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Classification of Ransomware Based on Artificial Neural Networks: Proceedings of EMENA-ISTL 2018
Abstract
Currently, different forms of ransomware are increasingly threatening users. Modern ransomware encrypts important user data and it is only possible to recover it once a ransom has been paid [14]. In this paper, we classify ransomware in 10 classes which are labeled using avclass tool. In this classification, we based on artificial neural networks with multilayer perceptron function. To do this, it was necessary to build the learning base based on ransomware files. We then implemented programs in java allowing the extraction of the key strings from ransomwares files intended for the learning stage and for the test one. Once the learning and testing databases have been prepared, we started the classification with the weka tool. The objective of this contribution is to investigate if the neural networks are an effective means for the classification of this kind of ransomwares or it will be necessary to think to another method of classification.
... Malware comes in several types; our goal is to focus on ransomware. The paper 9 presents our previous work, which was a comparative study of a proposed ransomware dataset on work 10 with a new proposed dataset. We changed the learning files but we did not have a test dataset, so we took 20% of the learning database file to build our test database. ...
... We changed the learning files but we did not have a test dataset, so we took 20% of the learning database file to build our test database. The results were high compared to those in paper 10 but the problem was that the 20% of files taken, was not removed from the learning database, we copied the test part in question to another file. We obtained approximately a rate of 70% for correctly classified instances, but they were not as surprising and relevant as expected. ...
Malware threat the security of computers and Internet. Among the diversity of malware, we have “ransomware”. Its main objective is to prevent and block access to user data and computers in exchange for a ransom, once paid, the data will be liberated. Researchers and developers are rushing to find reliable and safe techniques and methods to detect Ransomware to protect the Internet user from such threats. Among the techniques generally used to detect malware are machine learning techniques. In this paper, we will discuss the different types of neural networks, the related work of each type, aiming at the classification of malware in general and ransomware in particular. After this study, we will talk about the adopted methodology for the implementation of our neural network model (multilayer perceptron). We tested this model, firstly, with the binary detection whether it is malware or goodware, and secondly, with the classification of the nine families of Ransomware by taking the vector of our previous work and we will make a comparison of the accuracy rate of the instances that are correctly classified.
... Malware comes in several types; our goal is to focus on ransomware. The paper [6] presents our previous work, which was a comparative study of a proposed ransomware dataset on work [7] with a new proposed dataset. We changed the learning files but we did not have a test dataset, so we took 20% of the learning database file to build our test database. ...
... We changed the learning files but we did not have a test dataset, so we took 20% of the learning database file to build our test database. The results were high compared to that in paper [7] but the problem was that the 20% files taken, was not removed (but copied) from the learning database, we copied the test part in question to another file. We obtained approximately a rate of 70% for correctly classified instances, but were not as surprising and relevant as expected. ...
Among the diversity of malware, we mention “Ransomware”. Its main objective is to take a user’s data hostage, preventing and blocking access to the data and even to the computer (depending on the type of attack). The data is released once the ransom is paid. Despite the efforts of developers and the research community, this scourge remains a viable security threat that is why there is competition between IT community (developers and researchers) to realize and create a tool and method to detect malware, among the methods and techniques used are Artificial Intelligence and more precisely machine learning using neural networks. In this paper, we will discuss the different types of neural networks, the related work of each type, aiming at the classification of malware in general and ransomware in particular. After this study, we will talk about the methodology followed for the implementation of our neural network model (multilayer perceptron). We tested this model, firstly, with the binary classification if it is a malware or a goodware, and secondly, with the classification of the nine families of Ransomware by taking the vector of our previous work and we will make a comparison on the accuracy rate of the instance that are correctly classified.
This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.
