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2019-06-10
Tran, T. K., Sato, H., Kubo, M..  2018.  One-Shot Learning Approach for Unknown Malware Classification. 2018 5th Asian Conference on Defense Technology (ACDT). :8-13.
Early detection of new kinds of malware always plays an important role in defending the network systems. Especially, if intelligent protection systems could themselves detect an existence of new malware types in their system, even with a very small number of malware samples, it must be a huge benefit for the organization as well as the social since it help preventing the spreading of that kind of malware. To deal with learning from few samples, term ``one-shot learning'' or ``fewshot learning'' was introduced, and mostly used in computer vision to recognize images, handwriting, etc. An approach introduced in this paper takes advantage of One-shot learning algorithms in solving the malware classification problem by using Memory Augmented Neural Network in combination with malware's API calls sequence, which is a very valuable source of information for identifying malware behavior. In addition, it also use some advantages of the development in Natural Language Processing field such as word2vec, etc. to convert those API sequences to numeric vectors before feeding to the one-shot learning network. The results confirm very good accuracies compared to the other traditional methods.
Kargaard, J., Drange, T., Kor, A., Twafik, H., Butterfield, E..  2018.  Defending IT Systems against Intelligent Malware. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). :411-417.
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.
Roseline, S. A., Geetha, S..  2018.  Intelligent Malware Detection Using Oblique Random Forest Paradigm. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :330-336.
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.
Udayakumar, N., Saglani, V. J., Cupta, A. V., Subbulakshmi, T..  2018.  Malware Classification Using Machine Learning Algorithms. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). :1-9.
Lately, we are facing the Malware crisis due to various types of malware or malicious programs or scripts available in the huge virtual world - the Internet. But, what is malware? Malware can be a malicious software or a program or a script which can be harmful to the user's computer. These malicious programs can perform a variety of functions, including stealing, encrypting or deleting sensitive data, altering or hijacking core computing functions and monitoring users' computer activity without their permission. There are various entry points for these programs and scripts in the user environment, but only one way to remove them is to find them and kick them out of the system which isn't an easy job as these small piece of script or code can be anywhere in the user system. This paper involves the understanding of different types of malware and how we will use Machine Learning to detect these malwares.
Jiang, J., Yin, Q., Shi, Z., Li, M..  2018.  Comprehensive Behavior Profiling Model for Malware Classification. 2018 IEEE Symposium on Computers and Communications (ISCC). :00129-00135.
In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.
Stokes, J. W., Wang, D., Marinescu, M., Marino, M., Bussone, B..  2018.  Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Detection Models. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :1-8.
Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial learning-based attacks, or adversarial attacks, where miscreants can avoid detection by the classification algorithm with very few perturbations of the input data. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defenses, the number of successfully crafted adversarial samples is reduced compared to an unprotected baseline system. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we show that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks.
Kim, C. H., Kabanga, E. K., Kang, S..  2018.  Classifying Malware Using Convolutional Gated Neural Network. 2018 20th International Conference on Advanced Communication Technology (ICACT). :40-44.
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.
Kalash, M., Rochan, M., Mohammed, N., Bruce, N. D. B., Wang, Y., Iqbal, F..  2018.  Malware Classification with Deep Convolutional Neural Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1-5.
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.
Kornish, D., Geary, J., Sansing, V., Ezekiel, S., Pearlstein, L., Njilla, L..  2018.  Malware Classification Using Deep Convolutional Neural Networks. 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). :1-6.
In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse. Converting malware samples into images can cause these patterns to manifest as image features, which can be exploited for DCNN classification. Techniques for converting malware binaries into images for visualization and classification have been reported in the literature, and while these methods do reach a high level of classification accuracy on training datasets, they tend to be vulnerable to overfitting and perform poorly on previously unseen samples. In this paper, we explore and document a variety of techniques for representing malware binaries as images with the goal of discovering a format best suited for deep learning. We implement a database for malware binaries from several families, stored in hexadecimal format. These malware samples are converted into images using various approaches and are used to train a neural network to recognize visual patterns in the input and classify malware based on the feature vectors. Each image type is assessed using a variety of learning models, such as transfer learning with existing DCNN architectures and feature extraction for support vector machine classifier training. Each technique is evaluated in terms of classification accuracy, result consistency, and time per trial. Our preliminary results indicate that improved image representation has the potential to enable more effective classification of new malware.
Alsulami, B., Mancoridis, S..  2018.  Behavioral Malware Classification Using Convolutional Recurrent Neural Networks. 2018 13th International Conference on Malicious and Unwanted Software (MALWARE). :103-111.
Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification aims to go beyond the detection of malware by also identifying a malware's family according to a naming scheme such as the ones used by anti-virus vendors. Behavioral malware classification techniques use run-time features, such as file system or network activities, to capture the behavioral characteristic of running processes. The increasing volume of malware samples, diversity of malware families, and the variety of naming schemes given to malware samples by anti-virus vendors present challenges to behavioral malware classifiers. We describe a behavioral classifier that uses a Convolutional Recurrent Neural Network and data from Microsoft Windows Prefetch files. We demonstrate the model's improvement on the state-of-the-art using a large dataset of malware families and four major anti-virus vendor naming schemes. The model is effective in classifying malware samples that belong to common and rare malware families and can incrementally accommodate the introduction of new malware samples and families.
Kim, H. M., Song, H. M., Seo, J. W., Kim, H. K..  2018.  Andro-Simnet: Android Malware Family Classification Using Social Network Analysis. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1-8.
While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.
Debatty, T., Mees, W., Gilon, T..  2018.  Graph-Based APT Detection. 2018 International Conference on Military Communications and Information Systems (ICMCIS). :1-8.
In this paper we propose a new algorithm to detect Advanced Persistent Threats (APT's) that relies on a graph model of HTTP traffic. We also implement a complete detection system with a web interface that allows to interactively analyze the data. We perform a complete parameter study and experimental evaluation using data collected on a real network. The results show that the performance of our system is comparable to currently available antiviruses, although antiviruses use signatures to detect known malwares while our algorithm solely uses behavior analysis to detect new undocumented attacks.
Xue, S., Zhang, L., Li, A., Li, X., Ruan, C., Huang, W..  2018.  AppDNA: App Behavior Profiling via Graph-Based Deep Learning. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1475-1483.
Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.
Jiang, H., Turki, T., Wang, J. T. L..  2018.  DLGraph: Malware Detection Using Deep Learning and Graph Embedding. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). :1029-1033.
In this paper we present a new approach, named DLGraph, for malware detection using deep learning and graph embedding. DLGraph employs two stacked denoising autoencoders (SDAs) for representation learning, taking into consideration computer programs' function-call graphs and Windows application programming interface (API) calls. Given a program, we first use a graph embedding technique that maps the program's function-call graph to a vector in a low-dimensional feature space. One SDA in our deep learning model is used to learn a latent representation of the embedded vector of the function-call graph. The other SDA in our model is used to learn a latent representation of the given program's Windows API calls. The two learned latent representations are then merged to form a combined feature vector. Finally, we use softmax regression to classify the combined feature vector for predicting whether the given program is malware or not. Experimental results based on different datasets demonstrate the effectiveness of the proposed approach and its superiority over a related method.
Jain, D., Khemani, S., Prasad, G..  2018.  Identification of Distributed Malware. 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS). :242-246.
Smartphones have evolved over the years from simple devices to communicate with each other to fully functional portable computers although with comparatively less computational power but inholding multiple applications within. With the smartphone revolution, the value of personal data has increased. As technological complexities increase, so do the vulnerabilities in the system. Smartphones are the latest target for attacks. Android being an open source platform and also the most widely used smartphone OS draws the attention of many malware writers to exploit the vulnerabilities of it. Attackers try to take advantage of these vulnerabilities and fool the user and misuse their data. Malwares have come a long way from simple worms to sophisticated DDOS using Botnets, the latest trends in computer malware tend to go in the distributed direction, to evade the multiple anti-virus apps developed to counter generic viruses and Trojans. However, the recent trend in android system is to have a combination of applications which acts as malware. The applications are benign individually but when grouped, these may result into a malicious activity. This paper proposes a new category of distributed malware in android system, how it can be used to evade the current security, and how it can be detected with the help of graph matching algorithm.
Luo, Chen, Chen, Zhengzhang, Tang, Lu-An, Shrivastava, Anshumali, Li, Zhichun, Chen, Haifeng, Ye, Jieping.  2018.  TINET: Learning Invariant Networks via Knowledge Transfer. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :1890-1899.
The latent behavior of an information system that can exhibit extreme events, such as system faults or cyber-attacks, is complex. Recently, the invariant network has shown to be a powerful way of characterizing complex system behaviors. Structures and evolutions of the invariance network, in particular, the vanishing correlations, can shed light on identifying causal anomalies and performing system diagnosis. However, due to the dynamic and complex nature of real-world information systems, learning a reliable invariant network in a new environment often requires continuous collecting and analyzing the system surveillance data for several weeks or even months. Although the invariant networks learned from old environments have some common entities and entity relationships, these networks cannot be directly borrowed for the new environment due to the domain variety problem. To avoid the prohibitive time and resource consuming network building process, we propose TINET, a knowledge transfer based model for accelerating invariant network construction. In particular, we first propose an entity estimation model to estimate the probability of each source domain entity that can be included in the final invariant network of the target domain. Then, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of TINET. We also apply TINET to a real enterprise security system for intrusion detection. TINET achieves superior detection performance at least 20 days lead-lag time in advance with more than 75% accuracy.
Cao, Cheng, Chen, Zhengzhang, Caverlee, James, Tang, Lu-An, Luo, Chen, Li, Zhichun.  2018.  Behavior-Based Community Detection: Application to Host Assessment In Enterprise Information Networks. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. :1977-1985.
Community detection in complex networks is a fundamental problem that attracts much attention across various disciplines. Previous studies have been mostly focusing on external connections between nodes (i.e., topology structure) in the network whereas largely ignoring internal intricacies (i.e., local behavior) of each node. A pair of nodes without any interaction can still share similar internal behaviors. For example, in an enterprise information network, compromised computers controlled by the same intruder often demonstrate similar abnormal behaviors even if they do not connect with each other. In this paper, we study the problem of community detection in enterprise information networks, where large-scale internal events and external events coexist on each host. The discovered host communities, capturing behavioral affinity, can benefit many comparative analysis tasks such as host anomaly assessment. In particular, we propose a novel community detection framework to identify behavior-based host communities in enterprise information networks, purely based on large-scale heterogeneous event data. We continue proposing an efficient method for assessing host's anomaly level by leveraging the detected host communities. Experimental results on enterprise networks demonstrate the effectiveness of our model.
Su, Fang-Hsiang, Bell, Jonathan, Kaiser, Gail, Ray, Baishakhi.  2018.  Obfuscation Resilient Search Through Executable Classification. Proceedings of the 2Nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages. :20-30.
Android applications are usually obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations. Obfuscators might hide the true intent of code by renaming variables and/or modifying program structures. It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently. Prior approaches toward obfuscation resilient search have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relationships between identifiers are not broken by obfuscators; others have assumed that control flow graphs maintain their structures. Both approaches can be easily defeated by a motivated obfuscator. We present a new approach, MACNETO, to search for programs relevant to obfuscated executables leveraging deep learning and principal components on instructions. MACNETO makes few assumptions about the kinds of modifications that an obfuscator might perform. We show that it has high search precision for executables obfuscated by a state-of-the-art obfuscator that changes control flow. Further, we also demonstrate the potential of MACNETO to help developers understand executables, where MACNETO infers keywords (which are from relevant un-obfuscated programs) for obfuscated executables.
Jo, Saehan, Yoo, Jaemin, Kang, U.  2018.  Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. :297-305.
Given graphs with millions or billions of vertices and edges, how can we efficiently make inferences based on partial knowledge? Loopy Belief Propagation(LBP) is a graph inference algorithm widely used in various applications including social network analysis, malware detection, recommendation, and image restoration. The algorithm calculates approximate marginal probabilities of vertices in a graph within a linear running time proportional to the number of edges. However, when it comes to real-world graphs with millions or billions of vertices and edges, this cost overwhelms the computing power of a single machine. Moreover, this kind of large-scale graphs does not fit into the memory of a single machine. Although several distributed LBP methods have been proposed, previous works do not consider the properties of real-world graphs, especially the effect of power-law degree distribution on LBP. Therefore, our work focuses on developing a fast and scalable LBP for such large real-world graphs on distributed environment. In this paper, we propose DLBP, a Distributed Loopy Belief Propagation algorithm which efficiently computes LBP in a distributed manner across multiple machines. By setting the correct convergence criterion and carefully scheduling the computations, DLBP provides up to 10.7x speed up compared to standard distributed LBP. We show that DLBP demonstrates near-linear scalability with respect to the number of machines as well as the number of edges.
Mpanti, Anna, Nikolopoulos, Stavros D., Polenakis, Iosif.  2018.  A Graph-Based Model for Malicious Software Detection Exploiting Domination Relations Between System-Call Groups. Proceedings of the 19th International Conference on Computer Systems and Technologies. :20-26.
