematical modeling is leveraged to define the proposed model's properties and execution process. The proposed model heavily relies on statistical methods for analyzing time series data and its deviations, as well as classification neural networks for semantic detection of suspicious behavior.

R e s u l t s . As a result of the following research, a new model is developed that leverages statistical and semantical verification for anomaly detection. The continuous monitoring and detection process is optimized towards highly loaded systems with a constant flurry of data streams.

C o n c l u s i o n s . Since the distributed attacks could be potentially equipped with intelligent means to bypass existing security measures, the development of a protection model against potential resource leaks is gaining relevance. The recent success in the development of artificial generative intelligence leads to raising concerns about the safety and adequacy of the current security measures against automation-based distributed attack vectors. It is often a case that the protection models are inclined towards prevention of the attack rather than recovery. This approach, while targeting the source of risks, often leads to complacent design decisions without considering the potential outcomes of a successful breach. The proposed model provides a theoretical foundation for building systems that both react to the active execution of threats and perform recovery mechanisms, assuming that the attack may potentially bypass initial security measures.