An IoT-based backup and recovery system

Abstract

An IoT-based backup and recovery system that features: a system initialization to load policies and set baselines; a microcontroller connected to several modules, including: a multi-layered module that captures kernel events, network activity, user behavior, memory analysis, and container inspection; a module for time-bound differential micro-snapshots for signal processing to obtain spectral values; an AI-driven threat classification module for precise attack identification; an event tree-based forensic tagging module for creating maps of causal relationships between system events for a complete attack path; Policy-oriented recovery rules with context-aware policies that adapt depending on the type of attack and business impact; and a surgical rollback engine for component-specific recovery without service interruption.

Description

Background:

[0001] Traditional cyber recovery systems typically require complete system rollbacks in the event of attacks, resulting in significant operational downtime. If, for example, ransomware infects even a small portion of a network, the default approach is often to take entire systems offline for hours or days while IT teams restore data from backups.

[0002] Existing backup and recovery solutions operate very crudely, typically restoring entire virtual machines, physical servers, or entire databases. This "all or nothing" approach leads to significant inefficiencies in targeted attacks. Current recovery systems often require multiple human decisions during both the detection and recovery phases. Security teams must first identify the compromise, determine its scope, decide on recovery actions, and manually execute the recovery process—each step introduces delays that attackers can exploit.

[0003] The problem is solved by the features listed in claim 1.

[0004] However, none of the conventional techniques provide a backup and recovery system.

[0005] The present invention addresses this problem by implementing precise rollback capabilities that focus exclusively on the affected components—be they specific files, containers, processes, or network connections. This drastically reduces recovery time from potentially days to minutes, allowing critical business functions to continue running with minimal disruption while remediation occurs in the background. Traditional snapshot-based recovery approaches restore everything within the snapshot boundary, which poses two significant problems: They can recreate vulnerabilities that existed at the time the snapshot was taken, and they inevitably result in the loss of legitimate work performed after the snapshot was created. Most recovery solutions operate in isolation from the threat detection and prevention ecosystem.After recovery is complete, the same attack vectors often remain usable.

[0006] In current solutions, there is a significant gap between detection technologies and recovery mechanisms. Detections often occur in separate systems and recovery tools, leading to integration complexities and response delays.

[0007] The problem is solved by the features listed in claim 1. Purpose of the invention:

[0008] The invention relates to an IoT-based backup and recovery system. The system solves the above-mentioned problems through unprecedented granularity in recovery. Administrators can thus restore individual files, specific containers, specific process trees, or even individual network connections based on forensic tagging and event trees. This granular approach means that legitimate work running concurrently with an attack need not be lost during recovery. Its autonomous system eliminates these bottlenecks by combining AI-driven detection with policy-based automated recovery decisions. This removes human latency from the response cycle. This autonomous operation is particularly valuable during attacks during off-hours, when security teams may be unavailable or slow to respond.Your invention's differential and component-specific approach ensures that only malicious changes are rolled back, while harmless activities are preserved. This significantly reduces productivity loss during recovery events. Your system incorporates a self-learning feature that analyzes attack patterns after recovery and automatically updates protection policies to prevent similar future attacks. This creates an adaptive defense strategy that evolves with each attack attempt, continuously hardening the system against recurring threats. Your invention closes this gap by directly connecting kernel-level event monitoring (via eBPF, Sysmon, or the Linux Audit Framework) with recovery functions, enabling immediate response to malicious activity.This unified approach enables sub-second response times from detection to containment and recovery.

[0009] The purpose of the invention is achieved by an IoT-based backup and recovery system according to claim 1.The system includes system initialization for loading policies and establishing baselines; a microcontroller connected to several modules, including a multi-layer module with kernel events, network activity, user behavior, memory analysis, and container inspection; a time-bound differential micro-snapshot module for processing the signal to obtain spectral values; an AI-driven threat classification module for precise attack identification; an event tree-based forensic tagging module for creating maps of causal relationships between system events for a complete attack path; policy-aware recovery rules with context-dependent policies that adapt based on attack type and business impact; and a surgical rollback engine for component-specific recovery without service interruption. Detailed description:

[0010] The present invention solves the problem by implementing precise rollback capabilities that focus exclusively on the affected components—be they specific files, containers, processes, or network connections. This drastically reduces recovery time from potentially days to minutes, allowing critical business functions to continue running with minimal disruption while remediation occurs in the background. This enables unprecedented granularity in recovery: Administrators can restore individual files, specific containers, specific process trees, or even individual network connections based on forensic markers and event trees. This granular approach means that legitimate work running concurrently with an attack need not be lost during recovery.

