Visible to the public Biblio

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2019-07-01
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.

Ferreyra, N. E. Díaz, Meisy, R., Heiselz, M..  2018.  At Your Own Risk: Shaping Privacy Heuristics for Online Self-Disclosure. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1-10.

Revealing private and sensitive information on Social Network Sites (SNSs) like Facebook is a common practice which sometimes results in unwanted incidents for the users. One approach for helping users to avoid regrettable scenarios is through awareness mechanisms which inform a priori about the potential privacy risks of a self-disclosure act. Privacy heuristics are instruments which describe recurrent regrettable scenarios and can support the generation of privacy awareness. One important component of a heuristic is the group of people who should not access specific private information under a certain privacy risk. However, specifying an exhaustive list of unwanted recipients for a given regrettable scenario can be a tedious task which necessarily demands the user's intervention. In this paper, we introduce an approach based on decision trees to instantiate the audience component of privacy heuristics with minor intervention from the users. We introduce Disclosure- Acceptance Trees, a data structure representative of the audience component of a heuristic and describe a method for their generation out of user-centred privacy preferences.

2019-06-10
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.

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.

Eziama, E., Jaimes, L. M. S., James, A., Nwizege, K. S., Balador, A., Tepe, K..  2018.  Machine Learning-Based Recommendation Trust Model for Machine-to-Machine Communication. 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1-6.

The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.

2019-05-01
Konstantelos, I., Jamgotchian, G., Tindemans, S., Duchesne, P., Cole, S., Merckx, C., Strbac, G., Panciatici, P..  2018.  Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids. 2018 IEEE Power Energy Society General Meeting (PESGM). :1–1.

This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages High Performance Computing (HPC) to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform's workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10,000 parallel cores) is analyzed, including task timings and data flows. Finally, the generated decision trees are compared with test data to quantify the functional performance of the DSA platform.

2019-03-22
Teoh, T. T., Chiew, G., Franco, E. J., Ng, P. C., Benjamin, M. P., Goh, Y. J..  2018.  Anomaly Detection in Cyber Security Attacks on Networks Using MLP Deep Learning. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). :1-5.

Malicious traffic has garnered more attention in recent years, owing to the rapid growth of information technology in today's world. In 2007 alone, an estimated loss of 13 billion dollars was made from malware attacks. Malware data in today's context is massive. To understand such information using primitive methods would be a tedious task. In this publication we demonstrate some of the most advanced deep learning techniques available, multilayer perceptron (MLP) and J48 (also known as C4.5 or ID3) on our selected dataset, Advanced Security Network Metrics & Non-Payload-Based Obfuscations (ASNM-NPBO) to show that the answer to managing cyber security threats lie in the fore-mentioned methodologies.

Terzi, D. S., Arslan, B., Sagiroglu, S..  2018.  Smart Grid Security Evaluation with a Big Data Use Case. 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018). :1-6.

Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.

2019-03-06
Hess, S., Satam, P., Ditzler, G., Hariri, S..  2018.  Malicious HTML File Prediction: A Detection and Classification Perspective with Noisy Data. 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA). :1-7.

Cybersecurity plays a critical role in protecting sensitive information and the structural integrity of networked systems. As networked systems continue to expand in numbers as well as in complexity, so does the threat of malicious activity and the necessity for advanced cybersecurity solutions. Furthermore, both the quantity and quality of available data on malicious content as well as the fact that malicious activity continuously evolves makes automated protection systems for this type of environment particularly challenging. Not only is the data quality a concern, but the volume of the data can be quite small for some of the classes. This creates a class imbalance in the data used to train a classifier; however, many classifiers are not well equipped to deal with class imbalance. One such example is detecting malicious HMTL files from static features. Unfortunately, collecting malicious HMTL files is extremely difficult and can be quite noisy from HTML files being mislabeled. This paper evaluates a specific application that is afflicted by these modern cybersecurity challenges: detection of malicious HTML files. Previous work presented a general framework for malicious HTML file classification that we modify in this work to use a $\chi$2 feature selection technique and synthetic minority oversampling technique (SMOTE). We experiment with different classifiers (i.e., AdaBoost, Gentle-Boost, RobustBoost, RusBoost, and Random Forest) and a pure detection model (i.e., Isolation Forest). We benchmark the different classifiers using SMOTE on a real dataset that contains a limited number of malicious files (40) with respect to the normal files (7,263). It was found that the modified framework performed better than the previous framework's results. However, additional evidence was found to imply that algorithms which train on both the normal and malicious samples are likely overtraining to the malicious distribution. We demonstrate the likely overtraining by determining that a subset of the malicious files, while suspicious, did not come from a malicious source.

