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Ethereum project status update 2018


ethereum project status update 2018

On June 18, , hackers maliciously attacked The DAO project, wherein each node can obtain the updated status of blockchain from some active nodes. Regarding blockchain programs, Ethereum is the platform most the DASP 10 website with the first iteration of the project is dated A decentralized computing platform that runs smart contracts: applications that run exactly as programmed without downtime, censorship or third party. PARTIDO AMERICA VS TIGRES

In their conclusion, the authors state that many tools are inefficient and require specific knowledge for defining security properties. They also notice a trade-off between accuracy and the coverage of multiple vulnerabilities. Vacca et al. The survey is based on 96 articles, published between and , on the analysis and the testing of smart contracts, on metrics for and security of smart contracts, on Dapp performance and on blockchain applications.

The work summarizes the properties and application areas of 26 tools for the automated analysis of smart contracts. Moreover, the review describes experimental datasets and 18 empirical validations. The authors emphasize the need for guidelines and further research regarding the development and testing of smart contracts. Automated analysis is covered in varying degrees, but is not at the center.

Vulnerabilities are described in one review, a taxonomy is suggested by two. Most SLRs include a description of the methods found, but usually without indicating the vulnerabilities that can be tackled by the methods. Tool descriptions are more often included than not, while comparisons of tool properties are less frequent.

The conclusions of the SLRs portray an immature field, in particular with respect to standards and guidelines, program behavior, tool efficiency, and testing. This situation and the marked increase in publications warrants regular reviews of the state of the art. Naturally, our review includes more recent research, up to January , as it was conducted later than the other SLRs. What sets our work apart is its specific scope, its breadth, and rigor.

Our main focus is automated vulnerability detection, including tools, taxonomies and benchmarks. Section 2. We start with our consolidated taxonomy of the vulnerabilities identified in the body of literature. Then we summarize classifications by scholars and present two community taxonomies. Finally, we present a mapping of our consolidated taxonomy to the community classifications.

In the reviewed literature, the term vulnerability is used in a broader sense than is common in computer security. It refers to a weakness or limitation of a smart contract that may result in security problems. A vulnerability allows for the execution of a smart contract in unintended ways.

This includes locked or stolen resources, breaches of confidentiality or data integrity, and state changes in the environment of smart contracts that were not anticipated by developers or users and that put some involved party at an advantage or disadvantage. The Supplementary Material contains a short description for each, including references. Our consolidated classification in Table 6 consists of 10 classes of vulnerabilities.

It is based on 17 systematically selected surveys as documented in the supplement and two popular community classifications presented below. Consolidated taxonomy of vulnerabilities of smart contracts on Ethereum. Luu et al. They define the vulnerabilities and present code snippets, examples of attacks, and affected real live smart contracts. To fix some problems, they propose improvements to the operational semantics of Ethereum, namely guarded transactions countering TOD , deterministic timestamps and enhanced exception handling.

Atzei et al. At the top, vulnerabilities are classified according to where they appear: in the source code usually Solidity , at machine level in the bytecode or related to instruction semantics , or at blockchain level. A mapping to actual examples of attacks and vulnerable smart contracts completes the taxonomy. Although this work is referenced in several other papers, we have found some issues and inconsistencies regarding the classification of concrete vulnerabilities.

For example, the vulnerability type called unpredictable state is illustrated by an example that is viewed in most other work as an instance of transaction order dependency. At the same time another example for problems associated with dynamic libraries is assigned to the same class. It can be argued that these two examples exhibit different vulnerabilities, as the underlying causes are inherently different.

Dika extends the taxonomy of Atzei et al. Grishchenko et al. If a bytecode satisfies such a property, it is provably free of the corresponding vulnerabilities. As the properties usually are too complex to be established automatically, the authors consider simpler criteria that imply the properties. The project neither defines the listed vulnerabilities nor explains how the vulnerabilities were selected and ranked.

Several studies like Durieux et al. Currently, the registry holds 36 vulnerabilities, with descriptions, references, suggestions for remediation and sample Solidity contracts. While several taxonomies build on the early classification of Atzei et al. EVM vs. Solidity or cause vs. So far, none of the taxonomies has seen wide adoption. Table 7 maps our ten classes, omitting vulnerabilities that have no counterpart in the other taxonomies.

