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2021-03-15
Danilova, A., Naiakshina, A., Smith, M..  2020.  One Size Does Not Fit All: A Grounded Theory and Online Survey Study of Developer Preferences for Security Warning Types. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :136–148.
A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.
Brauckmann, A., Goens, A., Castrillon, J..  2020.  ComPy-Learn: A toolbox for exploring machine learning representations for compilers. 2020 Forum for Specification and Design Languages (FDL). :1–4.
Deep Learning methods have not only shown to improve software performance in compiler heuristics, but also e.g. to improve security in vulnerability prediction or to boost developer productivity in software engineering tools. A key to the success of such methods across these use cases is the expressiveness of the representation used to abstract from the program code. Recent work has shown that different such representations have unique advantages in terms of performance. However, determining the best-performing one for a given task is often not obvious and requires empirical evaluation. Therefore, we present ComPy-Learn, a toolbox for conveniently defining, extracting, and exploring representations of program code. With syntax-level language information from the Clang compiler frontend and low-level information from the LLVM compiler backend, the tool supports the construction of linear and graph representations and enables an efficient search for the best-performing representation and model for tasks on program code.
2021-03-09
Anithaashri, T. P., Ravichandran, G..  2020.  Security Enhancement for the Network Amalgamation using Machine Learning Algorithm. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :411—416.

Accessing the secured data through the network is a major task in emerging technology. Data needs to be protected from the network vulnerabilities, malicious users, hackers, sniffers, intruders. The novel framework has been designed to provide high security in data transaction through computer network. The implant of network amalgamation in the recent trends, make the way in security enhancement in an efficient manner through the machine learning algorithm. In this system the usage of the biometric authenticity plays a vital role for unique approach. The novel mathematical approach is used in machine learning algorithms to solve these problems and provide the security enhancement. The result shows that the novel method has consistent improvement in enhancing the security of data transactions in the emerging technologies.

Lee, T., Chang, L., Syu, C..  2020.  Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

Hossain, M. D., Ochiai, H., Doudou, F., Kadobayashi, Y..  2020.  SSH and FTP brute-force Attacks Detection in Computer Networks: LSTM and Machine Learning Approaches. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :491—497.

Network traffic anomaly detection is of critical importance in cybersecurity due to the massive and rapid growth of sophisticated computer network attacks. Indeed, the more new Internet-related technologies are created, the more elaborate the attacks become. Among all the contemporary high-level attacks, dictionary-based brute-force attacks (BFA) present one of the most unsurmountable challenges. We need to develop effective methods to detect and mitigate such brute-force attacks in realtime. In this paper, we investigate SSH and FTP brute-force attack detection by using the Long Short-Term Memory (LSTM) deep learning approach. Additionally, we made use of machine learning (ML) classifiers: J48, naive Bayes (NB), decision table (DT), random forest (RF) and k-nearest-neighbor (k-NN), for additional detection purposes. We used the well-known labelled dataset CICIDS2017. We evaluated the effectiveness of the LSTM and ML algorithms, and compared their performance. Our results show that the LSTM model outperforms the ML algorithms, with an accuracy of 99.88%.

Zhou, B., He, J., Tan, M..  2020.  A Two-stage P2P Botnet Detection Method Based on Statistical Features. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :497—502.

P2P botnet has become one of the most serious threats to today's network security. It can be used to launch kinds of malicious activities, ranging from spamming to distributed denial of service attack. However, the detection of P2P botnet is always challenging because of its decentralized architecture. In this paper, we propose a two-stage P2P botnet detection method which only relies on several traffic statistical features. This method first detects P2P hosts based on three statistical features, and then distinguishes P2P bots from benign P2P hosts by means of another two statistical features. Experimental evaluations on real-world traffic datasets shows that our method is able to detect hidden P2P bots with a detection accuracy of 99.7% and a false positive rate of only 0.3% within 5 minutes.

Susanto, Stiawan, D., Arifin, M. A. S., Idris, M. Y., Budiarto, R..  2020.  IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning. 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). :15—20.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Injadat, M., Moubayed, A., Shami, A..  2020.  Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach. 2020 32nd International Conference on Microelectronics (ICM). :1—4.

