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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Security of machine learning classifiers"

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (April 10, 2021): 22–28. http://dx.doi.org/10.35940/ijainn.b1025.041221.

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In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

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ALGorain, Fahad T., and John A. Clark. "Covering Arrays ML HPO for Static Malware Detection." Eng 4, no. 1 (February 9, 2023): 543–54. http://dx.doi.org/10.3390/eng4010032.

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Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection.

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Katzir, Ziv, and Yuval Elovici. "Quantifying the resilience of machine learning classifiers used for cyber security." Expert Systems with Applications 92 (February 2018): 419–29. http://dx.doi.org/10.1016/j.eswa.2017.09.053.

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Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

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One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.

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Mehanović, Dželila, and Jasmin Kevrić. "Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection." Traitement du Signal 37, no. 4 (October 10, 2020): 563–69. http://dx.doi.org/10.18280/ts.370403.

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Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.

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Deshmukh, Miss Maithili, and Dr M. A. Pund. "Implementation Paper on Network Data Verification Using Machine Learning Classifiers Based on Reduced Feature Dimensions." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2921–24. http://dx.doi.org/10.22214/ijraset.2022.41938.

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Abstract: With the rapid development of network-based applications, new risks arise and extra security mechanisms require additional attention to enhance speed and accuracy. Although many new security tools are developed, the rapid rise of malicious activity may be a major problem and therefore the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. a serious approach is machine learning methods for intrusion detection, where we learn models from data to differentiate between abnormal and normal traffic. Although machine learning methods are often used, there are some drawbacks to deep analysis of machine learning algorithms in terms of intrusion detection. during this work, we present a comprehensive analysis of some existing machine learning classifiers within the context of known intrusions into network traffic. Specifically, we analyze classification along different dimensions, that is, feature selection, sensitivity to hyper-parameter selection, and sophistication imbalance problems involved in intrusion detection. We evaluate several classifications using the NSL-KDD dataset and summarize their effectiveness using detailed experimental evaluation. Keywords: IDS, Machine Learning, Classification Algorithms, NSL-KDD Dataset, Network Intrusion Detection, Data Mining, Feature Selection, WEKA, Hyperparameters, Hyperparameter Optimization.

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Runwal, Akshat. "Anomaly based Intrusion Detection System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 255–60. http://dx.doi.org/10.22214/ijraset.2021.37955.

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Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on NSL- KDD dataset after extracting some of the relevant records and then several experiments have been performed and evaluated to assess various machine learning classifiers based on dataset. The implemented experiments demonstrated that the Random forest classifier has achieved the highest average accuracy and has outperformed the other models in various evaluations. Keywords: Intrusion Detection System, Anomaly Detection, Machine Learning, Random Forest, Network Security

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Abdulrezzak, Sarah, and Firas Sabir. "An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers." Journal of Engineering 29, no. 2 (February 1, 2023): 164–78. http://dx.doi.org/10.31026/j.eng.2023.02.11.

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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade the detection rates of current NIDSs, thorough analyses are essential to identify where ML predictors outperform them. The first step is to provide assessment of most used NIDS worldwide, Snort, and comparing its performance with ML classifiers. This paper provides an empirical study to evaluate performance of Snort and four supervised ML classifiers, KNN, Decision Tree, Bayesian net and Naïve Bays against network attacks, probing, Brute force and DoS. By measuring Snort metric, True Alarm Rate, F-measure, Precision and Accuracy and compares them with the same metrics conducted from applying ML algorithms using Weka tool. ML classifiers show an elevated performance with over 99% correctly classified instances for most algorithms, While Snort intrusion detection system shows a degraded classification of about 25% correctly classified instances, hence identifying Snort weaknesses towards certain attack types and giving leads on how to overcome those weaknesses. es.

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Singh, Ravi, and Virender Ranga. "Performance Evaluation of Machine Learning Classifiers on Internet of Things Security Dataset." International Journal of Control and Automation 11, no. 5 (May 31, 2018): 11–24. http://dx.doi.org/10.14257/ijca.2018.11.5.02.

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Дисертації з теми "Security of machine learning classifiers"

Lubenko, Ivans. "Towards robust steganalysis : binary classifiers and large, heterogeneous data." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c1ae44b8-94da-438d-b318-f038ad6aac57.