We find a correspondence for 34 vulnerabilities, while 20 vulnerabilities documented in literature remain uncovered. Mapping of classifications for vulnerabilities. The mapping is not exact in the sense that categories in the same line of the table may overlap only partially. SWC covers a range of 36 vulnerabilities, but 22 of our categories are missing. Both community classifications seem inactive: SWC was last updated in March , and the DASP 10 website with the first iteration of the project is dated For other summaries, differing in breadth and depth, see the surveys Almakhour et al.

We discuss four groups of methods: static code analysis, dynamic code analysis, formal specification and verification, and miscellany. The distinction between static analysis and formal methods is to some extent arbitrary, as the latter are mostly used in a static context.

Moreover, methods like symbolic execution regularly use formal methods as a black box. A key difference is the aspiration of formal methods to be rigorous, requiring correctness and striving for completeness. In this sense abstract interpretation should be rather considered a formal method, but it resembles symbolic execution and therefore is presented there.

The analysis starts either from the source or the machine code of the contract. In most cases, the aim is to identify code patterns that indicate vulnerabilities. Some tools also compute input data to trigger the suspected vulnerability and check whether the attack has been effective, thereby eliminating false positives. To put the various methods into perspective, we take a closer look at the process of compiling a program from a high-level language like Solidity to machine code Aho et al.

The sequence of characters first becomes a stream of lexical tokens comprising e. The parser transforms the linear stream of tokens into an abstract syntax tree AST and performs semantic checks. Now several rounds of code analysis, code optimization, and code instrumentation may take place, with the output in each round again in IR.

This last step linearizes any hierarchical structures left, by arranging code fragments into a sequence and by converting control flow dependencies to jump instructions. Such representations are readily available when starting from source code, as AST and IR are by-products of compilation.

This approach is fast, but lacks accuracy if a vulnerability cannot be adequately characterized by such patterns. Recovering a control flow graph CFG from machine code is inherently more complex. Its nodes correspond to the basic blocks of a program. A basic block is a sequence of instructions executed linearly one after the other, ending with the first instruction that potentially alters the flow of control, must notably conditional and unconditional jumps. Nodes are connected by a directed edge if the corresponding basic blocks may be executed one after the other.

The reachability of code is difficult to determine, as indirect jumps retrieve the target address from a register or the stack, where it has been stored by an earlier computation. Backward slicing resolves many situations by tracking down the origins of the jump targets. If this fails, the analysis has the choice between over- and under-approximation, by either treating all blocks as potential successors or by ignoring the undetectable successors.

Some tools go on by transforming the CFG and a specification of the vulnerability to a restricted form of Horn Logic called DataLog, which is not computationally universal, but admits efficient reasoning algorithms see e. Soufle, Starting from the CFG, decompilation attempts to reverse also the other phases of the compilation process, with the aim to obtain source from machine code. The result is intended for manual inspection by humans, as it usually is not fully functional and does not compile.

Any operation on such symbols results in a symbolic expression that is passed to the next operation. In the case of a fork, all branches are explored, but they are annotated with complementary symbolic conditions that restrict the symbols to those values that will lead to the execution of the particular branch. At intervals, an SMT Satisfiability Modulo Theory solver is invoked to check whether the constraints on the current path are still simultaneously satisfiable.

If they are contradictory, the path does not correspond to an actual execution trace and can be skipped. Otherwise, exploration continues. When symbolic execution reaches code that matches a vulnerability pattern, a potential vulnerability is reported. If, in addition, the SMT solver succeeds in computing a satisfying assignment for the constraints on the path, it can be used to devise an exploit that verifies the existence of the vulnerability. The effectiveness of symbolic execution is limited by several factors.

First, the number of paths grows exponentially with depth, so the analysis has to stop at a certain point. Second, some aspects of the machine are difficult to model precisely, like the relationship between storage and memory cells, or complex operations like hash functions. Third, SMT solvers are limited to certain types of constraints, and even for these, the evaluation may time out instead of detecting un satisfiability. Symbolic execution of the same path then yields formal constraints characterizing the path.