1. Introduction
Existing Chinese-English parallel corpus noise processing systems with high accuracy rate still have the disadvantages of time consumption, high cost, and inconvenient use [1]. The actual voice recognition system requires real-time Chinese-English parallel corpus noise processing on a general-purpose computer with limited resources [2]. Therefore, the development of fast recognition algorithms has been important in the study on noise processing of Chinese-English parallel corpora. Chinese-English parallel corpus noise processing technology is a subject that uses computers to analyze speech signals to realize automatic understanding of human speech [3]. Speech recognition technology has become a very active research field in information science. As a cross-discipline, it is gradually becoming a key technology of human-computer interaction in information technology [4]. Speech signal processing is a discipline that studies the use of digital signal processing techniques to deal with noise in Chinese-English parallel corpora. The purpose of processing is to obtain certain parameters for efficient transmission or storage or for certain applications, such as speech synthesis, Chinese-English parallel corpus noise processing, and speech enhancement. [5]. It is not only an effective and convenient way of information exchange, but also an important tool for humans to use machines. Whether it is the language communication between humans and machines, the noise processing of Chinese-English parallel corpus, especially the digital processing of voice signals, has a particularly important role [6]. Once voice recognition and voice synthesis technology are combined, people can leave the keyboard, receive voice commands, and perform operations [7].
Mohammad [8] proposed a neural network machine translation architecture, which is completely in terms of the neural network structure and is divided into two parts. The encoder converts the source language text into a set of context vectors and then decodes them. The processor then decodes the set of vectors into target language text. This structure completely gets rid of the previous statistical machine translation architecture. The model no longer includes explicit word alignment and translation rule extraction steps, which simplifies the complicated feature design work brought about by the complexity and change of natural language itself. With the attention mechanism proposed by Mojrian [9], the ability of neural network machine translation of processing long sentences has been further improved. The attention mechanism separately calculates the alignment information of the corresponding parts between the source sequence and the target sequence through weight distribution, so that the model “targets” the specified part in the training and prediction stages. Later, Lazli [10] and others further studied the attention mechanism, replacing the entire sentence with a fixed-length window, reducing the amount of calculation of this mechanism. The proposal of the attention force mechanism makes the results of neural network machine translation comparable to traditional statistical machine translation. As a result, neural network-based machine translation methods have become the mainstream method in the research field. At this stage, in order to overcome the gradient disappearance and gradient explosion problems that may be caused by the classic recurrent neural network model, the nodes of the network usually use complex structures such as LSTM (Long-Short Term Memory) and its variant GRU (Gated Recurrent Unit), so that model training is slow. Subsequently, in order to strengthen the accuracy of model training, Sheta [11] introduced a translation model based on convolutional neural networks, which uses convolutional neural networks to window and hierarchically extract sentence features, while retaining the accuracy of recurrent neural networks. Next, model training is accelerated through parallel computing. Poddar [12] realized the English-Chinese machine translation mode which is based on the sample neural network of the attention mechanism, using different Word2Vec models to generate English word vectors, and optimized the English-Chinese neural network machine translation model. Some scholars have implemented the machine translation model based on convolutional neural network and transformer-based English-Chinese machine translation model adding pretrained word vectors to the English-Chinese translation model and improving the quality of the model by providing prior information [13–15].
This article analyzes the specific content of neural networks and genetic algorithms on the basis of their respective shortcomings and analyzes the necessity and feasibility of combining neural networks and genetic algorithms. In the research of this article, by using the generation gap operator and the intersection operator based on the convex set theory, an improved genetic algorithm for the learning of neural network weights is formed, and the algorithm is used in the verification of the progressive voice. At the same time, the artificial neural network method can be helpful to design and implement the genetic algorithm. The impulse response or step response curve of the object is easier to obtain in the process. Take their series of values at the sampling moment as the information describing the dynamic characteristics of the object to form a predictive model. Because the nonparametric model is easier to obtain and the calculation is simple, the robustness is better. The structure and characteristics of the multilayer feedforward neural network are analyzed and summarized, as well as the computing power and function approximation of the multilayer feedforward neural network. Several methods for selecting the number of internal nodes and finally two heuristic algorithms and the implementation process are given: a detailed design of a genetic algorithm model is given, and related tests and performance analysis are done.