2018-12-10
Lee, J., Hao, Y., Abdelzaher, T., Marcus, K., Hobbs, R..  2018.  A Command-by-Intent Architecture for Battlefield Information Acquisition Systems. 2018 21st International Conference on Information Fusion (FUSION). :2298–2305.

In military operations, Commander's Intent describes the desired end state and purpose of the operation, expressed in a concise and clear manner. Command by intent is a paradigm that empowers subordinate units to exercise measured initiative to meet mission goals and accept prudent risk within commander's intent. It improves agility of military operations by allowing exploitation of local opportunities without an explicit directive from the commander to do so. This paper discusses what the paradigm entails in terms of architectural decisions for data fusion systems tasked with real-time information collection to satisfy operational mission goals. In our system, information needs of decisions are expressed at a high level, and shared among relevant nodes. The selected nodes, then, jointly operate to meet mission information needs by forwarding and caching relevant data without explicit directives regarding the objects to fetch and sources to contact. A preliminary evaluation of the system is presented using a target tracking application, set in the context of a NATO-based mission scenario, called Anglova. Evaluation results show that delegating some decision authority to the data fusion system (in terms of objects to fetch and sources to contact) allows it to save more network resources, while also increasing mission success rate. The system is therefore particularly well-suited to operation in partially denied or contested environments, where resource bottlenecks caused by adversarial activity impair one's ability to collect real-time information for mission-critical decision making.

2018-09-28
Li, Z., Li, S..  2017.  Random forest algorithm under differential privacy. 2017 IEEE 17th International Conference on Communication Technology (ICCT). :1901–1905.

Trying to solve the risk of data privacy disclosure in classification process, a Random Forest algorithm under differential privacy named DPRF-gini is proposed in the paper. In the process of building decision tree, the algorithm first disturbed the process of feature selection and attribute partition by using exponential mechanism, and then meet the requirement of differential privacy by adding Laplace noise to the leaf node. Compared with the original algorithm, Empirical results show that protection of data privacy is further enhanced while the accuracy of the algorithm is slightly reduced.

2018-07-18
Vávra, J., Hromada, M..  2017.  Anomaly Detection System Based on Classifier Fusion in ICS Environment. 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). :32–38.

The detection of cyber-attacks has become a crucial task for highly sophisticated systems like industrial control systems (ICS). These systems are an essential part of critical information infrastructure. Therefore, we can highlight their vital role in contemporary society. The effective and reliable ICS cyber defense is a significant challenge for the cyber security community. Thus, intrusion detection is one of the demanding tasks for the cyber security researchers. In this article, we examine classification problem. The proposed detection system is based on supervised anomaly detection techniques. Moreover, we utilized classifiers algorithms in order to increase intrusion detection capabilities. The fusion of the classifiers is the way how to achieve the predefined goal.

2018-06-20
Hassen, M., Carvalho, M. M., Chan, P. K..  2017.  Malware classification using static analysis based features. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). :1–7.

Anti-virus vendors receive hundreds of thousands of malware to be analysed each day. Some are new malware while others are variations or evolutions of existing malware. Because analyzing each malware sample by hand is impossible, automated techniques to analyse and categorize incoming samples are needed. In this work, we explore various machine learning features extracted from malware samples through static analysis for classification of malware binaries into already known malware families. We present a new feature based on control statement shingling that has a comparable accuracy to ordinary opcode n-gram based features while requiring smaller dimensions. This, in turn, results in a shorter training time.

2018-06-07
Appelt, D., Panichella, A., Briand, L..  2017.  Automatically Repairing Web Application Firewalls Based on Successful SQL Injection Attacks. 2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE). :339–350.

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).

2018-05-30
Wen, M., Zhang, X., Li, H., Li, J..  2017.  A Data Aggregation Scheme with Fine-Grained Access Control for the Smart Grid. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). :1–5.

With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine- grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.

2018-04-04
Zekri, M., Kafhali, S. E., Aboutabit, N., Saadi, Y..  2017.  DDoS attack detection using machine learning techniques in cloud computing environments. 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). :1–7.

Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.

2018-04-02
Wang, Y., Pulgar-Painemal, H., Sun, K..  2017.  Online Analysis of Voltage Security in a Microgrid Using Convolutional Neural Networks. 2017 IEEE Power Energy Society General Meeting. :1–5.

Although connecting a microgrid to modern power systems can alleviate issues arising from a large penetration of distributed generation, it can also cause severe voltage instability problems. This paper presents an online method to analyze voltage security in a microgrid using convolutional neural networks. To transform the traditional voltage stability problem into a classification problem, three steps are considered: 1) creating data sets using offline simulation results; 2) training the model with dimensional reduction and convolutional neural networks; 3) testing the online data set and evaluating performance. A case study in the modified IEEE 14-bus system shows the accuracy of the proposed analysis method increases by 6% compared to back-propagation neural network and has better performance than decision tree and support vector machine. The proposed algorithm has great potential in future applications.

2018-02-21
Pak, W., Choi, Y. J..  2017.  High Performance and High Scalable Packet Classification Algorithm for Network Security Systems. IEEE Transactions on Dependable and Secure Computing. 14:37–49.

Packet classification is a core function in network and security systems; hence, hardware-based solutions, such as packet classification accelerator chips or Ternary Content Addressable Memory (T-CAM), have been widely adopted for high-performance systems. With the rapid improvement of general hardware architectures and growing popularity of multi-core multi-threaded processors, software-based packet classification algorithms are attracting considerable attention, owing to their high flexibility in satisfying various industrial requirements for security and network systems. For high classification speed, these algorithms internally use large tables, whose size increases exponentially with the ruleset size; consequently, they cannot be used with a large rulesets. To overcome this problem, we propose a new software-based packet classification algorithm that simultaneously supports high scalability and fast classification performance by merging partition decision trees in a search table. While most partitioning-based packet classification algorithms show good scalability at the cost of low classification speed, our algorithm shows very high classification speed, irrespective of the number of rules, with small tables and short table building time. Our test results confirm that the proposed algorithm enables network and security systems to support heavy traffic in the most effective manner.

2018-02-06
Scheitle, Q., Gasser, O., Rouhi, M., Carle, G..  2017.  Large-Scale Classification of IPv6-IPv4 Siblings with Variable Clock Skew. 2017 Network Traffic Measurement and Analysis Conference (TMA). :1–9.

Linking the growing IPv6 deployment to existing IPv4 addresses is an interesting field of research, be it for network forensics, structural analysis, or reconnaissance. In this work, we focus on classifying pairs of server IPv6 and IPv4 addresses as siblings, i.e., running on the same machine. Our methodology leverages active measurements of TCP timestamps and other network characteristics, which we measure against a diverse ground truth of 682 hosts. We define and extract a set of features, including estimation of variable (opposed to constant) remote clock skew. On these features, we train a manually crafted algorithm as well as a machine-learned decision tree. By conducting several measurement runs and training in cross-validation rounds, we aim to create models that generalize well and do not overfit our training data. We find both models to exceed 99% precision in train and test performance. We validate scalability by classifying 149k siblings in a large-scale measurement of 371k sibling candidates. We argue that this methodology, thoroughly cross-validated and likely to generalize well, can aid comparative studies of IPv6 and IPv4 behavior in the Internet. Striving for applicability and replicability, we release ready-to-use source code and raw data from our study.

2018-01-16
Miramirkhani, N., Appini, M. P., Nikiforakis, N., Polychronakis, M..  2017.  Spotless Sandboxes: Evading Malware Analysis Systems Using Wear-and-Tear Artifacts. 2017 IEEE Symposium on Security and Privacy (SP). :1009–1024.