[0011] These bottlenecks are eliminated by combining AI-powered detection with policy-based, automated remediation decisions. This removes human latency from the response cycle. This autonomous operation is particularly valuable during off-hours attacks when security teams may be unavailable or slow to respond.

[0012] The differential and component-specific approach ensures that only malicious changes are rolled back, while benign activities remain intact. This significantly reduces productivity loss during recovery events. The integrated self-learning feature analyzes attack patterns after recovery and automatically updates protection policies to prevent similar future attacks. This creates an adaptive defense strategy that evolves with each attack attempt, continuously hardening the system against recurring threats. Kernel-level event monitoring (via eBPF, Sysmon, or the Linux Audit Framework) is directly linked to recovery functions, enabling immediate response to malicious activity. This unified approach enables sub-second response times from detection to containment and recovery.It enables highly customizable recovery rules that can be tailored to specific threat types, business priorities, and compliance requirements. For example, a critical production database might have different recovery policies than a development environment, or ransomware attacks might trigger different responses than data exfiltration attempts.

[0013] Immutable forensic logging preserves detailed evidence of the attack, including the system state before, during, and after the incident, while enabling effective recovery. This forensic backup helps organizations meet compliance requirements and assists law enforcement agencies in prosecuting attackers. The rollback approach minimizes resource consumption by targeting only the affected components. This makes recovery operations more efficient and has less impact on overall system performance. The container-based recovery capabilities enable integration with orchestration platforms such as Kubernetes to selectively roll back individual containers or pods without impacting the entire application ecosystem. This closes a critical gap in current recovery solutions for cloud-native architectures.

[0014] AI-powered threat detection integrates predictive analytics to identify attack patterns at an early stage—before widespread damage occurs. This capability shifts the recovery paradigm from purely reactive to proactive, potentially preventing attacks entirely by initiating containment and recovery measures based on early behavioral indicators.

[0015] The detailed audit and forensic trail automatically generates documentation that meets legal incident reporting requirements. This reduces administrative burden during cyber emergencies and helps companies avoid compliance penalties.

[0016] The system's ability to perform targeted restores with unprecedented granularity is a key innovation. Unlike traditional solutions that restore entire systems, this feature enables component-specific rollback. It implements granular recovery for individual files, processes, containers, or network connections. Cryptographic hashing and integrity checking precisely determine which components were modified during an attack. System consistency is maintained by detecting dependencies between components and ensuring the coherent recovery of related elements. Transactional recovery mechanisms ensure atomic operations during rollback (successfully completed or fully rolled back).The target is specific memory areas, registry keys, configuration files, or database entries without affecting the surrounding components. System events are assigned unique identifiers, and their relationships are tracked in a directed acyclic graph. Temporal and causal analyses are used to link seemingly unrelated activities in an attack chain. Taint tracking tracks the flow of potentially malicious data through the system. It creates comprehensive impact maps that reveal the full extent of the compromise and stores the complete event tree in immutable storage for forensic analysis and compliance reporting.

[0017] Policy-oriented recovery rule engine: The system's ability to apply customizable, context-sensitive recovery policies represents a significant advance over rigid, one-size-fits-all recovery approaches.

[0018] Implements a declarative policy language specifically designed for recovery scenarios.

[0019] Supports conditional policy execution based on threat type, severity, asset value, and business context.

[0020] Allows the implementation of tiered policies with inheritance and override capabilities.

[0021] Includes policy simulation mode for testing recovery strategies before deployment.

[0022] Provides policy templates for common attack scenarios (ransomware, data exfiltration, credential theft).

[0023] Automatic selection of optimal recovery strategies based on attack characteristics and system state.

[0024] Continuous integrity monitoring at the kernel level: The system's deep integration into the operating system kernel enables real-time detection of malicious changes at the lowest system level.

[0025] Uses eBPF (Extended Berkeley Packet Filter) hooks in Linux environments for low-overhead monitoring.

[0026] Implements ETW (Event Tracing for Windows) integration for Windows environments.

[0027] Creates integrity baselines for critical system components and performs continuous validation against these baselines.

[0028] Monitors critical system call patterns to identify attack attempts.

[0029] Captures process creation chains and execution context to identify malicious activities.

[0030] Uses behavior-based anomaly detection instead of solely signature-based approaches.

[0031] Time-bound differential micro-snapshots: The system's innovative approach to creating compact, component-specific snapshots represents a breakthrough in efficient state preservation.

[0032] Implements copy-on-write mechanisms to track only changed data instead of full copies.

[0033] Creates temporary snapshots at configurable intervals (up to every few seconds for critical components).

[0034] Uses content-based deduplication to minimize storage overhead.

[0035] Maintains snapshot chains with intelligent cleanup based on risk assessment.

[0036] Supports cross-referencing between snapshots to identify compromise patterns.