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The security of a steganography system is defined by our ability to detect it. It is of no surprise then that steganography and steganalysis both depend heavily on the accuracy and robustness of our detectors. This is especially true when real-world data is considered, due to its heterogeneity. The difficulty of such data manifests itself in a penalty that has periodically been reported to affect the performance of detectors built on binary classifiers; this is known as cover source mismatch. It remains unclear how the performance drop that is associated with cover source mismatch is mitigated or even measured. In this thesis we aim to show a robust methodology to empirically measure its effects on the detection accuracy of steganalysis classifiers. Some basic machine-learning based methods, which take their origin in domain adaptation, are proposed to counter it. Specifically, we test two hypotheses through an empirical investigation. First, that linear classifiers are more robust than non-linear classifiers to cover source mismatch in real-world data and, second, that linear classifiers are so robust that given sufficiently large mismatched training data they can equal the performance of any classifier trained on small matched data. With the help of theory we draw several nontrivial conclusions based on our results. The penalty from cover source mismatch may, in fact, be a combination of two types of error; estimation error and adaptation error. We show that relatedness between training and test data, as well as the choice of classifier, both have an impact on adaptation error, which, as we argue, ultimately defines a detector's robustness. This provides a novel framework for reasoning about what is required to improve the robustness of steganalysis detectors. Whilst our empirical results may be viewed as the first step towards this goal, we show that our approach provides clear advantages over earlier methods. To our knowledge this is the first study of this scale and structure.

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Nowroozi, Ehsan. "Machine Learning Techniques for Image Forensics in Adversarial Setting." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096177.

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The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multimedia forensics. The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the security of such techniques is then of primary importance, and, recently, has marked the birth of a new discipline, named Adversarial Machine Learning. By focusing on Image Forensics and image manipulation detection in particular, this thesis contributes to the above mission by developing novel techniques for enhancing the security of binary manipulation detectors based on machine learning in several adversarial scenarios. The validity of the proposed solutions has been assessed by considering several manipulation tasks, ranging from the detection of double compression and contrast adjustment, to the detection of geometric transformations and ltering operations.

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Singh, Gurpreet. "Statistical Modeling of Dynamic Risk in Security Systems." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273599.

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Big data has been used regularly in finance and business to build forecasting models. It is, however, a relatively new concept in the security industry. This study predicts technology related alarm codes that will sound in the coming 7 days at location $L$ by observing the past 7 days. Logistic regression and neural networks are applied to solve this problem. Due to the problem being of a multi-labeled nature logistic regression is applied in combination with binary relevance and classifier chains. The models are trained on data that has been labeled with two separate methods, the first method labels the data by only observing location $L$. The second considers $L$ and $L$'s surroundings. As the problem is multi-labeled the labels are likely to be unbalanced, thus a resampling technique, SMOTE, and random over-sampling is applied to increase the frequency of the minority labels. Recall, precision, and F1-score are calculated to evaluate the models. The results show that the second labeling method performs better for all models and that the classifier chains and binary relevance model performed similarly. Resampling the data with the SMOTE technique increases the macro average F1-scores for the binary relevance and classifier chains models, however, the neural networks performance decreases. The SMOTE resampling technique also performs better than random over-sampling. The neural networks model outperforms the other two models on all methods and achieves the highest F1-score.
Big data har använts regelbundet inom ekonomi för att bygga prognosmodeller, det är dock ett relativt nytt koncept inom säkerhetsbranschen. Denna studie förutsäger vilka larmkoder som kommer att låta under de kommande 7 dagarna på plats $L$ genom att observera de senaste 7 dagarna. Logistisk regression och neurala nätverk används för att lösa detta problem. Eftersom att problemet är av en multi-label natur tillämpas logistisk regression i kombination med binary relevance och classifier chains. Modellerna tränas på data som har annoterats med två separata metoder. Den första metoden annoterar datan genom att endast observera plats $L$ och den andra metoden betraktar $L$ och $L$:s omgivning. Eftersom problemet är multi-labeled kommer annoteringen sannolikt att vara obalanserad och därför används resamplings metoden, SMOTE, och random over-sampling för att öka frekvensen av minority labels. Recall, precision och F1-score mättes för att utvärdera modellerna. Resultaten visar att den andra annoterings metoden presterade bättre för alla modeller och att classifier chains och binary relevance presterade likartat. Binary relevance och classifier chains modellerna som tränades på datan som använts sig av resamplings metoden SMOTE gav ett högre macro average F1-score, dock sjönk prestationen för neurala nätverk. Resamplings metoden SMOTE presterade även bättre än random over-sampling. Neurala nätverksmodellen överträffade de andra två modellerna på alla metoder och uppnådde högsta F1-score.