After negating some constraint, the SMT solver searches for a satisfying assignment. Using it as the input for the next cycle leads, by construction, to the exploration of a new path. This way, concolic execution achieves a better coverage of the code.

Propagation rules define how tags are transformed by the instructions. Some vulnerabilities can be identified by inspecting the tags arriving at specific code locations. Taint analysis is often used in combination with other methods, like symbolic execution. They may report vulnerabilities where there are none false positives, unsoundness , and may fail to detect vulnerabilities present in the code false negatives, incompleteness.

The first limitation arises from the inability to specify necessary conditions for the presence of vulnerabilities that can be effectively checked. The second one is a consequence of the infeasibly large number of computation paths to explore, and the difficulty to come up with sufficient conditions that can be checked. Abstract interpretation Cousot and Cousot, aims at completeness by focusing on properties that can be evaluated for all execution traces.

As an example, abstract interpretation may split the integer range into the three groups zero, positive, and negative values. Instead of using symbolic expressions to capture the precise result of instructions, abstract interpretation reasons about how the property of belonging to one of the three groups propagates with each instruction. This way it may be possible to show that the divisors in the code always belong to the positive group, ruling out division by zero, for any input.

The challenge is to come up with a property that is strong enough to entail the absence of a particular vulnerability, but weak enough to allow for the exploration of the search space. Contrary to symbolic execution and most other methods, this approach does not indicate the presence of a vulnerability, but proves that a contract is definitely free from a certain vulnerability safety guarantee.

The most common method is testing, where the code is run with selected inputs and its output is compared to the expected result. Fuzzing is a technique that runs a program with a large number of randomized inputs, in order to provoke crashes or otherwise unexpected behavior.

Code instrumentation augments the program with additional instructions that check for abnormal behavior or monitor performance during runtime. An attempt to exploit a vulnerability then may trigger an exception and terminate execution. As an example, a program could be systematically extended by assertions ensuring that arithmetic operations do not cause an overflow. Machine instrumentation is similar to code instrumentation, but adds the additional checks on machine level, enforcing them for all contracts.

Some authors go even further by proposing changes to the transaction semantics or the Ethereum protocol, in order to prevent vulnerabilities. While interesting from a conceptual point of view, such proposals are difficult to realize, as they require a hard fork affecting also the contracts already deployed. Mutation testing is a technique that evaluates the quality of test suites. The source code of a program is subjected to small syntactic changes, known as mutations, which mimic common errors in software development.

For example, a mutation might change a mathematical operator or negate a logical condition. If a test suite is able to detect such artificial mistakes, it is more likely that it also finds real programming errors. Modeling smart contracts on an even higher level of abstraction offers additional benefits, like formal proofs of contract properties. The core logic of many blockchain applications can be modeled as finite state machines FSMs , with constraints guarding the transitions.

As FSMs are simple formal objects, techniques like model checking can be used to verify properties specified in variants of computation tree logic. Once the model is finished, tools translate the FSM to conventional source code, where additional functionality can be added. The high cost of errors and the small size of blockchain programs makes them a promising target for formal verification approaches.

Unlike testing, which detects the presence of bugs, formal verification aims at proving the absence of bugs and vulnerabilities. As a necessary prerequisite, the execution environment and the semantics of the programming language or the machine need to be formalized. Then functional and security properties can be added, expressed in some specification language. Finally, automated theorem provers or semi-automatic proof assistants can be used to show that the given program satisfies the properties.

Bhargavan et al. From the specification, the K framework is able to generate tools like interpreters and model-checkers, but also deductive program verifiers. Horn logic is a restricted form of first-order logic, but still computationally universal. It forms the basis of logic-oriented programming and is attractive as a specification language, as Horn formulas can be read as if-then rules. Techniques like long-short term memory LSTM modeling, convolution neural networks or N-gram language models may achieve high test accuracy.

A common challenge is to obtain a labeled training set that is large enough and of sufficient quality. Formal reasoning and constraint solving is most frequently employed, due to the many tools integrating formal methods as a black box, like constraint solvers to prune the search space or Datalog reasoners to check intermediate representations.