The increased reliance on the Internet and the corresponding surge in connectivity demand has led to a significant growth in Internet-of-Things (IoT) devices. The continued deployment of IoT devices has in turn led to an increase in network attacks due to the larger number of potential attack surfaces as illustrated by the recent reports that IoT malware attacks increased by 215.7% from 10.3 million in 2017 to 32.7 million in 2018. This illustrates the increased vulnerability and susceptibility of IoT devices and networks. Therefore, there is a need for proper effective and efficient attack detection and mitigation techniques in such environments. Machine learning (ML) has emerged as one potential solution due to the abundance of data generated and available for IoT devices and networks. Hence, they have significant potential to be adopted for intrusion detection for IoT environments. To that end, this paper proposes an optimized ML-based framework consisting of a combination of Bayesian optimization Gaussian Process (BO-GP) algorithm and decision tree (DT) classification model to detect attacks on IoT devices in an effective and efficient manner. The performance of the proposed framework is evaluated using the Bot-IoT-2018 dataset. Experimental results show that the proposed optimized framework has a high detection accuracy, precision, recall, and F-score, highlighting its effectiveness and robustness for the detection of botnet attacks in IoT environments.

Hegde, M., Kepnang, G., Mazroei, M. Al, Chavis, J. S., Watkins, L..  2020.  Identification of Botnet Activity in IoT Network Traffic Using Machine Learning. 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA). :21—27.

Today our world benefits from Internet of Things (IoT) technology; however, new security problems arise when these IoT devices are introduced into our homes. Because many of these IoT devices have access to the Internet and they have little to no security, they make our smart homes highly vulnerable to compromise. Some of the threats include IoT botnets and generic confidentiality, integrity, and availability (CIA) attacks. Our research explores botnet detection by experimenting with supervised machine learning and deep-learning classifiers. Further, our approach assesses classifier performance on unbalanced datasets that contain benign data, mixed in with small amounts of malicious data. We demonstrate that the classifiers can separate malicious activity from benign activity within a small IoT network dataset. The classifiers can also separate malicious activity from benign activity in increasingly larger datasets. Our experiments have demonstrated incremental improvement in results for (1) accuracy, (2) probability of detection, and (3) probability of false alarm. The best performance results include 99.9% accuracy, 99.8% probability of detection, and 0% probability of false alarm. This paper also demonstrates how the performance of these classifiers increases, as IoT training datasets become larger and larger.

Muhammad, A., Asad, M., Javed, A. R..  2020.  Robust Early Stage Botnet Detection using Machine Learning. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1—6.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

Yerima, S. Y., Alzaylaee, M. K..  2020.  Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—8.

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

MATSUNAGA, Y., AOKI, N., DOBASHI, Y., KOJIMA, T..  2020.  A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :653–656.
This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models a distortion stomp box as a neural network consisting of LSTM layers. In this approach, the neural network is employed for learning the nonlinear behavior of the distortion stomp boxes. All the parameters for replicating the distortion sound are estimated through its training process using the input and output signals obtained from some commercial stomp boxes. The experimental result indicates that the proposed technique may have a certain appropriateness to replicate the distortion sound by using the well-trained neural networks.
2021-03-04
Ghaffaripour, S., Miri, A..  2020.  A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :4510—4515.
Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients' data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.
Abedin, N. F., Bawm, R., Sarwar, T., Saifuddin, M., Rahman, M. A., Hossain, S..  2020.  Phishing Attack Detection using Machine Learning Classification Techniques. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). :1125—1130.

Phishing attacks are the most common form of attacks that can happen over the internet. This method involves attackers attempting to collect data of a user without his/her consent through emails, URLs, and any other link that leads to a deceptive page where a user is persuaded to commit specific actions that can lead to the successful completion of an attack. These attacks can allow an attacker to collect vital information of the user that can often allow the attacker to impersonate the victim and get things done that only the victim should have been able to do, such as carry out transactions, or message someone else, or simply accessing the victim's data. Many studies have been carried out to discuss possible approaches to prevent such attacks. This research work includes three machine learning algorithms to predict any websites' phishing status. In the experimentation these models are trained using URL based features and attempted to prevent Zero-Day attacks by using proposed software proposal that differentiates the legitimate websites and phishing websites by analyzing the website's URL. From observations, the random forest classifier performed with a precision of 97%, a recall 99%, and F1 Score is 97%. Proposed model is fast and efficient as it only works based on the URL and it does not use other resources for analysis, as was the case for past studies.

Wang, H., Sayadi, H., Kolhe, G., Sasan, A., Rafatirad, S., Homayoun, H..  2020.  Phased-Guard: Multi-Phase Machine Learning Framework for Detection and Identification of Zero-Day Microarchitectural Side-Channel Attacks. 2020 IEEE 38th International Conference on Computer Design (ICCD). :648—655.