Malware sandboxes, widely used by antivirus companies, mobile application marketplaces, threat detection appliances, and security researchers, face the challenge of environment-aware malware that alters its behavior once it detects that it is being executed on an analysis environment. Recent efforts attempt to deal with this problem mostly by ensuring that well-known properties of analysis environments are replaced with realistic values, and that any instrumentation artifacts remain hidden. For sandboxes implemented using virtual machines, this can be achieved by scrubbing vendor-specific drivers, processes, BIOS versions, and other VM-revealing indicators, while more sophisticated sandboxes move away from emulation-based and virtualization-based systems towards bare-metal hosts. We observe that as the fidelity and transparency of dynamic malware analysis systems improves, malware authors can resort to other system characteristics that are indicative of artificial environments. We present a novel class of sandbox evasion techniques that exploit the "wear and tear" that inevitably occurs on real systems as a result of normal use. By moving beyond how realistic a system looks like, to how realistic its past use looks like, malware can effectively evade even sandboxes that do not expose any instrumentation indicators, including bare-metal systems. We investigate the feasibility of this evasion strategy by conducting a large-scale study of wear-and-tear artifacts collected from real user devices and publicly available malware analysis services. The results of our evaluation are alarming: using simple decision trees derived from the analyzed data, malware can determine that a system is an artificial environment and not a real user device with an accuracy of 92.86%. As a step towards defending against wear-and-tear malware evasion, we develop statistical models that capture a system's age and degree of use, which can be used to aid sandbox operators in creating system i- ages that exhibit a realistic wear-and-tear state.

2017-12-28
Mehetrey, P., Shahriari, B., Moh, M..  2016.  Collaborative Ensemble-Learning Based Intrusion Detection Systems for Clouds. 2016 International Conference on Collaboration Technologies and Systems (CTS). :404–411.
Cloud computation has become prominent with seemingly unlimited amount of storage and computation available to users. Yet, security is a major issue that hampers the growth of cloud. In this research we investigate a collaborative Intrusion Detection System (IDS) based on the ensemble learning method. It uses weak classifiers, and allows the use of untapped resources of cloud to detect various types of attacks on the cloud system. In the proposed system, tasks are distributed among available virtual machines (VM), individual results are then merged for the final adaptation of the learning model. Performance evaluation is carried out using decision trees and using fuzzy classifiers, on KDD99, one of the largest datasets for IDS. Segmentation of the dataset is done in order to mimic the behavior of real-time data traffic occurred in a real cloud environment. The experimental results show that the proposed approach reduces the execution time with improved accuracy, and is fault-tolerant when handling VM failures. The system is a proof-of-concept model for a scalable, cloud-based distributed system that is able to explore untapped resources, and may be used as a base model for a real-time hierarchical IDS.
Vu, Q. H., Ruta, D., Cen, L..  2017.  An ensemble model with hierarchical decomposition and aggregation for highly scalable and robust classification. 2017 Federated Conference on Computer Science and Information Systems (FedCSIS). :149–152.

This paper introduces an ensemble model that solves the binary classification problem by incorporating the basic Logistic Regression with the two recent advanced paradigms: extreme gradient boosted decision trees (xgboost) and deep learning. To obtain the best result when integrating sub-models, we introduce a solution to split and select sets of features for the sub-model training. In addition to the ensemble model, we propose a flexible robust and highly scalable new scheme for building a composite classifier that tries to simultaneously implement multiple layers of model decomposition and outputs aggregation to maximally reduce both bias and variance (spread) components of classification errors. We demonstrate the power of our ensemble model to solve the problem of predicting the outcome of Hearthstone, a turn-based computer game, based on game state information. Excellent predictive performance of our model has been acknowledged by the second place scored in the final ranking among 188 competing teams.

2017-12-20
Abdelhamid, N., Thabtah, F., Abdel-jaber, H..  2017.  Phishing detection: A recent intelligent machine learning comparison based on models content and features. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :72–77.

In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing are needed. Machine learning (ML) is a popular tool for data analysis and recently has shown promising results in combating phishing when contrasted with classic anti-phishing approaches, including awareness workshops, visualization and legal solutions. This article investigates ML techniques applicability to detect phishing attacks and describes their pros and cons. In particular, different types of ML techniques have been investigated to reveal the suitable options that can serve as anti-phishing tools. More importantly, we experimentally compare large numbers of ML techniques on real phishing datasets and with respect to different metrics. The purpose of the comparison is to reveal the advantages and disadvantages of ML predictive models and to show their actual performance when it comes to phishing attacks. The experimental results show that Covering approach models are more appropriate as anti-phishing solutions, especially for novice users, because of their simple yet effective knowledge bases in addition to their good phishing detection rate.