2. Chinese-English Parallel Corpus Noise Processing Model Based on Multilayer Perceptron Genetic Algorithm Neural Network
2.1. Multilayer Perceptron Hierarchical Distribution
Digital Chinese-English parallel corpus noise processing includes three aspects, namely, the digital representation method of Chinese-English parallel corpus noise, various methods and techniques of Chinese-English parallel corpus noise digital processing theory, and their practical applications in various fields [16]. Figure 1 shows the hierarchical spatial distribution of multilayer perceptrons.
In the present time Chatbot is an essential tool used by many organizations to provide services to their targeted customers round the clock. This research focuses on a domain-specific Chatbot that can be helpful for educational institutes. This Chatbot will be a virtual (representation) to the admission seekers. It will provide answers regarding the university, its departments, admission fees and other admission related FAQ. For the sake of the research, frequently asked questions of a university were collected and an unsupervised learning model along with natural language processing techniques was deployed to answer the questions of the admission candidates. Tokenization, stop words removal followed by vectorization were implemented for preprocessing the training data. User’s inputs were similarly processed and then tf-idf based cosine similarity applied to retrieve the best answer. Later, a user-centric evaluation metric was used to evaluate the model and as per the metric, our current model showed approximately 80% accuracy.
Machine Learning automatically creates analytical models that adapt to what is in the data. After a while, the algorithm is used to deliver accurate results, whether it’s making smarter credit decisions, entering into retail deals, medical diagnostics, or detecting fraud. The use of Deep Learning technology as a new machine learning tool has experienced considerable success in digital image processing in recent years. In order to contribute to the classification and the security of Medical Images (storage, sharing, transfer …), we present, in this paper:a comparative study between the famous Convolutional Neural Network architectures and ACSA-Net that we propose concerning the classification of Brain Tumors detected in the Magnetic Resonance Imaging.a new digital signature method using Artificial and Convolutional Neural Network that consists of inserting into the Medical Image the Signature information like the hospital center and patient data, using Watermarking. It must ensure the integrity, confidentiality of the images when it is shared and the robustness to the different types of attacks (JPEG compression, copy and paste, geometric transformations, etc.). As a result, we have shown that the use of Machine and Deep Learning techniques makes it possible to detect the Brain Tumors, in particular ACSA-Net major in terms of recognition rate. And the signature process proposed resists various frequency analysis attacks.
Nowadays, ransomware has become the most widespread malware targeting businesses and individuals. It's one of the computer viruses that infiltrate servers, computers, smartphones etc... In this paper and based on the previous work, we will modify and re-classify the ransomware in 9 classes labeled; to make this classification we used artificial neural networks and Bayesian networks. To do this task, we had to rebuild a new learning base that relies on the new files. We used Java programs previously implemented to make a new extraction of strings, which allows us to identify common strings in the system calls of each ransomware file in order to create a learning database and another to do the test. Once these databases are ready, we will start the classification with the Wekatool. The aim of this work is to compare the old classification with the new one using the artificial neural networks and Bayesian networks.
250 Abstract-The basic principles of data mining is to analyze the data from different angle, categorize it and finally to summarize it. In today's world data mining have increasingly become very interesting and popular in terms of all application. The need for data mining is that we have too much data, too much technology but don't have useful information. Data mining software allows user to analyze data. This paper introduces the key principle of data pre-processing, classification, clustering and introduction of WEKA tool. Weka is a data mining tool. In this paper we are describing the steps of how to use WEKA tool for these technologies. It provides the facility to classify the data through various algorithms.
We present the Malware - O - Matic analysis platform and the Data Aware Defense ransomware countermeasure based on real time data gathering with as little impact as possible on system performance. Our solution monitors (and blocks if necessary) file system activity of all userland threads with new indicators of compromise. We successfully detect 99.37% of our 798 active ransomware samples with at most 70 MB lost per sample’s thread in 90% of cases, or less than 7 MB in 70% of cases. By a careful analysis of the few false negatives we show that some ransomware authors are specifically trying to hide ongoing encryption. We used free (as in free beer) de facto industry standard benchmarks to evaluate the impact of our solution and enable fair comparisons. In all but the most demanding tests the impact is marginal.