Microarchitectural Side-Channel Attacks (SCAs) have emerged recently to compromise the security of computer systems by exploiting the existing processors' hardware vulnerabilities. In order to detect such attacks, prior studies have proposed the deployment of low-level features captured from built-in Hardware Performance Counter (HPC) registers in modern microprocessors to implement accurate Machine Learning (ML)-based SCAs detectors. Though effective, such attack detection techniques have mainly focused on binary classification models offering limited insights on identifying the type of attacks. In addition, while existing SCAs detectors required prior knowledge of attacks applications to detect the pattern of side-channel attacks using a variety of microarchitectural features, detecting unknown (zero-day) SCAs at run-time using the available HPCs remains a major challenge. In response, in this work we first identify the most important HPC features for SCA detection using an effective feature reduction method. Next, we propose Phased-Guard, a two-level machine learning-based framework to accurately detect and classify both known and unknown attacks at run-time using the most prominent low-level features. In the first level (SCA Detection), Phased-Guard using a binary classification model detects the existence of SCAs on the target system by determining the critical scenarios including system under attack and system under no attack. In the second level (SCA Identification) to further enhance the security against side-channel attacks, Phased-Guard deploys a multiclass classification model to identify the type of SCA applications. The experimental results indicate that Phased-Guard by monitoring only the victim applications' microarchitectural HPCs data, achieves up to 98 % attack detection accuracy and 99.5% SCA identification accuracy significantly outperforming the state-of-the-art solutions by up to 82 % in zero-day attack detection at the cost of only 4% performance overhead for monitoring.

Guo, H., Wang, Z., Wang, B., Li, X., Shila, D. M..  2020.  Fooling A Deep-Learning Based Gait Behavioral Biometric System. 2020 IEEE Security and Privacy Workshops (SPW). :221—227.

We leverage deep learning algorithms on various user behavioral information gathered from end-user devices to classify a subject of interest. In spite of the ability of these techniques to counter spoofing threats, they are vulnerable to adversarial learning attacks, where an attacker adds adversarial noise to the input samples to fool the classifier into false acceptance. Recently, a handful of mature techniques like Fast Gradient Sign Method (FGSM) have been proposed to aid white-box attacks, where an attacker has a complete knowledge of the machine learning model. On the contrary, we exploit a black-box attack to a behavioral biometric system based on gait patterns, by using FGSM and training a shadow model that mimics the target system. The attacker has limited knowledge on the target model and no knowledge of the real user being authenticated, but induces a false acceptance in authentication. Our goal is to understand the feasibility of a black-box attack and to what extent FGSM on shadow models would contribute to its success. Our results manifest that the performance of FGSM highly depends on the quality of the shadow model, which is in turn impacted by key factors including the number of queries allowed by the target system in order to train the shadow model. Our experimentation results have revealed strong relationships between the shadow model and FGSM performance, as well as the effect of the number of FGSM iterations used to create an attack instance. These insights also shed light on deep-learning algorithms' model shareability that can be exploited to launch a successful attack.

Kalin, J., Ciolino, M., Noever, D., Dozier, G..  2020.  Black Box to White Box: Discover Model Characteristics Based on Strategic Probing. 2020 Third International Conference on Artificial Intelligence for Industries (AI4I). :60—63.

In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model information: the underlying architecture and primary training dataset. With the process in this paper, a structured set of input probes and the output of the model become the training data for a deep classifier. Two subdomains in Machine Learning are explored - image based classifiers and text transformers with GPT-2. With image classification, the focus is on exploring commonly deployed architectures and datasets available in popular public libraries. Using a single transformer architecture with multiple levels of parameters, text generation is explored by fine tuning off different datasets. Each dataset explored in image and text are distinguishable from one another. Diversity in text transformer outputs implies further research is needed to successfully classify architecture attribution in text domain.

Crescenzo, G. D., Bahler, L., McIntosh, A..  2020.  Encrypted-Input Program Obfuscation: Simultaneous Security Against White-Box and Black-Box Attacks. 2020 IEEE Conference on Communications and Network Security (CNS). :1—9.

We consider the problem of protecting cloud services from simultaneous white-box and black-box attacks. Recent research in cryptographic program obfuscation considers the problem of protecting the confidentiality of programs and any secrets in them. In this model, a provable program obfuscation solution makes white-box attacks to the program not more useful than black-box attacks. Motivated by very recent results showing successful black-box attacks to machine learning programs run by cloud servers, we propose and study the approach of augmenting the program obfuscation solution model so to achieve, in at least some class of application scenarios, program confidentiality in the presence of both white-box and black-box attacks.We propose and formally define encrypted-input program obfuscation, where a key is shared between the entity obfuscating the program and the entity encrypting the program's inputs. We believe this model might be of interest in practical scenarios where cloud programs operate over encrypted data received by associated sensors (e.g., Internet of Things, Smart Grid).Under standard intractability assumptions, we show various results that are not known in the traditional cryptographic program obfuscation model; most notably: Yao's garbled circuit technique implies encrypted-input program obfuscation hiding all gates of an arbitrary polynomial circuit; and very efficient encrypted-input program obfuscation for range membership programs and a class of machine learning programs (i.e., decision trees). The performance of the latter solutions has only a small constant overhead over the equivalent unobfuscated program.

2021-03-01
Tan, R., Khan, N., Guan, L..  2020.  Locality Guided Neural Networks for Explainable Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.
Sun, S. C., Guo, W..  2020.  Approximate Symbolic Explanation for Neural Network Enabled Water-Filling Power Allocation. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). :1–4.
Water-filling (WF) is a well-established iterative solution to optimal power allocation in parallel fading channels. Slow iterative search can be impractical for allocating power to a large number of OFDM sub-channels. Neural networks (NN) can transform the iterative WF threshold search process into a direct high-dimensional mapping from channel gain to transmit power solution. Our results show that the NN can perform very well (error 0.05%) and can be shown to be indeed performing approximate WF power allocation. However, there is no guarantee on the NN is mapping between channel states and power output. Here, we attempt to explain the NN power allocation solution via the Meijer G-function as a general explainable symbolic mapping. Our early results indicate that whilst the Meijer G-function has universal representation potential, its large search space means finding the best symbolic representation is challenging.
Davis, B., Glenski, M., Sealy, W., Arendt, D..  2020.  Measure Utility, Gain Trust: Practical Advice for XAI Researchers. 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX). :1–8.
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.
Golagha, M., Pretschner, A., Briand, L. C..  2020.  Can We Predict the Quality of Spectrum-based Fault Localization? 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). :4–15.
Fault localization and repair are time-consuming and tedious. There is a significant and growing need for automated techniques to support such tasks. Despite significant progress in this area, existing fault localization techniques are not widely applied in practice yet and their effectiveness varies greatly from case to case. Existing work suggests new algorithms and ideas as well as adjustments to the test suites to improve the effectiveness of automated fault localization. However, important questions remain open: Why is the effectiveness of these techniques so unpredictable? What are the factors that influence the effectiveness of fault localization? Can we accurately predict fault localization effectiveness? In this paper, we try to answer these questions by collecting 70 static, dynamic, test suite, and fault-related metrics that we hypothesize are related to effectiveness. Our analysis shows that a combination of only a few static, dynamic, and test metrics enables the construction of a prediction model with excellent discrimination power between levels of effectiveness (eight metrics yielding an AUC of .86; fifteen metrics yielding an AUC of.88). The model hence yields a practically useful confidence factor that can be used to assess the potential effectiveness of fault localization. Given that the metrics are the most influential metrics explaining the effectiveness of fault localization, they can also be used as a guide for corrective actions on code and test suites leading to more effective fault localization.
Lim, S., Ko, Y..  2020.  Intellectual Priority-based Low Latency Data Delivery Scheme for Multi-interface and Multi-channel Devices in Multi-hop Wireless Mesh Networks. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :417–419.
In multi-hop wireless mesh networks, the end-to-end delay for a packet is getting longer as the relaying hops to the destination are increasing. The real-time packet such as the urgent safety message should be delivered within the stipulated deadline. Most previous studies have been focused to find out the optimal route to the destination. We propose an intellectual priority-based packet transmission scheme for multi-interface devices in multi-hop wireless mesh networks.
2021-02-23
Khan, M., Rehman, O., Rahman, I. M. H., Ali, S..  2020.  Lightweight Testbed for Cybersecurity Experiments in SCADA-based Systems. 2020 International Conference on Computing and Information Technology (ICCIT-1441). :1—5.

A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.

Ratti, R., Singh, S. R., Nandi, S..  2020.  Towards implementing fast and scalable Network Intrusion Detection System using Entropy based Discretization Technique. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.