Biblio
Phishing websites remain a persistent security threat. Thus far, machine learning approaches appear to have the best potential as defenses. But, there are two main concerns with existing machine learning approaches for phishing detection. The first is the large number of training features used and the lack of validating arguments for these feature choices. The second concern is the type of datasets used in the literature that are inadvertently biased with respect to the features based on the website URL or content. To address these concerns, we put forward the intuition that the domain name of phishing websites is the tell-tale sign of phishing and holds the key to successful phishing detection. Accordingly, we design features that model the relationships, visual as well as statistical, of the domain name to the key elements of a phishing website, which are used to snare the end-users. The main value of our feature design is that, to bypass detection, an attacker will find it very difficult to tamper with the visual content of the phishing website without arousing the suspicion of the end user. Our feature set ensures that there is minimal or no bias with respect to a dataset. Our learning model trains with only seven features and achieves a true positive rate of 98% and a classification accuracy of 97%, on sample dataset. Compared to the state-of-the-art work, our per data instance classification is 4 times faster for legitimate websites and 10 times faster for phishing websites. Importantly, we demonstrate the shortcomings of using features based on URLs as they are likely to be biased towards specific datasets. We show the robustness of our learning algorithm by testing on unknown live phishing URLs and achieve a high detection accuracy of \$99.7%\$.
Phishing is a security attack to acquire personal information like passwords, credit card details or other account details of a user by means of websites or emails. Phishing websites look similar to the legitimate ones which make it difficult for a layman to differentiate between them. As per the reports of Anti Phishing Working Group (APWG) published in December 2018, phishing against banking services and payment processor was high. Almost all the phishy URLs use HTTPS and use redirects to avoid getting detected. This paper presents a focused literature survey of methods available to detect phishing websites. A comparative study of the in-use anti-phishing tools was accomplished and their limitations were acknowledged. We analyzed the URL-based features used in the past to improve their definitions as per the current scenario which is our major contribution. Also, a step wise procedure of designing an anti-phishing model is discussed to construct an efficient framework which adds to our contribution. Observations made out of this study are stated along with recommendations on existing systems.
The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.
Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.
Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have enough data to train a reasonably accurate model. For such organizations, a realistic solution is to train machine learning models based on a joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player.
The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a security analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]
Being able to describe a specific network as consistent is a large step towards resiliency. Next to the importance of security lies the necessity of consistency verification. Attackers are currently focusing on targeting small and crutial goals such as network configurations or flow tables. These types of attacks would defy the whole purpose of a security system when built on top of an inconsistent network. Advances in Artificial Intelligence (AI) are playing a key role in ensuring a fast responce to the large number of evolving threats. Software Defined Networking (SDN), being centralized by design, offers a global overview of the network. Robustness and adaptability are part of a package offered by programmable networking, which drove us to consider the integration between both AI and SDN. The general goal of our series is to achieve an Artificial Intelligence Resiliency System (ARS). The aim of this paper is to propose a new AI-based consistency verification system, which will be part of ARS in our future work. The comparison of different deep learning architectures shows that Convolutional Neural Networks (CNN) give the best results with an accuracy of 99.39% on our dataset and 96% on our consistency test scenario.
Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.
A covert channel is a communication channel that is subjugated for illegal flow of information in a way that violates system security policies. It is a dangerous, invisible, undetectable, and developed security attack. Recently, Packet length covert channel has motivated many researchers as it is a one of the most undetectable network covert channels. Packet length covert channel generates a covert traffic that is very similar to normal terrific which complicates the detection of such type of covert channels. This motivates us to introduce a machine learning based detection scheme. Recently, a machine learning approach has proved its capability in many different fields especially in security field as it usually brings up a reliable and realistic results. Based in our developed content and frequency-based features, the developed detection scheme has been fully trained and tested. Our detection scheme has gained an excellent degree of detection accuracy which reaches 98% (zero false negative rate and 0.02 false positive rate).
In the light of the information revolution, and the propagation of big social data, the dissemination of misleading information is certainly difficult to control. This is due to the rapid and intensive flow of information through unconfirmed sources under the propaganda and tendentious rumors. This causes confusion, loss of trust between individuals and groups and even between governments and their citizens. This necessitates a consolidation of efforts to stop penetrating of false information through developing theoretical and practical methodologies aim to measure the credibility of users of these virtual platforms. This paper presents an approach to domain-based prediction to user's trustworthiness of Online Social Networks (OSNs). Through incorporating three machine learning algorithms, the experimental results verify the applicability of the proposed approach to classify and predict domain-based trustworthy users of OSNs.
Cross-site scripting (XSS) is a scripting attack targeting web applications by injecting malicious scripts into web pages. Blind XSS is a subset of stored XSS, where an attacker blindly deploys malicious payloads in web pages that are stored in a persistent manner on target servers. Most of the XSS detection techniques used to detect the XSS vulnerabilities are inadequate to detect blind XSS attacks. In this research, we present machine learning based approach to detect blind XSS attacks. Testing results help to identify malicious payloads that are likely to get stored in databases through web applications.
Telephone spam has become an increasingly prevalent problem in many countries all over the world. For example, the US Federal Trade Commission's (FTC) National Do Not Call Registry's number of cumulative complaints of spam/scam calls reached 30.9 million submissions in 2016. Naturally, telephone carriers can play an important role in the fight against spam. However, due to the extremely large volume of calls that transit across large carrier networks, it is challenging to mine their vast amounts of call detail records (CDRs) to accurately detect and block spam phone calls. This is because CDRs only contain high-level metadata (e.g., source and destination numbers, call start time, call duration, etc.) related to each phone calls. In addition, ground truth about both benign and spam-related phone numbers is often very scarce (only a tiny fraction of all phone numbers can be labeled). More importantly, telephone carriers are extremely sensitive to false positives, as they need to avoid blocking any non-spam calls, making the detection of spam-related numbers even more challenging. In this paper, we present a novel detection system that aims to discover telephone numbers involved in spam campaigns. Given a small seed of known spam phone numbers, our system uses a combination of unsupervised and supervised machine learning methods to mine new, previously unknown spam numbers from large datasets of call detail records (CDRs). Our objective is not to detect all possible spam phone calls crossing a carrier's network, but rather to expand the list of known spam numbers while aiming for zero false positives, so that the newly discovered numbers may be added to a phone blacklist, for example. To evaluate our system, we have conducted experiments over a large dataset of real-world CDRs provided by a leading telephony provider in China, while tuning the system to produce no false positives. The experimental results show that our system is able to greatly expand on the initial seed of known spam numbers by up to about 250%.
Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naïve bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.
Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.
Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.
Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.
In this special discussion session on machine learning, the panel members discuss various issues related to building secure and low power neuromorphic systems. The security of neuromorphic systems may be discussed in term of the reliability of the model, trust in the model, and security of the underlying hardware. The low power aspect of neuromorphic computing systems may be discussed in terms of adaptation of new devices and technologies, the adaptation of new computational models, development of heterogeneous computing frameworks, or dedicated engines for processing neuromorphic models. This session may include discussion on the design space of such supporting hardware, exploring tradeoffs between power/energy, security, scalability, hardware area, performance, and accuracy.
The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.
With the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.
Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.
In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.