On Malware Characterization and Attack Classification

نویسندگان

  • Alan Lee
  • Vijay Varadharajan
  • Udaya Kiran Tupakula
چکیده

Malware is one of the significant problems in the current Internet. Often security tool vendors develop an attack signature to deal with the attacks. However attack techniques such as polymorphism and metamorphism can be used by the attacker to generate multiple variants of the malware and complicate the signature identification. In this paper we present our analysis on sample set of malware and then discuss how MAEC’s taxonomy can help to address the malware problem. .

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Feature-based Malicious URL and Attack Type Detection Using Multi-class Classification

Nowadays, malicious URLs are the common threat to the businesses, social networks, net-banking etc. Existing approaches have focused on binary detection i.e. either the URL is malicious or benign. Very few literature is found which focused on the detection of malicious URLs and their attack types. Hence, it becomes necessary to know the attack type and adopt an effective countermeasure. This pa...

متن کامل

Malware Detection using Classification of Variable-Length Sequences

In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. Acco...

متن کامل

A Framework for Optimizing Malware Classification by Using Genetic Algorithm

Malware classification is a vital in combating the malware. Malware classification system is important and work together with malware identification to prepare the right and effective antidote for malware. Current techniques in malware classification do not give a good classification result when it deals with the new and unique types of malware. For this reason, we proposed the usage of Genetic...

متن کامل

A Stochastic Approach for Malware Detection in Mobile Network

Wireless mobile devices have turned out to be the integral part of all human communication. As a result, the computer malware is now drifting from computers to mobile phones. The problem of optimal distribution of the content-based signatures of malware helps to detect the corresponding malware and disable further propagation, in order to minimize the number of infected nodes. But in some cases...

متن کامل

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

Deep neural networks (DNNs) are used to solve complex classification problems, for which other machine learning classifiers, such as SVM, fall short. Recurrent neural networks (RNNs) have been used for tasks that involves sequential inputs, such as speech to text. In the cyber security domain, RNNs based on API calls have been used effectively to classify previously un-encountered malware. In t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013