In this paper, we propose a graph-based algorithmic technique for malware detection, utilizing the System-call Dependency Graphs (ScDG) obtained through taint analysis traces. We leverage the grouping of system-calls into system-call groups with respect to their functionality to merge disjoint vertices of ScDG graphs, transforming them to Group Relation Graphs (GrG); note that, the GrG graphs represent malware's behavior being hence more resilient to probable mutations of its structure. More precisely, we extend the use of GrG graphs by mapping their vertices on the plane utilizing the degrees and the vertex-weights of a specific underlying graph of the GrG graph as to compute domination relations. Furthermore, we investigate how the activity of each system-call group could be utilized in order to distinguish graph-representations of malware and benign software. The domination relations among the vertices of GrG graphs result to a new graph representation that we call Coverage Graph of the GrG graph. Finally, we evaluate the potentials of our detection model using graph similarity between Coverage Graphs of known malicious and benign software samples of various types.
Karbab, ElMouatez Billah, Debbabi, Mourad.  2018.  ToGather: Automatic Investigation of Android Malware Cyber-Infrastructures. Proceedings of the 13th International Conference on Availability, Reliability and Security. :20:1-20:10.
The popularity of Android, not only in handsets but also in IoT devices, makes it a very attractive target for malware threats, which are actually expanding at a significant rate. The state-of-the-art in malware mitigation solutions mainly focuses on the detection of malicious Android apps using dynamic and static analysis features to segregate malicious apps from benign ones. Nevertheless, there is a small coverage for the Internet/network dimension of Android malicious apps. In this paper, we present ToGather, an automatic investigation framework that takes Android malware samples as input and produces insights about the underlying malicious cyber infrastructures. ToGather leverages state-of-the-art graph theory techniques to generate actionable, relevant and granular intelligence to mitigate the threat effects induced by the malicious Internet activity of Android malware apps. We evaluate ToGather on a large dataset of real malware samples from various Android families, and the obtained results are both interesting and promising.
Nathezhtha, T., Yaidehi, V..  2018.  Cloud Insider Attack Detection Using Machine Learning. 2018 International Conference on Recent Trends in Advance Computing (ICRTAC). :60-65.
Security has always been a major issue in cloud. Data sources are the most valuable and vulnerable information which is aimed by attackers to steal. If data is lost, then the privacy and security of every cloud user are compromised. Even though a cloud network is secured externally, the threat of an internal attacker exists. Internal attackers compromise a vulnerable user node and get access to a system. They are connected to the cloud network internally and launch attacks pretending to be trusted users. Machine learning approaches are widely used for cloud security issues. The existing machine learning based security approaches classify a node as a misbehaving node based on short-term behavioral data. These systems do not differentiate whether a misbehaving node is a malicious node or a broken node. To address this problem, this paper proposes an Improvised Long Short-Term Memory (ILSTM) model which learns the behavior of a user and automatically trains itself and stores the behavioral data. The model can easily classify the user behavior as normal or abnormal. The proposed ILSTM not only identifies an anomaly node but also finds whether a misbehaving node is a broken node or a new user node or a compromised node using the calculated trust factor. The proposed model not only detects the attack accurately but also reduces the false alarm in the cloud network.
Sokolov, A. N., Pyatnitsky, I. A., Alabugin, S. K..  2018.  Research of Classical Machine Learning Methods and Deep Learning Models Effectiveness in Detecting Anomalies of Industrial Control System. 2018 Global Smart Industry Conference (GloSIC). :1-6.
Modern industrial control systems (ICS) act as victims of cyber attacks more often in last years. These attacks are hard to detect and their consequences can be catastrophic. Cyber attacks can cause anomalies in the work of the ICS and its technological equipment. The presence of mutual interference and noises in this equipment significantly complicates anomaly detection. Moreover, the traditional means of protection, which used in corporate solutions, require updating with each change in the structure of the industrial process. An approach based on the machine learning for anomaly detection was used to overcome these problems. It complements traditional methods and allows one to detect signal correlations and use them for anomaly detection. Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation dataset was analyzed as example of industrial process. In the course of the research, correlations between the signals of the sensors were detected and preliminary data processing was carried out. Algorithms from the most common techniques of machine learning (decision trees, linear algorithms, support vector machines) and deep learning models (neural networks) were investigated for industrial process anomaly detection task. It's shown that linear algorithms are least demanding on computational resources, but they don't achieve an acceptable result and allow a significant number of errors. Decision tree-based algorithms provided an acceptable accuracy, but the amount of RAM, required for their operations, relates polynomially with the training sample volume. The deep neural networks provided the greatest accuracy, but they require considerable computing power for internal calculations.
Farooq, H. M., Otaibi, N. M..  2018.  Optimal Machine Learning Algorithms for Cyber Threat Detection. 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim). :32-37.
With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.
Clemente, C. J., Jaafar, F., Malik, Y..  2018.  Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms? 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). :95-102.
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.