Code similarity studies if two programs are similar or if one program is similar to a portion of another program (code containment). Code similarity is an important component of program analysis that finds application in many fields of computer science, such as reverse engineering of big collections of code fragments,

clone detection, identification of violations of the intellectual property of programs, malware detection, software maintenance, software forensics. In these applications, when comparing two fragments of code it is important to take into account changes due to code evolution, compiler optimization and post-compile obfuscation. These code changes give rise to fragments of code that are syntactically different while having the same intended behavior. This means that it is important to recognize modifications of the same program that are obtained though compiler optimization or code obfuscation as similar. There is necessary to abstract from syntactic changes and implementation details that do not alter the intended behavior of programs, namely that preserve to some extent the semantics of programs.

In order to consider both semantic meanings and syntactic patterns, existing tools for similarity analysis often employ mixed syntactic/symbolic and semantic representations of programs, as for example control flow graphs and dependency graphs that express the flow of control or the dependencies among program instructions. Recently, in  the authors investigate the use of symbolic finite automata(SFA) and their abstractions for the analysis of code similarity. SFAs have been introduced in as an extension of traditional finite state automata for modeling languages with a potential infinite alphabet. Transitions in a SFA are modeled as constraints interpreted in a given Boolean algebra, providing the semantic interpretation of constraints, and therefore the (potentially infinite) structural components of the language recognized (see . In  the authors show how SFAs can be used to represent both the syntax and the semantics of programs written in an arbitrary programming language, the idea is to label transitions with syntactic labels representing program instructions, while their interpretation is given by the semantics of such instructions. Thus, SFAs provide the ideal formal setting in order to treat within the same model, the abstraction of both the syntactic structure of programs and their intended semantics. A formal framework for the abstraction of syntactic and semantic properties of SFAs and therefore of programs turns out to be very useful in the understanding of existing similarity analysis tools, and in the development of similarity analysis tools based on semantic and syntactic properties of programs.

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Arora, B. (July 5, 2016). Exploring and analyzing Internet crimes and their behaviours. Perspectives in Science, 8, 540—542. Retrieved 6 December 2017 from https://core.ac.uk/download/pdf/82405095.pdf

Exploring and analyzing Internet crimes and their behaviours

Introduction

 The rising rate of cybercrime with its varied and diverse characteristic features has been a source of concern. Cyber-crime is a term that covers a broad scope of criminal. Cybercrime is referred to the act of performing criminal act using cyberspace as the communication medium. As a consequence of rapid globalization, low cost of mobile phones and easy access to Internet the cyber crime. The cyber-crimes like cyber bullying and cyber defamation are common issues and are rapidly increasing. The government is framing policies and laws to prevent the growing number of such crimes. Most of the countries are not fully equipped with the legal infrastructure to handle cybercrimes. Young children and youth are among the most targeted section of the society that is affected by the perilous effects of electronic media. Broadly, the cybercrimes can be of Type I and Type II.

 Type I cybercrime is generally a single event from the perspective of the victim. Type II cybercrimes, on the other hand refers to on-going series of events, involving repeated interactions with the target. These activities are such as computer related frauds, cyber defamation, cyber harassment, child predation, identity theft, extortion, travel scam, stock market manipulation, complex corporate espionage, planning or carrying out terrorist activities, health care, insurance/bonds frauds, auction frauds, fake escrow scams, blackmail, non-delivery of merchandize, newsgroup scams, credit card frauds, email spoofing, salami attacks, data didling, sabotage web jacking, spamming, DoS, software piracy, forgery etc.

Categories of cybercrimes

The role of computer in cybercrime can be classified in narrow or broad sense where computer can be used as an object, a tool or computer as the environment or context. The cybercrimes can be broadly classified as follows: