Re: ul
Brandi Baylon
The first academic work on the theory of self-replicating computer programs was done in 1949 by John von Neumann who gave lectures at the University of Illinois about the "Theory and Organization of Complicated Automata". The work of von Neumann was later published as the "Theory of self-reproducing automata". In his essay von Neumann described how a computer program could be designed to reproduce itself.[12] Von Neumann's design for a self-reproducing computer program is considered the world's first computer virus, and he is considered to be the theoretical "father" of computer virology.[13] In 1972, Veith Risak directly building on von Neumann's work on self-replication, published his article "Selbstreproduzierende Automaten mit minimaler Informationsübertragung" (Self-reproducing automata with minimal information exchange).[14] The article describes a fully functional virus written in assembler programming language for a SIEMENS 4004/35 computer system. In 1980, Jürgen Kraus wrote his Diplom thesis "Selbstreproduktion bei Programmen" (Self-reproduction of programs) at the University of Dortmund.[15] In his work Kraus postulated that computer programs can behave in a way similar to biological viruses.
Virus for windows. We create the simplest malicious program in assembler \"Hacker\"
Download https://psfmi.com/2yW06e
The Creeper virus was first detected on ARPANET, the forerunner of the Internet, in the early 1970s.[16] Creeper was an experimental self-replicating program written by Bob Thomas at BBN Technologies in 1971.[17] Creeper used the ARPANET to infect DEC PDP-10 computers running the TENEX operating system.[18] Creeper gained access via the ARPANET and copied itself to the remote system where the message, "I'M THE CREEPER. CATCH ME IF YOU CAN!" was displayed.[19] The Reaper program was created to delete Creeper.[20]
A power virus is a computer program that executes specific machine code to reach the maximum CPU power dissipation (thermal energy output for the central processing units). Computer cooling apparatus are designed to dissipate power up to the thermal design power, rather than maximum power, and a power virus could cause the system to overheat if it does not have logic to stop the processor. This may cause permanent physical damage. Power viruses can be malicious, but are often suites of test software used for integration testing and thermal testing of computer components during the design phase of a product, or for product benchmarking.[69]
Examples of Microsoft Windows anti virus and anti-malware software include the optional Microsoft Security Essentials[96] (for Windows XP, Vista and Windows 7) for real-time protection, the Windows Malicious Software Removal Tool[97] (now included with Windows (Security) Updates on "Patch Tuesday", the second Tuesday of each month), and Windows Defender (an optional download in the case of Windows XP).[98] Additionally, several capable antivirus software programs are available for free download from the Internet (usually restricted to non-commercial use).[99] Some such free programs are almost as good as commercialcompetitors.[100] Common security vulnerabilities are assigned CVE IDs and listed in the US National Vulnerability Database. Secunia PSI[101] is an example of software, free for personal use, that will check a PC for vulnerable out-of-date software, and attempt to update it. Ransomware and phishing scam alerts appear as press releases on the Internet Crime Complaint Center noticeboard. Ransomware is a virus that posts a message on the user's screen saying that the screen or system will remain locked or unusable until a ransom payment is made. Phishing is a deception in which the malicious individual pretends to be a friend, computer security expert, or other benevolent individual, with the goal of convincing the targeted individual to reveal passwords or other personal information.
Types of malware include computer viruses, worms, Trojan horses, ransomware and spyware. These malicious programs steal, encrypt and delete sensitive data; alter or hijack core computing functions and monitor end users' computer activity.
One of the first known examples of malware was the Creeper virus in 1971, which was created as an experiment by BBN Technologies engineer Robert Thomas. Creeper was designed to infect mainframes on ARPANET. While the program did not alter functions or steal or delete data, it moved from one mainframe to another without permission while displaying a teletype message that read, "I'm the creeper: Catch me if you can." Creeper was later altered by computer scientist Ray Tomlinson, who added the ability to self-replicate to the virus and created the first known computer worm.
Have you ever wished you could create your own virus, either for your own learning or as a prank? Virus creation takes time and knowledge, but anyone can do it if they put their mind to it. Creating a virus can teach you a lot about how a programming language works, as well as operating system and network security. While it may seem as if all viruses are malicious, viruses are simply pieces of code whose goal is to spread as many copies of itself as possible. See Step 1 below to get started and have fun creating your own virus.
Computer viruses are a type of malicious software that infects a computer and hijacks its resources to self-replicate and spread. Computer viruses inject their own malicious code into programs or files, and they can inflict lots of damage, including corrupting or erasing files and stealing sensitive data.
Get an antivirus program.