TorrentLocker is a ransomware that encrypts sensitive data located on infected computer systems. Its creators aim to ransom the victims, if they want to retrieve their data. Unfortunately, antiviruses have difficulties to detect such polymorphic malware. In this paper, we propose a novel approach to detect online suspicious processes accessing a large number of files and encrypting them. Such a behavior corresponds to the classical scenario of a malicious ransomware. We show that the Kullback-Liebler divergence can be used to detect with high effectiveness whether a process transforms structured input files (such as JPEG files) into unstructured encrypted files, or not. We focus mainly on JPEG files since irreplaceable pictures represent in many cases the most valuable data on personal computers or smartphones.
Labeling a malicious executable as a variant of a known family is important for security applications such as triage, lineage, and for building reference datasets in turn used for evaluating malware clustering and training malware classification approaches. Oftentimes, such labeling is based on labels output by antivirus engines. While AV labels are well-known to be inconsistent, there is often no other information available for labeling, thus security analysts keep relying on them. However, current approaches for extracting family information from AV labels are manual and inaccurate. In this work, we describe AVclass, an automatic labeling tool that given the AV labels for a, potentially massive, number of samples outputs the most likely family names for each sample. AVclass implements novel automatic techniques to address 3 key challenges: normalization, removal of generic tokens, and alias detection. We have evaluated AVclass on 10 datasets comprising 8.9 M samples, larger than any dataset used by malware clustering and classification works. AVclass leverages labels from any AV engine, e.g., all 99 AV engines seen in VirusTotal, the largest engine set in the literature. AVclass’s clustering achieves F1 measures up to 93.9 on labeled datasets and clusters are labeled with fine-grained family names commonly used by the AV vendors. We release AVclass to the community.
Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data and it is only possible to recover it once a ransom has been paid. In this paper we show how Software-Defined Networking (SDN) can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware - CryptoWall - and, based on this knowledge, we propose two real-time mitigation methods. Then we designed the SDN-based system, implemented using OpenFlow, which facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.
In this paper we introduce a deep neural network based malware detection system that Invincea has developed, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. We show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. In addition, we describe a non-parametric method for adjusting the classifier’s scores to better represent expected precision in the deployment environment. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive expert rule based malware detection, while also detecting previously unseen malware missed by these traditional approaches. Since machine learning models tend to improve with larger datasizes, we foresee deep neural network classification models gaining in importance as part of a layered network defense strategy in coming years.
Département de génié electrique et de génie informatique Université Laval 10 septembre 2004 Le perceptron multicouche est un réseau orienté de neurones artificiels organisé en couches etò u l'information voyage dans un seul sens, de la couche d'entrée vers la couche de sor-tie. La figure 1 donne l'exemple d'un réseau contenant une couche d'entrée, deux couches cachées et une couche de sortie. La couche d'entrée représente toujours une couche virtuelle associée aux entrées dusys eme. Elle ne contient aucun neurone. Les couches suivantes sont des couches de neurones. Dans l'exemple illustré, il y a 3 entrées, 4 neurones sur lapremì ere couche cachée, trois neurones sur ladeuxì eme et quatre neurones sur la couche de sortie. Les sorties des neurones de ladernì ere couche correspondent toujours aux sorties dusys eme. Dans le cas général, un perceptron multicouche peut posséder un nombre de couches quel-conque et un nombre de neurones (ou d'entrées) par couché egalement quelconque. Les neurones sont reliés entre eux par des connexions pondérées. Ce sont les poids de ces connexions qui gouvernent le fonctionnement du réseau et "programment" une appli-cation de l'espace des entrées vers l'espace des sortie a l'aide d'une transformation non linéaire. La création d'un perceptron multicouche pour résoudre unprobì eme donné passe donc par l'inférence de la meilleure application possible telle que définie par un ensemble de données d'apprentissage constituées de paires de vecteurs d'entrées et de sorties désirées. Cette inférence peut se faire, entre autre, par l'algorithme dit de rétropropagation.
Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.