AI-enabled threat detection for Blockchain attacks

The article explains the critical role AI plays in enhancing Blockchain security by thoroughly identifying, analysing, and mitigating various potential threats. This ensures the integrity, availability, and confidentiality of a given Blockchain system

Artificial Intelligence enabled threat detection for Blockchain attacks mainly involved in the application of deep learning and machine learning techniques to identify and mitigate vulnerable and malicious activities targeting Blockchain networks. It focuses on utilising AI to detect anomalies, predict potential attacks from the detected anomalies, and enhance the overall security of different Blockchain systems.

In recent decades, the development of single-cell genomics data analysis has led to the emergence of multi-modal assays, enabling simultaneous measurement of multiple molecular characteristics in each individual cell. These assays hold great potential in uncovering the range of cells and offering insights into complex biological processes. However, obstacles that must be overcome in effectively integrating data from different modalities, necessitating the creation of innovative methodological approaches still exist. In this analysis, we thoroughly assess the current status of single-cell multi-modal analysis, with a specific emphasis on technological advancements, methodological gaps, and noteworthy discoveries. We evaluate the capabilities of single-cell multi-modal assays in exploring cellular diversity, while recognising the challenges associated with data integration and the requirement for new definitions and concepts in method development. Digital signatures that utilise the Elliptic Curve Digital Signature Algorithm (ECDSA) serve to authenticate the legitimacy of transactions. Data architectures, such as Merkle trees, facilitate the efficient validation of data integrity, whereas the linked-list configuration inherent in Blockchain technology guarantees resistance to tampering. This amalgamation of algorithms renders Blockchain an exceptionally effective instrument for fostering trust and transparency within distributed networks. Its utility extends beyond the realm of cryptocurrencies, encompassing industries such as finance, healthcare, and supply chain management, thereby providing a decentralised paradigm for the secure management of digital assets.

AI-enhanced threat detection for Blockchain attacks can be viewed in several categories.

1. Detection of anomalies in Blockchain networks

Utilising machine learning for anomaly detection

AI enhanced models, especially models utilising unsupervised learning algorithms such as clustering, isolation forests, and autoencoders, are used to detect abnormal patterns or behaviours in Blockchain transactions or its network activity that is an indication of an attack. By establishing a baseline of normal operations, these models can detect and highlight deviations in real time.

Anomaly-based Intrusion Detection Systems (IDS)

These systems use AI to monitor the traffic of Blockchain networks or transaction logs in order to identify abnormal and irregular behaviours of the network. These behaviours include unusual high transaction volumes or latency, which could signal an ongoing attack, like a DoS, DDoS or eclipse attack.

2. Use of predictive analytics for forecasting of attacks

Time series analysis for predicting Blockchain attacks

Blockchain essentially records each transaction with a timestamp and creates a chronological sequence of events. This data includes block creation times, hash rates, transaction volumes, network delays, miner rewards, block propagation times, or any other metric of Blockchain performance over time. These time series data often presents patterns like seasonality (e.g: unusual daily or weekly spikes in transactions), trends such as increasing network activity, and other irregularities (e.g. sudden spikes or drops due to attacks). Therefore, AI can be utilised to analyse these Blockchain data using time series forecasting techniques. Further, by identifying trends or patterns that precede known types of attacks (e.g., 51% attacks or selfish mining), AI systems can provide early warnings to network administrators.

Reinforcement learning for dynamic threat prediction

Agents can be trained using reinforcement learning to interact with the Blockchain network and simulate various attacks. By learning from these interactions, the AI can predict countermeasures for potential threats that arise in the future. Unlike traditional machine learning models that rely on fixed training data, reinforcement learning agents learn by interacting dynamically with the Blockchain network over time. They can observe the current state of the system, take actions (i.e. by modifying certain parameters or issuing alerts), and receive feedback based on whether their actions helped prevent attacks or not. One of the key advantages of reinforcement learning is its capability to adapt to new threats that were not part of the original training data. As the Blockchain environment changes over time or as novel types of attacks emerge, the reinforcement learning agent continuously updates its policy to improve its threat prediction abilities.

3. AI-enabled detection of selfish mining attacks

Behavioural analysis of miners

Selfish mining attack occurs when a miner withholds mined blocks to gain some advantage. AI-enabled models can recognise miners’ behaviours and detect suspicious mining patterns such as frequent block withholding, or unusual block releases that are not aligned with normal mining scenarios. To this end, supervised learning models can be trained on past data records of known selfish mining activities to improve detection accuracy.

Deep neural networks for mining activity monitoring

This can be used to examine mining activities continuously to identify suspicious patterns that indicate attempts at selfish mining. Further, this may involve detecting anomalies in block propagation as well as delays in broadcasting novel blocks.

4. AI-enabled double-spending attacks detection

Transaction pattern recognition

Mostly graph-based neural networks are utilised in analysing the relationships between transactions in the Blockchain. Here the Blockchain data can be represented in a graph; transactions or addresses are represented by nodes and spending or funding are represented by edges.  Though Double-spending attacks involve complex transaction structures frequently it can be detected through the capabilities of AI by recognising suspicious transaction patterns in the graphs.