Proper formal verification, automated or via proof assistants, is rare, even though smart contracts, due to their limited size and the value at stake, seem to be a promising application domain. This may be due to the specific knowledge required for this approach.

Number of analysis tools employing a particular method. Next in popularity are the construction of control flow graphs 46, In the Supplementary Material , we describe the tools and list their functionalities and methods. Number of analysis tools providing a particular functionality. Code level. More than half of the tools analyze Solidity code 86, More than half of the tools 79, Some tools go the extra length of verifying the vulnerabilities they found by providing exploits or suggesting remedies.

Almost a third 41, Analysis Type. The vast majority of tools , The development of new tools has increased rapidly since , with more than half of them published open source. If a variable store is less than bits, Ethereum virtual machine may store multiple variables in one slot.

Or the mapping type occupies a slot in which the mapping or array length is stored. The specific locations of arrays and mapping elements are stored in slots according to a set of special hash rules. Transaction Process of Ethereum The trading process of Ethereum is as follows. There are three types of transactions.

For contract deployment, enter a bytecode to create a new contract account and associate it with the entered bytecode. For contract call, where the recipient is the called smart contract, the input uniquely identifies the callee function through the hash digest algorithm, and the bytecode associated with the called smart contract account is loaded into the Ethereum virtual machine for execution. The hash value of metadata of this new block needs to be smaller than a certain value, which reflects the difficulty of creating the block.

Security Attacks on Ethereum Attacks on Ethereum can be divided into five layers: application layer, contract layer, consensus layer, network layer, and data layer attacks. In this paper, we focus on the attacks in the application layer, the contract layer, and the network layer. The other two layers of attacks are our future research directions.

Attacks of Application Layer The application layer is the carrier of blockchain technology and provides solutions for various business scenarios. Security vulnerabilities in various trading platforms and user accounts seriously threaten the asset security of blockchain wallet users. Therefore, we analyze three common types of attacks. Replay Attack The replay attack is to replay transaction information.

The user signs a message, uploads it to the contract, and then verifies the signature inside the contract. It can be widely understood as the process of using the same payment information to purchase goods multiple times. When the Ethereum and Ethereum Classic chains emerged after the hard fork, it was found that transactions on the Ethereum chain were still valid when they were replayed on the Ethereum Classic chain.

As Figure 2 shows, while parameters remain unchanged, multiple transfers can be made through the replay attack. Figure 2 Replay attack. False Top-Up Attack The status field in the Ethereum token transaction receipt is true or false depending on whether an exception is thrown during the execution of the transaction. When the user calls the transfer function of the token contract to transfer, if the transfer function runs normally and no exception is thrown, the status of the transaction is true.

As Figure 3 shows, when , it enters the else logical section and returns false, and finally no exception is thrown. In this attack, although the exchange did not receive the real tokens, the transaction execution did not throw an exception, and the user did get the real recharge record.

In this case, users can steal real assets. The false top-up attack has become a type of attack that cannot be ignored in blockchain system. Figure 3 False top-up attack. Transaction Order Dependence Attack Transaction order dependence attack is a kind of attack that widely exists in the blockchain system; an example of transaction order dependence attack is shown in Figure 4. In blockchain, transactions initiated by nodes need to be packaged by miners before they can be finally recorded on the blockchain.

Miners select a series of transactions from the trading pool and then package them into a new block. Therefore, the sequence of a series of transactions packaged in the block is not the same as the sequence of transaction generation but is also related to the gas cost consumed by the transaction. Therefore, the contract code cannot know the order of transactions. And, the transaction is visible to each node in the transaction pool, so its execution order can be observed.

Figure 4 Transaction order dependence attack. The attacker observes the transactions that may contain the target contract in the pool. If they exist, the status of the contract that is not conducive to the attacker or the authority of the contract will be modified by the attacker.

Attackers can also steal transaction data, create their own transactions at a higher gas price, and then package their own transactions in the block before the original transaction, thus obtaining transaction processing priority. In Ethereum geth client, txpool consists of two parts, namely, pending queue and queued queue.