[0037] Implements cryptographic snapshot integrity verification to prevent tampering.

[0038] AI-powered threat classification and response selection: The system's machine learning capabilities enable automated threat assessment and the selection of optimal recovery strategies without human intervention.

[0039] Uses multiple specialized machine learning models trained on specific types of attacks.

[0040] Features ensemble decision making across different detection algorithms to minimize false alarms.

[0041] Continuous learning from successful and attempted attacks

[0042] Uses reinforcement learning to optimize recovery strategies based on previous results

[0043] Including anomaly detection for zero-day threats without known signatures

[0044] Has explainable AI components that document decision reasons for audit purposes

[0045] Self-healing adaptation framework: This groundbreaking feature enables the system to learn from each attack and automatically strengthen defenses against similar future threats.

[0046] Analyzes attack vectors and automatically generates security hardening recommendations

[0047] Implements automatic policy updates based on observed attack patterns

[0048] Has vulnerability correlation that links attack techniques to specific CVEs and patches

[0049] Creates and implements dynamic protection rules to block similar attack paths

[0050] The system's specialized handling of modern container environments closes a significant gap in current recovery solutions.

[0051] Integration with container orchestration platforms (Kubernetes, Docker Swarm) via custom APIs

[0052] Understands container-image relationships and can selectively reset individual containers

[0053] This innovative feature preserves immutable evidence records while simultaneously performing recovery actions, resolving the traditional conflict between recovery and forensics.

[0054] Implements a cryptographically verifiable chain of evidence for all forensic data. • Creates atomic snapshots of attack evidence before recovery efforts begin. • Provides out-of-band evidence storage to prevent tampering by attackers. • Provides timeline reconstruction capabilities for post-incident analysis. • Supports legally secure evidence preservation with tamper-evident sealing. • Generates automated incident reports that meet regulatory compliance standards.

[0055] Recovery orchestration with near-zero downtime: The system's unique ability to recover compromised components with minimal disruption represents a paradigm shift in cyber recovery. • Parallel processing of recovery operations minimizes recovery time. • Transparent failover during component recovery ensures service availability is maintained. • Virtualization techniques create temporary “clean room” environments for testing before the transition. • Progressive recovery with prioritization of business-critical functions. • Resource throttling prevents recovery operations from impacting system performance. • Adjustable recovery speeds based on criticality and business requirements.

[0056] Endpoint security platforms (e.g., SentinelOne, Check Point Harmony Endpoint): SentinelOne's Singularity platform and Check Point's Harmony Endpoint platform provide ransomware protection through real-time threat detection, file-level rollback, and endpoint isolation. Check Point's anti-ransomware technology uses a purpose-built engine to identify ransomware "fingerprints" and recover encrypted data, prioritizing business continuity.

[0057] Real-time detection: Both detect ransomware in real time, similar to Autoritomi's kernel-level event monitoring.

[0058] File-level rollback: SentinelOne and Check Point can restore encrypted files, similar to Autoritomi's recovery mechanism. Policy-based response: Check Point uses predefined threat response policies, some of which are similar to Autoritomi's policy-driven recovery.

[0059] Granularity: These platforms focus on file-level recovery, not surgical rollback across file systems, containers, and network states like Autoritomi.

[0060] Kernel-level monitoring: While they monitor system activity, they may not utilize deep kernel-level events (e.g., via eBPF) as extensively as Autoritomi.

[0061] Predictive capabilities: In contrast to Autoritomi's proactive approach, there is limited evidence of predictive analytics or post-attack learning.

[0062] Scalability: Less optimized for container or microservice environments compared to Autoritomi.

[0063] Forensic data: Unlike Autoritomi, which prioritizes compliance and analysis, data containment takes precedence over forensic data protection.

Claims

[1] An IoT-based backup and recovery system characterized by including: a system initialization to load policies and set baselines; a microcontroller connected to several modules, including: a multi-layered module that captures kernel events, network activity, user behavior, memory analysis, and container inspection; a module for time-bound differential micro-snapshots for signal processing to obtain spectral values; an AI-driven threat classification module for precise attack identification; an event tree-based forensic tagging module for creating maps of causal relationships between system events for a complete attack path; Policy-oriented recovery rules with context-aware policies that adapt depending on the type of attack and business impact; and a surgical rollback engine for component-specific recovery without service interruption. [2] The system of claim 1, wherein the system includes immutable evidence records and simultaneously performs recovery actions, thereby resolving the traditional conflict between recovery and forensics. [3] The system of claim 1, wherein the system comprises individual files, processes, containers, and network connections. [4] The system of claim 1, wherein the system comprises automated threat assessment and selection of the optimal recovery strategy without human intervention.