Using an antivirus app will protect you against malware and viruses before they infect your computer. The best antivirus software also includes protection against malicious websites, phishing scams, unsafe downloads, and more.
Today, most malware is a combination of traditional malicious programs, often including parts of Trojans and worms and occasionally a virus. Usually the malware program appears to the end-user as a Trojan, but once executed, it attacks other victims over the network like a worm.
As an alternative to online scanners, I encourage pentester's to simulate their target's operating system environment using virtual machines. For example, if it's discovered that a target on the local network is using Windows 10 with AVG or Avast, create a Windows 10 VM, install the latest antivirus software in the VM, and test payloads inside the VM. This will give pentester's some reassurance that a payload is working properly and prevent VirusTotal from over-analyzing the malicious file and sharing its results with other companies.
A computer virus is a type of malware that attaches to another program (like a document), which can replicate and spread after a person first runs it on their system. For instance, you could receive an email with a malicious attachment, open the file unknowingly, and then the computer virus runs on your computer. Viruses are harmful and can destroy data, slow down system resources, and log keystrokes.
A computer virus is a type of malware that attaches to another program (like a document), which can replicate and spread after a person first runs it on their system. or instance, you could receive an email with a malicious attachment, open the file unknowingly, and then the computer virus runs on your computer.
Metamorphic malware are self-modifying programs which apply semantic preserving transformations to their own code in order to foil detection systems based on signature matching. Metamorphism impacts both software security and code protection technologies: it is used by malware writers to evade detection systems based on pattern matching and by software developers for preventing malicious host attacks through software diversification. In this paper, we consider the problem of automatically extracting metamorphic signatures from the analysis of metamorphic malware variants. We define a metamorphic signature as an abstract program representation that ideally captures all the possible code variants that might be generated during the execution of a metamorphic program. For this purpose, we developed MetaSign: a tool that takes as input a collection of metamorphic code variants and produces, as output, a set of transformation rules that could have been used to generate the considered metamorphic variants. MetaSign starts from a control flow graph representation of the input variants and agglomerates them into an automaton which approximates the considered code variants. The upper approximation process is based on the concept of widening automata, while the semantic preserving transformation rules, used by the metamorphic program, can be viewed as rewriting rules and modeled as grammar productions. In this setting, the grammar recognizes the language of code variants, while the production rules model the metamorphic transformations. In particular, we formalize the language of code variants in terms of pure context-free grammars, which are similar to context-free grammars with no terminal symbols. After the widening process, we create a positive set of samples from which we extract the productions of the grammar by applying a learning grammar technique. This allows us to learn the transformation rules used by the metamorphic engine to generate the considered code variants. We validate the results of MetaSign on some case studies.
On the other hand, we are aware that numerous future works need to be accomplish in order to complete the process of our new methodology in analyzing metamorphic behaviors. As a priority of future work, we will try to apply this tool to a set of real malware variants. Currently, our proposed tool reads program code written in our intermediate x86-like language. The conversion process should be automated by an ad-hoc disassembler, similar to the one implemented in MetaPHOR. In this way, an automated metamorphic signature extraction program can be implemented: given a set of disassembled payload of metamorphic variants, or parts of them, generated by the same metamorphic engine, we can feed them to MetaSign in order to extract a possible metamorphic signature, i.e., a set of rewriting rules used by the unknown metamorphic engine. In this work, we considered one level of abstraction on the instructions, that is, we discard the operands of each assembly instruction. Clearly, this strong abstraction influences the accuracy results since MetaSign is more likely to capture a rule by visiting a widening CFG where all nodes, i.e., instructions, have no arguments. It would be interesting to consider different abstractions assigning to the operands, for example, symbolic values such as those of [31]. We would like to point out that MetaSign is implemented from scratch, i.e., with no code reuse from other implemented software. Space and time complexity of the whole program need to be optimized as this task was not a priority for our purposes. The current implementation can be extended with new rewriting rules and a new learner algorithm. Currently, only rules having form \(\x\) are implemented. The learning algorithm is a critical core part, as the third case study highlighted: an optimal learner algorithm should exploit the tuning of the widening language length parameter. Moreover, the new learner needs to be able to learn, in an approximate way, more complex rewriting rules in order to catch more sophisticated metamorphic engines and it should be able to generate a sound metamorphic signature, i.e., a set of rules that generate all the real metamorphic variants, namely, all the possible variants that the unknown metamorphic engine can create, admitting some false positives (spurious variants). Finally, new more expressive formal languages should be considered modeling code mutations, such as, e.g., indexed grammars [32, 33] or context-sensitive grammars, and their respective learning algorithms.
aa06259810