Attacks of Contract Layer As an indispensable part of blockchain technology, smart contract not only expands the application of blockchain technology but also increases the attack surface faced by the blockchain system. The smart contract is written in a high-level language like solidity, and then the contract will be compiled into bytecode, which will be deployed to the blockchain by the contract owner and run on various virtual machines similar to Ethereum virtual machines.

In the process, the smart contract will face various security threats [ 31 ]. Integer Overflow Attack Integer overflow is a typical loophole in the blockchain system, which once caused serious economic losses in the development of blockchain. In the Ethereum platform, Solidity language is the most mainstream language for writing intelligent contracts.

Because of the insecurity of its design, integer overflow is a serious problem. Generally speaking, integer overflow can be divided into integer overflow and integer underflow. According to arithmetic classification, there are three overflow problems: multiplication overflow, addition overflow, and subtraction overflow.

In April , nearly RMB 6 billion was stolen by hackers due to integer overflow loopholes in the contract code of the American Chain BEC project, which reduced the market value of tokens to almost zero. In the same month, hackers used the integer overflow vulnerability of SMT project side to create a huge amount of SMT currency for selling, and the Firecoin Exchange suspended the recharge and withdrawal of all other currencies for this purpose.

In Solidity, the variable supports unsigned integers, and the value after uint represents the number of bits occupied by its unsigned integers in storage, and supports 8-bit unsigned integers to bit unsigned integers. An unsigned integer of type uint8 stored in the range of 0 to , that is, [0, ], and an unsigned integer of type uint stored in the range of 0 to. Because the range of stored integers from uint8 to uint is limited, and the range of represented integers is also limited, there is an overflow problem.

The integer overflow attack is shown in Figure 5. When , it results in an underflow. Figure 5 Integer Overflow attack. Re-Entrancy Attack Re-entrancy attack is a typical attack in Ethereum, which directly led to the hard bifurcation of Ethereum. The main reason for the attack is the sequencing and atomicity of updating smart contract variables and transferring operations, the re-entrancy attack is shown in Figure 6.

When the logic in the smart contract code adopts the sequence of transferring operation first and then modifying the variable value, the attacker can construct a smart contract with the malicious callback function. If the object of the transfer operation is a malicious contract, it can lead to recursively calling the contract, destroying the original business logic of the contract, and bypassing its inspection to obtain additional transfer income.

Figure 6 Re-entrancy attack. By default, the Ethereum smart contract has an unnamed callback function, which has no parameters or return values. If no function can be found in the calling contract to match the hash of the provided function, the callback function will be called. When the contract receives a transfer without data, it will also call the callback function. In addition, in order to receive Ether, the callback function must be marked as payable.

If it is not marked as payable, the contract can only receive Ether by calling other functions marked with payable. Imagine such a scenario, if a special callback function is constructed, in which the transfer function of the other party is called, then a recursive transfer will be generated, and the contract with loopholes will continuously transfer money to the special contract until the gas is exhausted. It should be noted that this attack is only aimed at the transfer method of address.

Honeypot Attack Honeypot contracts are the most interesting findings. These contracts hold ether, and pretend to do so insecurely. In short, they are scam contracts that try to fool us into thinking we can steal the ether they hold, while in fact all we can do is lose ether. As Figure 7 shows, CryptoRoulette is a type of honeypot attack.

The submitted number is stored in the second location. A common pattern they follow is, in order to win the ether they hold, we must send them some ether of our own first. However, if we try that, we are in for a nasty surprise: the smart contract eats up our ether, and we find out that the smart contract does not do what we thought it would. Figure 7 Honeypot attack. Short Url Attack Short url attack is a typical attack in Ethereum, which usually occurs in exchanges.

In Ethereum virtual machine, the data end of the input will be automatically filled with 0. Malicious attackers can use an address account with the end of 0, and the exchange fails to verify the address length input by the user, which causes the transferred related variables to shift and enlarge, thus expanding the actual transfer amount by several times, and malicious attackers can obtain a large amount of benefits.