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Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each service component along with our solutions and results. We mainly contribute to cost-sensitive hybrid classification, selective classification, model calibration, and active learning. We highlight the importance of model calibration in hybrid classification services and propose novel approaches to improve the calibration of human-machine classifiers. In addition, we argue that the current accuracy-based metrics are misaligned with the actual value of machine learning models and propose a novel metric ``value". We further test the performance of SOTA machine learning models in NLP tasks with a cost-sensitive hybrid classification context. We show that the performance of the SOTA models in cost-sensitive tasks significantly drops when we evaluate them according to value rather than accuracy. Finally, we investigate the quality of hybrid classifiers in the active learning scenarios. We review the existing active learning strategies, evaluate their effectiveness, and propose a novel value-aware active learning strategy to improve the performance of selective classifiers in the active learning of cost-sensitive tasks.

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McClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.

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In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having $11\%$ lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations.

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Dang, Robin, and Anders Nilsson. "Evaluation of Machine Learning classifiers for Breast Cancer Classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280349.

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Breast cancer is a common and fatal disease among women globally, where early detection is vital to improve the prognosis of patients. In today’s digital society, computers and complex algorithms can evaluate and diagnose diseases more efficiently and with greater certainty than experienced doctors. Several studies have been conducted to automate medical imaging techniques, by utilizing machine learning techniques, to predict and detect breast cancer. In this report, the suitability of using machine learning to classify whether breast cancer is of benign or malignant characteristic is evaluated. More specifically, five different machine learning methods are examined and compared. Furthermore, we investigate how the efficiency of the methods, with regards to classification accuracy and execution time, is affected by the preprocessing method Principal component analysis and the ensemble method Bootstrap aggregating. In theory, both methods should favor certain machine learning methods and consequently increase the classification accuracy. The study is based on a well-known breast cancer dataset from Wisconsin which is used to train the algorithms. The result was evaluated by applying statistical methods concerning the classification accuracy, sensitivity and execution time. Consequently, the results are then compared between the different classifiers. The study showed that the use of neither Principal component analysis nor Bootstrap aggregating resulted in any significant improvements in classification accuracy. However, the results showed that the support vector machines classifiers were the better performer. As the survey was limited in terms of the amount of datasets and the choice of different evaluation methods with associating adjustments, it is uncertain whether the obtained result can be generalized over other datasets or populations.
Bröstcancer är en vanlig och dödlig sjukdom bland kvinnor globalt där en tidig upptäckt är avgörande för att förbättra prognosen för patienter. I dagens digitala samhälle kan datorer och komplexa algoritmer utvärdera och diagnostisera sjukdomar mer effektivt och med större säkerhet än erfarna läkare. Flera studier har genomförts för att automatisera tekniker med medicinska avbildningsmetoder, genom maskininlärnings tekniker, för att förutsäga och upptäcka bröstcancer. I den här rapport utvärderas och jämförs lämpligheten hos fem olika maskininlärningsmetoder att klassificera huruvida bröstcancer är av god- eller elakartad karaktär. Vidare undersöks hur metodernas effektivitet, med avseende på klassificeringssäkerhet samt exekveringstid, påverkas av förbehandlingsmetoden Principal component analysis samt ensemble metoden Bootstrap aggregating. I teorin skall båda förbehandlingsmetoder gynna vissa maskininlärningsmetoder och således öka klassificeringssäkerheten. Undersökningen är baserat på ett välkänt bröstcancer dataset från Wisconsin som används till att träna algoritmerna. Resultaten är evaluerade genom applicering av statistiska metoder där träffsäkerhet, känslighet och exekveringstid tagits till hänsyn. Följaktligen jämförs resultaten mellan de olika klassificerarna. Undersökningen visade att användningen av varken Principal component analysis eller Bootstrap aggregating resulterade i några nämnvärda förbättringar med avseende på klassificeringssäkerhet. Dock visade resultaten att klassificerarna Support vector machines Linear och RBF presterade bäst. I och med att undersökningen var begränsad med avseende på antalet dataset samt val av olika evalueringsmetoder med medförande justeringar är det därför osäkert huruvida det erhållna resultatet kan generaliseras över andra dataset och populationer.

