Artificial Intellect In Antivirus Diagnosis System Computer Research Essay

Abstract- Artificial intellect (AI) techniques have performed ever more important role in antivirus diagnosis. At present, some principal artificial cleverness techniques applied in antivirus detection are suggested, including heuristic technique, data mining, agent strategy, artificial immune system, and manufactured neural network. It is convinced that it will enhance the performance of antivirus diagnosis systems, and promote the production of new manufactured cleverness algorithm and the application in antivirus detection to incorporate antivirus recognition with artificial intelligence. This paper presents the main man-made intelligence solutions, especially Heuristic which have been applied in antivirus system. On the other hand, it also points out a fact that combining all types of artificial intelligence technology will become the primary development trend in neuro-scientific antivirus.

Keywords- Anti-virus, Artificial Cleverness, Data mining, Heuristic, Neural network


Artificial Intellect (AI) is the branch of computer science which handles intelligence of machines where an intelligent agent is a system that perceives its environment and calls for actions which optimize its likelihood of success. They have numerous applications like robotics, medicine, Finance, Space.

One of the very most recent one is antivirus softwares.

Here we give details regarding heuristic method found in antivirus software.

Malware and its types

Malware (destructive software) is software made to infiltrate or damage your computer system minus the owner's educated consent.

Malware types

We can identify quite few harmful software types. It is important to be aware that nevertheless most of them have similar goal, each one behave differently.




Trojan horses



Due to different behaviour, each malware group uses alternate means of being undetected. This pushes anti-virus software manufacturers to develop numerous alternatives and countermeasures for computer safety. This paper focuses on methods used specifically for virus detection, not necessarily effective against other types of harmful software.

Infection Strategies

To better know how viruses are found and recognized, it is essential to divide them by their disease ways.

A. Non Citizen Viruses

The simplest form of trojans which don't stay static in storage area, but infect founded executable record and seek out another to reproduce.

Resident viruses

More intricate and efficient type of viruses which stay static in memory and cover their existence from other operations. Sort of TSR apps.

Fast infectors type which is designed to infect as much files as it can be.

Slow infectors using stealth and encryption ways to stay undetected outlast.

Methods Used

A. Metaheuristic

Metaheuristic is a heuristic way for solving a very general course of computational problems by incorporating user-given black-box strategies in a maybe reliable way. Metaheuristics are usually put on problems for which there is absolutely no sufficient problem-specific algorithm or heuristic.

B. Heuristic

Heuristic is a method to help solve an issue, commonly a casual method. It is specifically used to speedily come to a solution that is fairly close to the perfect answer.

General Heuristics

It is important to remember that metaheuristics are only 'ideas' to solve a problem not really a specific way to achieve that. List below shows main metaheuristics used for trojan detection and acceptance

Pattern matching

Automatic learning

Environment emulation

Neural networks

Data mining

Bayes networks

Hidden Markov models

Concrete Heuristics

Specific heuristics virtually used in pathogen detection and acceptance, are effortlessly inherited from metaheuristics.

And so, for example concrete method for virus recognition using neural networks can be execution of SOM (Home Organizing Map). Neural Sites (metaheuristic) ' SOM (heuristic).

The most popular, and one of most productive heuristic used by

anti-virus software is approach called Heuristic Scanning.

Lacks in Specific Detection

Great deal of modern viruses are only slightly changed variants of few conceptions developed years ago. Specific diagnosis methods like signature scanning became very productive means of detecting known dangers. Finding specific signature in code allows scanning device to recognize every pathogen which personal has been stored in built-in databases.

BB ?2 B9 10 01 81 37 ?2 81 77 02 ?2 83 C3 04 E2 F2

FireFly disease signature(hexadecimal)

Problem occurs when computer virus source is evolved by way of a programmer or mutation engine. Signature has been malformed scheduled to even minimal changes. Disease may behave in an exactly same way but is undetectable scheduled to new, unique personal.

BB ?2 B9 10 01 81 37 ?2 81 A1 D3 ?2 01 C3 04 E2 F2

Malformed signature(hexadecimal)

Heuristic Scanning

We can recognise a pathogen without evaluating its

structure by its behavior and characteristics. Heuristic scanning in its basic form is implementation of three metaheuristics

Pattern matching

Automatic learning

Environment emulation

The basic idea of heuristic scanning is to examine assembly language instructions sequences(step-by-step) and define them by their potential harmfulness. If there are sequences behaving suspiciously, program can be trained as a disease. The phenomenon of the method is that this actually detects threats that aren't yet known!