There are two main reasons for this vulnerability; one is that the exchange has not verified the incoming address length of the user, and the other is that the Ethereum virtual machine has an automatic completion mechanism for the data whose length does not conform to the specification when calling the smart contract, resulting in the shift amplification of parameters.

We can use sendRawTransaction to achieve this attack and the code is shown in Figure 8. Figure 8 Short URL attack. Airdrop Hunting Attack The airdrop hunting attack uses multiple new accounts to call the airdrop function in order to obtain airdrop coins, and attackers transfer them to their account to achieve wealth accumulation. This attack is relatively common that as long as it is a contract with an airdrop function, it can make multiple profits. The first automated attack was the Simoleon contract.

As Figure 9 shows, the contract was designed to give some amount of ether to initialized an account, so the attacker thinks that we can create a few more accounts to get rewards, then transfer all the money to one account. The attacker write attack the contract and create many temporary contracts, and call this function in these contracts.

Figure 9 Airdrop hunting attack. Writing of Arbitrary Storage Address Attack The attack of arbitrary memory address writing is a common and harmful attack in the blockchain system. The attack can cause malicious users to write and overwrite any storage variable in the smart contract.

In Ethereum, the state variables of intelligent contracts will be stored in the storage area, which is an important and open contract storage space. Generally speaking, contract developers will set strict access control to the global variables stored in the storage area to ensure the security of contracts.

Storage key-value pair mapping is used to store data. If the user can arbitrarily control the key value of storage when writing, he or she can modify any storage variable value, so as to avoid all the related detection operations in the contract that uses the state variable value to check the authority, and thus achieve the purpose of improving the authority. In addition, because the attacker can use this vulnerability to destroy the contract storage structure, and perform any variable overwriting operation, such as overwriting the value of the state variable storing the address of the contract owner, this may cause abnormal execution of contract functions, freezing of funds, and other hazards.

Since the required guard is invalid, the contract owner can try to underflow the array size by executing the code of Figure 10 when the array length bonusCodes is 0. Therefore, we can write to any location in the storage arbitrarily. Figure 10 Writing of arbitrary storage address attack. Attacks of Network Layer The network layer is the most basic technical architecture in the blockchain system.

But these features also provide convenience for attackers who can easily launch a DoS attack. The purpose of the attack is to make users temporarily or permanently unable to use these services provided by the smart contract.

Gas Exhaustion Denial of Service Attack According to the design of Ethereum, when the smart contract is deployed or the function in the smart contract is called, the execution of the contract code needs a certain amount of gas to ensure that the calculation is completed completely.

At the same time, the Ethereum system limits the maximum total amount of gas consumed by each block, and the total amount of gas of all transactions in the block cannot exceed the maximum total amount of gas in this block. Once an operation in an intelligent contract consumes a lot of gas, resulting in the consumed gas value reaching the maximum total amount of gas in the block, the operation will not be successfully executed, and all processes depending on the operation will fail, so the contract cannot normally complete other functions, resulting in a denial of service state.

As Figure 11 shows, transferring money to everyone at once is likely to result in reaching the gas limit of ethereum blocks. Usually, this denial of service attack occurs when a contract developer does not consider the block gasLimit and introduces the operation of modifying dynamic data structure variables such as arrays whose size will change with time. After a block is mined, an attacker can issue multiple transactions at a higher gas price immediately, and then use the above operations of the contract to consume the gas limit of the whole block, so that the block does not contain any other transaction before a certain time, thus preventing other users from using the functions of the contract normally.

Figure 11 Gas exhaustion denial of service attack. Security Protection Schemes In this section, we propose the protection schemes against the ten attacks mentioned in the previous section. The details follow. Protection Schemes of the Application Layer We can prevent the replay attack in the following ways: 1 Avoiding using the transferProxy function and using a more secure signature method.

The nonce generation algorithm does not adopt the design of self-increment from 0 to avoid the same value as other scenarios. To prevent the false top-up attack, we judge not only transaction success but also whether the balance of the top-up wallet address increases accurately.