Fork analysis in Blockchains

In attacks such as double-spending, malicious users may try to create forks in the Blockchain. AI models and algorithms have the capability to detect forthcoming forks utilising different parameters; such as analysing network conditions, transaction histories, and propagation delays. This would alert nodes to potential double-spending attack risks before they are finalised.

5. AI-enabled Sybil and Eclipse attacks detection

Monitoring node behaviours

Sybil attacks involve creating several fake identities (nodes) to manipulate the decentralised network, while eclipse attacks try isolating nodes from the legitimate network to influence the view of the Blockchains. AI-enabled clustering algorithms can group similar behaviours of Blockchains, supporting the detection of numerous identities controlled by the same adversary.

Analysing Node Communication Patterns using Neural Networks

AI based developments such as recurrent neural networks (RNNs) can perform analysing communication patterns among the nodes in real-time. Here, various anomalies in these patterns could indicate an eclipse attack, where some of the nodes tend to receive manipulated information.

6. AI for smart contract attack detection

As smart contracts are used in Blockchains to keep the trust between members, they have also become vulnerable to various attacks.

Static and dynamic analysis with AI

Smart contracts are susceptible to reentrancy attacks, integer overflow and underflow bugs, etc. AI-enabled models can be used for both static analysis (to examine the contract’s code for vulnerabilities before the deployment) and dynamic analysis (to monitor the contract’s behavior during execution). Further, NLP techniques may be utilised in generating smart contract code to find potentially dangerous constructs and various logical flaws.

Use of adversarial learning to simulate attacks

AI models can be trained to simulate  and model attacks on smart contracts by finding weaknesses in contract logic with the support of adversarial learning techniques. These models can identify vulnerabilities that cannot be detected by traditional security reviews, helping developers to patch potential exploits before they are abused.

7. Detection of Distributed Denial of Service (DDoS) attacks by AI models

Analysis of network traffic

AI models (i.e. CNNs) can be trained to analyse network traffic in Blockchain systems. They can further detect suspicious patterns in this traffic, such as spikes showing DDoS attacks, permitting the network to respond fast and mitigate the attack.

AI-enabled Botnet support in detection

Most of the DDoS attacks are deployed using botnets. AI based classification algorithms can identify bots in the network by analysing their patterns, behaviour, and interaction in the traffic with the Blockchain nodes.

8. AI-enabled routing and partitioning attacks detection

AI-enabled traffic monitoring for routing attacks

Routing attacks are designed to change the routing paths of the Blockchain network in a way that may either isolate or reroute communication. AI models can identify the change in routing patterns and added latency that could signify an attack. These models utilise real-time information from network routers and communication paths to identify partitioning attempts.

AI-enabled resilience against partitioning

We have found that through reinforcement learning, AI models are able to learn the best routing strategies that can be adopted to counter partitioning attacks and keep as many nodes as possible connected in the face of such an attack.

9. AI-based Side-Channel attack detection

Side-channel attacks are another class of attacks based on the assumption that there are other data sources apart from the one being directly targeted such as timing patterns or power usage, to extract information from the Blockchain system. For example, fluctuations in the time that is taken to complete a number of transactions or fluctuations in power consumption may expose other operations, which the attackers can use to understand more about the system. Some of the AI techniques used in analysing these data leaks include the use of Support Vector Machines (SVMs), and random forests in order to identify the possible attacks. SVMs can be used to classify side-channel data in order to detect malicious behaviour from normal operations by building decision boundaries out of timing or power characteristics. Random forests, on the other hand, employ a number of decision trees to analyse various features of the data and to determine specific features that would suggest potential attacks. These models are trained with data which is labelled as normal and attack patterns, and can easily identify small anomalies that could signify an attack. Thus, AI algorithms can notify about side-channel attacks or even prevent them by tracking these signals constantly. This approach supplements the existing methods by detecting indicators that may be overlooked by traditional approaches to Blockchain protocols.

10. AI-enabled malware detection in Blockchain

AI-enabled malware analysis

Blockchain networks are vulnerable to malware attacks by embedding malicious code into transactions or smart contracts. The main approaches used by AI-enabled malware detection models are deep packet inspection and behavioural analysis to identify vulnerable code or activities that are identified as malware infections.

AI-enabled Sandboxing

AI models are capable of simulating the execution of smart contracts or transactions in a sandbox environment. They can analyse their behaviour for signs of malicious activities before they execute on the live Blockchain network.

11. Real-time AI-enabled threat intelligence

Blockchain-specific threat intelligence feeds:

AI can compile the threat intelligence data from different Blockchain nodes and external sources and make a timely alert to new threats. AI can also identify new vulnerabilities or attack patterns that have not been utilised by analysing the natural language processing and the knowledge graph analysis.

Use of adaptive learning models for evolving threats

Self-learning neural networks, for example, can gradually learn new threats as they update their parameters with new data from the activity of Blockchains. This makes it possible for the system to be very resistant to new forms of attack.

 (The author is a Senior Lecturer, Department of Computer Science, Faculty of Science, University of Ruhuna, Sri Lanka)