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Rigaki, Maria. "Adversarial Deep Learning Against Intrusion Detection Classifiers." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64577.

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Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluring than ever. The intention of this thesis is to show that using machine learning in the intrusion detection domain should be accompanied with an evaluation of its robustness against adversaries. Several adversarial techniques have emerged lately from the deep learning research, largely in the area of image classification. These techniques are based on the idea of introducing small changes in the original input data in order to make a machine learning model to misclassify it. This thesis follows a big data Analytics methodol- ogy and explores adversarial machine learning techniques that have emerged from the deep learning domain, against machine learning classifiers used for network intrusion detection. The study looks at several well known classifiers and studies their performance under attack over several metrics, such as accuracy, F1-score and receiver operating character- istic. The approach used assumes no knowledge of the original classifier and examines both general and targeted misclassification. The results show that using relatively sim- ple methods for generating adversarial samples it is possible to lower the detection accuracy of intrusion detection classifiers from 5% to 28%. Performance degradation is achieved using a methodology that is simpler than previous approaches and it re- quires only 6.25% change between the original and the adversarial sample, making it a candidate for a practical adversarial approach.

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Ford, John M. "Pulsar Search Using Supervised Machine Learning." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1001.

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Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

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Burago, Igor. "Automated Attacks on Compression-Based Classifiers." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18439.

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Methods of compression-based text classification have proven their usefulness for various applications. However, in some classification problems, such as spam filtering, a classifier confronts one or many adversaries willing to induce errors in the classifier's judgment on certain kinds of input. In this thesis, we consider the problem of finding thrifty strategies for character-based text modification that allow an adversary to revert classifier's verdict on a given family of input texts. We propose three statistical statements of the problem that can be used by an attacker to obtain transformation models which are optimal in some sense. Evaluating these three techniques on a realistic spam corpus, we find that an adversary can transform a spam message (detectable as such by an entropy-based text classifier) into a legitimate one by generating and appending, in some cases, as few additional characters as 20% of the original length of the message.

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Ishii, Shotaro, and David Ljunggren. "A Comparative Analysis of Robustness to Noise in Machine Learning Classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302532.

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Data that stems from real measurements often to some degree contain distortions. Such distortions are generally referred to as noise in machine learning terminology, and can lead to decreased classification accuracy and poor prediction results. In this study, three machine learning classifiers were compared by their performance and robustness in the presence of noise. More specifically, random forests, support vector machines and artificial neural networks were trained and compared on four different data sets with varying levels of noise artificially added to them. In summary, the random forest classifier performed the best and was the most robust classifier at eight out of ten of noise levels, closely followed by the artificial neural network classifier. At the two remaining noise levels, the support vector machine classifier with a linear kernel performed the best and was the most robust classifier.
Data som härstammar från verkliga mätningar innehåller ofta förvrängningar i viss utsträckning. Sådana förvrängningar kan i vissa fall leda till försämrad klassificeringsnoggrannhet. I den här studien jämförs tre klassificeringsalgoritmer med avseende på hur pass robusta de är när den data de presenteras innehåller syntetiska förvrängningar. Mer specifikt så tränades och jämfördes slumpskogar, stödvektormaskiner och artificiella neuronnät på fyra olika mängder data med varierande nivåer av syntetiska förvrängningar. Sammanfattningsvis så presterade slumpskogen bäst, och var den mest robusta klassificeringsalgoritmen på åtta av tio förvrängningsnivåer, tätt följt av det artificiella neuronnätet. På de två återstående förvrängningsnivåerna presterade stödvektormaskinen med linjär kärna bäst och var den mest robusta klassificeringsalgoritmen.

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Книги з теми "Security of machine learning classifiers"

Learning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.

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Chen, Xiaofeng, Willy Susilo, and Elisa Bertino, eds. Cyber Security Meets Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6726-5.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62460-6.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7.

