459 research outputs found
Malware "Ecology" Viewed as Ecological Succession: Historical Trends and Future Prospects
The development and evolution of malware including computer viruses, worms,
and trojan horses, is shown to be closely analogous to the process of community
succession long recognized in ecology. In particular, both changes in the
overall environment by external disturbances, as well as, feedback effects from
malware competition and antivirus coevolution have driven community succession
and the development of different types of malware with varying modes of
transmission and adaptability.Comment: 13 pages, 3 figure
Survey of Machine Learning Techniques for Malware Analysis
Coping with malware is getting more and more challenging, given their
relentless growth in complexity and volume. One of the most common approaches
in literature is using machine learning techniques, to automatically learn
models and patterns behind such complexity, and to develop technologies for
keeping pace with the speed of development of novel malware. This survey aims
at providing an overview on the way machine learning has been used so far in
the context of malware analysis. We systematize surveyed papers according to
their objectives (i.e., the expected output, what the analysis aims to), what
information about malware they specifically use (i.e., the features), and what
machine learning techniques they employ (i.e., what algorithm is used to
process the input and produce the output). We also outline a number of problems
concerning the datasets used in considered works, and finally introduce the
novel concept of malware analysis economics, regarding the study of existing
tradeoffs among key metrics, such as analysis accuracy and economical costs
Malware Detection Based on Structural and Behavioural Features of API Calls
In this paper, we propose a five-step approach to detect obfuscated malware by investigating the structural and behavioural features of API calls. We have developed a fully automated system to disassemble and extract API call features effectively from executables. Using n-gram statistical analysis of binary content, we are able to classify if an executable file is malicious or benign. Our experimental results with a dataset of 242 malwares and 72 benign files have shown a promising accuracy of 96.5% for the unigram model. We also provide a preliminary analysis by our approach using support vector machine (SVM) and by varying n-values from 1 to 5, we have analysed the performance that include accuracy, false positives and false negatives. By applying SVM, we propose to train the classifier and derive an optimum n-gram model for detecting both known and unknown malware efficiently
Application of Artificial Intelligence for Detecting Derived Viruses
Computer viruses have become complex and operates in a stealth mode to avoid detection. New viruses are argued to be created each and every day. However, most of these supposedly ânewâ viruses are not completely new. Most of the supposedly ânewâ viruses are not necessarily created from scratch with completely new (something novel that has never been seen before) mechanisms. For example, most of these viruses just change their form and signatures to avoid detection. But their operation and the way they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In this paper, the authors refer to such viruses as derived viruses. Just like new viruses, derived viruses are hard to detect with current scanning-detection methods. Therefore, this paper proposes a virus detection system that detects derived viruses better than existing methods. The proposed system integrates a mutating engine together with neural network to improve the detection rate of derived viruses. Experimental results show that the proposed model can detect derived viruses with an average accuracy detection rate of 80% (this include 91% success rate on first generation, 83% success rate on second generation and 65% success rate on third generation). The results further shows that the correlation between the original virus signature and its derivatives decreases further down along its generations
Malware Resistant Data Protection in Hyper-connected Networks: A survey
Data protection is the process of securing sensitive information from being
corrupted, compromised, or lost. A hyperconnected network, on the other hand,
is a computer networking trend in which communication occurs over a network.
However, what about malware. Malware is malicious software meant to penetrate
private data, threaten a computer system, or gain unauthorised network access
without the users consent. Due to the increasing applications of computers and
dependency on electronically saved private data, malware attacks on sensitive
information have become a dangerous issue for individuals and organizations
across the world. Hence, malware defense is critical for keeping our computer
systems and data protected. Many recent survey articles have focused on either
malware detection systems or single attacking strategies variously. To the best
of our knowledge, no survey paper demonstrates malware attack patterns and
defense strategies combinedly. Through this survey, this paper aims to address
this issue by merging diverse malicious attack patterns and machine learning
(ML) based detection models for modern and sophisticated malware. In doing so,
we focus on the taxonomy of malware attack patterns based on four fundamental
dimensions the primary goal of the attack, method of attack, targeted exposure
and execution process, and types of malware that perform each attack. Detailed
information on malware analysis approaches is also investigated. In addition,
existing malware detection techniques employing feature extraction and ML
algorithms are discussed extensively. Finally, it discusses research
difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye
Code Obfuscation and Virus Detection
Typically, computer viruses and other malware are detected by searching for a string of bits which is found in the virus or malware. Such a string can be viewed as a âfingerprintâ of the virus. These âfingerprintsâ are not generally unique; however they can be used to make rapid malware scanning feasible. This fingerprint is often called a signature and the technique of detecting viruses using signatures is known as signaturebased detection [8]. Today, virus writers often camouflage their viruses by using code obfuscation techniques in an effort to defeat signature-based detection schemes. So-called metamorphic viruses are viruses in which each instance has the same functionality but differs in its internal structure. Metamorphic viruses differ from polymorphic viruses in the method they use to hide their signature. While polymorphic viruses primarily rely on encryption for signature obfuscation, metamorphic viruses hide their signature via âmutatingâ their own code [3]. The paper [1] provides a rigorous proof that metamorphic viruses can bypass any signature-based detection, provided the code obfuscation has been done carefully based on a set of specified rules. Specifically, according to [1], if dead code is added and the control flow is changed sufficiently by inserting jump statements, the virus cannot be detected. In this project we first developed a code obfuscation engine conforming to the rules in [1]. We then used this engine to create metamorphic variants of a seed virus (created using the PS-MPK virus creation kit [15]) and demonstrated the validity of the assertion in [1] about metamorphic viruses and signature based detectors. In the second phase of this project we validated another theory advanced in [2], namely, that machine learning based methodsŸspecifically ones based on Hidden Markov Model (HMM) Ÿcan detect metamorphic viruses. In other words, we show that a collection of metamorphic viruses which are (provably) undetectable via signature detection techniques can nevertheless be detected using an HMM approach
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