Fig1. Study of assembly vocabulary sequence

A. Recognising Potential Threat

In real anti-virus software, heuristic scanning is put in place to recognize dangers by following built-in rules, e. g. if program will try to format hard drive its behavior is highly suspicious but it can be only simple drive energy. Singular suspicion is never grounds to bring about the alarm. But if the same program also attempts to remain resident and contains tedious tosearch for executables, it is highly probable that it's a real computer virus. AV software frequently classifies sequences by their behaviour granting them a flag. Every flag has its weight, if total values for just one program exceeds a predefined threshold, scanner regards it as computer virus.

Fig. 2. Single-layer classifier with threshold

Heuristics Flags

Some scanners set a flag for each suspected ability which includes been within the file being analyzed. This helps it be easier to clarify to an individual what has been found. TbScan for illustration identifies many suspected training sequences. Every suspected training sequence has a flag given to it.

A. Flag Information

F = Suspicious file access. Could probably infect a data file.

R = Relocator. Program code will be relocated in a dubious way.

A = Dubious Memory Allocation. The program runs on the non-standard way to search for, and/or allocate ram.

N = Wrong name extension. Expansion conflicts with program framework.

S = Contains a regular to find executable (. COM or. EXE) files.

# = Found an instructions decryption routine. That is common for infections also for some shielded software.

E = Adaptable Entry-point. The code appears to be designed to be associated on any location within an executable file. Common for viruses.

L = The program traps the loading of software. Might be a trojan that intercepts program insert to infect the program.

D = Drive write access. This program writes to drive without needing DOS.

M = Storage area resident code. This program was created to stay in memory space.

! = Invalid opcode (non-8088 instructions) or out-of-range branch.

T = Wrong timestamp. Some viruses use this to mark afflicted files.

J = Suspicious jump construct. Entry way via chained or indirect jumps. This is unusual for normal software but common for infections.

? = Inconsistent exe-header. May be a trojan but may also be a insect.

G = Garbage instructions. Contains code that seems to have no purpose other than encryption or keeping away from recognition by trojan scanners.

U = Undocumented interrupt/DOS call. This program might be just tricky but can also be a virus by using a non-standard way to detect itself.

Z = EXE/COM determination. The program attempts to check whether a record is a COM or EXE record. Viruses need to do this to infect an application.

O = Found code that can be used to overwrite/move a program in memory space.

B = Back to entry point. Contains code to re-start the program after adjustments at the entry-point are created. Very regular for trojans.

K = Unconventional stack. The program has a suspicious stack or an peculiar stack.

Avoiding False Positives

Just like all other generic detection techniques, heuristic scanners sometimes blame innocent programs to be contaminated by way of a virus. That is called a "false positive" or "False Alarm". The explanation for this is simple. Some programs eventually have several suspected ability.

If a heuristic scanner pops up with a message saying: "This program can format a drive and it stays resident in memory", and this program is a citizen disk format power, is this really a false alarm? Actually, the scanner is right. A resident format utility certainly contains code to format a drive, and it contains code to remain resident in storage area.

The heuristic scanning device is therefore completely right! You may name it a bogus suspicion, but not a phony positive. Really the only problem here is that the scanning device says that it could be a virus. If you believe the scanner lets you know it has found a trojan, it turns out to be a false alarm. However, if you take this information as is, expressing 'ok, the facts you reported are true for this program, I could verify this so that it is not a computer virus', I wouldn't depend it as a fake alarm. The scanner just tells the reality. The primary problem this is actually the person who has to make decisions with the information given by the scanner. If it is a novice user, it is a problem.

Whether we call it a incorrect positive or a fake suspicion doesn't subject. We don't like the scanner to yell each time we scan. So we need to avoid this example. Just how do we achieve this?

Definition of (combinations of) dubious abilities

Recognition of common program codes

Recognition of specific programs

Assumption that the device is at first not infected

Performance of Heuristics Scanning

Heuristics is a relatively new technique and still under development. It is however increasing importance rapidly. This isn't unexpected as heuristic scanners are able to discover over 90% of the trojans without using any predefined information like signatures or checksum ideals. The amount of false positives depends upon the scanning device, but a physique only 0. 1% can be reached easily. A incorrect positive test however is more difficult to perform so are there no indie results available.

Pros and Cons

A. Advantages

Can find future viruses. User is less reliant on product upgrades.

B. Disadvantages

False positives are possible. Judgment of the effect requires some basic knowledge.


Thus, artificial intelligence technique helps bettering the performance of antivirus softwares.

This detection-avoiding method makes detection by typical anti-virus products easier since it means that the programmer can not use very limited and upright code. The computer virus writer will be required to write more technical viruses. Thus man-made intelligence escalates the threat to trojan writers.


I hereby thank Ms. Padmapriya for encouraging and supporting us for the distribution of the paper

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