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Chen, Xiaofeng, Xinyi Huang, and Jun Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30619-9.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1.

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Dolev, Shlomi, Oded Margalit, Benny Pinkas, and Alexander Schwarzmann, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78086-9.

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Padmavathi, G., D. Shanmugapriya, and A. Roshni. "Evaluation of Supervised Machine Learning Classifiers to Detect Mobile Malware." In Progressions Made in Cyber-Security World, 10–21. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003302384-2.

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Singh, Amit Kumar, and Rajendra Pamula. "Vehicular Delay Tolerant Network Based Communication Using Machine Learning Classifiers." In Architectural Wireless Networks Solutions and Security Issues, 195–208. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0386-0_11.

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Wu, Datong, Taotao Wu, and Xiaotong Wu. "A Differentially Private Random Decision Tree Classifier with High Utility." In Machine Learning for Cyber Security, 376–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_32.

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Patil, Rohini, and Kamal Shah. "Performance Evaluation of Machine Learning Classifiers for Prediction of Type 2 Diabetes Using Stress-Related Parameters." In Data Science and Security, 93–101. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2211-4_8.

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Preethi, N., and W. Jaisingh. "Analysis of Fine Needle Aspiration Images by Using Hybrid Feature Selection and Various Machine Learning Classifiers." In Data Science and Security, 383–92. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2211-4_34.

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Bojjagani, Sriramulu, B. Ramachandra Reddy, Mulagala Sandhya, and Dinesh Reddy Vemula. "CybSecMLC: A Comparative Analysis on Cyber Security Intrusion Detection Using Machine Learning Classifiers." In Communications in Computer and Information Science, 232–45. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0419-5_19.

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Bhattacharya, Madhubrata, and Debabrata Datta. "Development of Predictive Models of Diabetes Using Ensemble Machine Learning Classifier." In Advancements in Smart Computing and Information Security, 377–88. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23092-9_30.

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Aggarwal, Ritu, and Prateek Thakral. "Meticulous Presaging Arrhythmia Fibrillation for Heart Disease Classification Using Oversampling Method for Multiple Classifiers Based on Machine Learning." In Advances in Data Computing, Communication and Security, 99–107. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8403-6_9.

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Arulmurugan, A., R. Kaviarasan, and Saiyed Faiayaz Waris. "Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security." In Big data management in Sensing, 115–48. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337355-9.

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Tran, Quang Duy, and Fabio Di Troia. "Word Embeddings for Fake Malware Generation." In Silicon Valley Cybersecurity Conference, 22–37. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_2.

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AbstractSignature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost $$90\%$$ of the time.

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Тези доповідей конференцій з теми "Security of machine learning classifiers"

Gao, Sida, and Geethapriya Thamilarasu. "Machine-Learning Classifiers for Security in Connected Medical Devices." In 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2017. http://dx.doi.org/10.1109/icccn.2017.8038507.

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Koli, J. D. "RanDroid: Android malware detection using random machine learning classifiers." In 2018 Technologies for Smart-City Energy Security and Power (ICSESP). IEEE, 2018. http://dx.doi.org/10.1109/icsesp.2018.8376705.

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Radhi Hadi, Mhmood, and Adnan Saher Mohammed. "A Novel Approach to Network Intrusion Detection System using Deep Learning for SDN: Futuristic Approach." In 4th International Conference on Machine Learning & Applications (CMLA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121106.

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Software-Defined Networking (SDN) is the next generation to change the architecture of traditional networks. SDN is one of the promising solutions to change the architecture of internet networks. Attacks become more common due to the centralized nature of SDN architecture. It is vital to provide security for the SDN. In this study, we propose a Network Intrusion Detection System-Deep Learning module (NIDS-DL) approach in the context of SDN. Our suggested method combines Network Intrusion Detection Systems (NIDS) with many types of deep learning algorithms. Our approach employs 12 features extracted from 41 features in the NSL-KDD dataset using a feature selection method. We employed classifiers (CNN, DNN, RNN, LSTM, and GRU). When we compare classifier scores, our technique produced accuracy results of (98.63%, 98.53%, 98.13%, 98.04%, and 97.78%) respectively. The novelty of our new approach (NIDS-DL) uses 5 deep learning classifiers and made pre-processing dataset to harvests the best results. Our proposed approach was successful in binary classification and detecting attacks, implying that our approach (NIDS-DL) might be used with great efficiency in the future.