This judgment can be made through the Event log. Many centralized exchanges, wallets, and other service platforms obtain the transfer amount and judge the accuracy of the transfer through Event logs. However, we need to pay special attention to the evil situation of the smart contract, because the Event can be written arbitrarily, and it is not a mandatory default option that cannot be tampered with.

The required and asserted methods can also be used that an exception will be thrown directly to interrupt the execution of the subsequent instructions of the contract when the conditions are not met. The protection of transaction order dependence attack is a very complicated process. For the ERC20 transaction order dependence attack that happened once, it only needs the contract developers to pay attention to this problem and follow the best programming practices.

For the attack scenario constructed in this example, this problem is not the problem of the contract developer, but the problem of the Ethereum system itself. At present, the better solution is to confuse transactions, such as hiding transactions as internal transactions, and so on. Protection Schemes of Contract Layer For the problem of integer overflow, we can consider the results of each step by setting up a complete inspection mechanism, but this method is difficult and cumbersome, and it is not universal.

Therefore, OpenZeppelin provides SafeMath [ 32 ] in an intelligent contract function library, which can effectively prevent integer overflow. There are two ways to use the SafeMath library. The first one is to use the library functions directly, such as SafeMath. The other is that library functions can be called after using SafeMath for unit. For example, a. For the protection of re-entrancy attack, the most fundamental solution is to update all the states that should be changed in advance before the transfer, instead of updating them after the transfer, which depends on the smart contract developers to follow the best practices.

In addition, it is also an idea to use other transfer methods instead of the msg. For the designed attack scenarios, we use these two methods to test them, respectively. In this way, the code re-entrancy attack is successfully prevented, and the protection scheme is effective. For the second scheme, we use the transfer function to replace the msg.

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Also, the assertion is based on the growth of Ethereum between while Bitcoin was between In fact, the CEO of hedge fund Polychain Capital, Olaf Carlson-Wee, is of the opinion that the entire market capitalization of Ethereum will exceed that of Bitcoin by the end of this year. If it exceeds it, the price will triple. On the one hand, the previously mentioned fold explosion of Ether in 4 months versus the 8. Besides that, the fact that Bitcoin has lost almost half of its market share to Ethereum during the past year.

For example, a new coalition by the name of the Enterprise Ethereum Alliance EEA has sprung up with the explicit objective of facilitating and fostering the growth of the Ether. The endorsement of Ether by such heavy hitters only goes to show the amount of potential in this technology.

First of all, Ether might have several differences to Bitcoin, yet it still runs on blockchain technology. This means that the same problems, that all current blockchain technologies face, plague it too, mainly scalability. When we are talking about scalability, our primary concern is whether the increased number of users will adversely affect the transaction time or not.

Simply put, when more people use the technology, there are more transactions up for registering and putting down in the ledger. This increase in the number of transactions means that any one transaction will wait in a long queue before going into a block.

Without going into details, this issue has created what is called the fork problem. And there is no agreement on how to solve it. Another issue with Ether is the possibility of cloning the technology. After all, any company can start its own blockchain based cryptocurrency tomorrow. The only thing conferring value on any currency is the consensus of the community using said currency. Naturally, these problems, if left unsolved, jeopardize the future of Ether, hence making any Ethereum price predictions for problematic.

So the question is this: will these issues find solving? The general outlook is quite bullish, especially when it comes to the two main contenders: Bitcoin and Ether. Furthermore, the outlook on Ether is even more positive than that of Bitcoin. The optimists believe that Ether should experience a positive increase within the coming couple of years, letting it pass Bitcoin on the way.

And some skeptics worry about what these problems could mean for the entire space. Naturally, making Ethereum price predictions for is not an easy feat. And any forecast is liable to overlook a thing or two. Please share your opinions with us in the comments below, and feel free to ask any questions you might have.

Explore DeFi The internet of assets Ethereum isn't just for digital money. Anything you can own can be represented, traded and put to use as non-fungible tokens NFTs. You can tokenise your art and get royalties automatically every time it's re-sold. Or use a token for something you own to take out a loan. The possibilities are growing all the time. More on NFTs An open internet Today, we gain access to 'free' internet services by giving up control of our personal data.

Ethereum services are open by default — you just need a wallet.

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