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Yazdani-Abyaneh, Amir-Hossein, and Marwan Krunz. "Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers." In WiSec '22: 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3522783.3529524.

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Thapa, Bipun. "Sentiment Analysis of Cyber Security Content on Twitter and Reddit." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120708.

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Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.

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Alnashashibi, May, Wael Hadi, and Nuha El-Khalili. "Predicting stress levels of automobile drivers using classical machine learning classifiers." In 2022 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2022. http://dx.doi.org/10.1109/icbats54253.2022.9759005.

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Adeshina, Qozeem Adeniyi, and Baidya Nath Saha. "Using Machine Learning to Predict Distributed Denial-of-Service (DDoS) Attack." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.21.

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The IT space is growing in all aspects ranging from bandwidth, storage, processing speed, machine learning and data analysis. This growth has consequently led to more cyber threat and attacks which now requires innovative and predictive security approach that uses cutting-edge technologies in order to fight the menace. The patterns of the cyber threats will be observed so that proper analysis from different sets of data will be used to develop a model that will depend on the available data. Distributed Denial of Service is one of the most common threats and attacks that is ravaging computing devices on the internet. This research talks about the approaches and the development of machine learning classifiers to detect DDoS attacks before it eventually happen. The model is built with seven different selection techniques each using ten machine learning classifiers. The model learns to understand the normal network traffic so that it can detect an ICMP, TCP and UDP DDoS traffic when they arrive. The goal is to build a data-driven, intelligent and decision-making machine learning algorithm model that will use classifiers to categorize normal and DDoS traffic using KDD-99 dataset. Results have shown that some classifiers have very good predictions obtained within a very short time.

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Verticale, Giacomo. "On the Portability of Trained Machine Learning Classifiers for Early Application Identification." In 2008 Second International Conference on Emerging Security Information, Systems and Technologies (SECUREWARE). IEEE, 2008. http://dx.doi.org/10.1109/securware.2008.13.

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Jamil, Hasibul, Ning Yang, and Ning Weng. "Securing Home IoT Network with Machine Learning Based Classifiers." In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE, 2021. http://dx.doi.org/10.1109/wf-iot51360.2021.9594932.

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Aghakhani, Hojjat, Fabio Gritti, Francesco Mecca, Martina Lindorfer, Stefano Ortolani, Davide Balzarotti, Giovanni Vigna, and Christopher Kruegel. "When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2020. http://dx.doi.org/10.14722/ndss.2020.24310.

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Barreno, Marco, Blaine A. Nelson, Anthony D. Joseph, and Doug Tygar. The Security of Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada519143.

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Lucas, Christine, Emily Hadley, Jason Nance, Peter Baumgartner, Rita Thissen, David Plotner, Christine Carr, and Aerian Tatum. Machine Learning for Medical Coding in Health Care Surveys. National Center for Health Statistics (U.S.), October 2021. http://dx.doi.org/10.15620/cdc:109828.

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Poppeliers, Christian. LDRD 218327: Seismic Spatial Gradients and Machine Learning-Based Classifiers for Explosion Monitoring. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1854996.

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Verzi, Stephen, Raga Krishnakumar, Drew Levin, Daniel Krofcheck, and Kelly Williams. Data Science and Machine Learning for Genome Security. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1855003.

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Caley, Jeffrey. A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2000.

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Ritchey, Ralph P., Garrett S. Payer, and Richard E. Harang. Compilation of a Network Security/Machine Learning Toolchain for Android ARM Platforms. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada609411.

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Buchanan, Ben. A National Security Research Agenda for Cybersecurity and Artificial Intelligence. Center for Security and Emerging Technology, May 2020. http://dx.doi.org/10.51593/2020ca001.

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Machine learning advances are transforming cyber strategy and operations. This necessitates studying national security issues at the intersection of AI and cybersecurity, including offensive and defensive cyber operations, the cybersecurity of AI systems, and the effect of new technologies on global stability.

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Buchanan, Ben. The AI Triad and What It Means for National Security Strategy. Center for Security and Emerging Technology, August 2020. http://dx.doi.org/10.51593/20200021.

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One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

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