Biblio

As the traditional grids are transitioning to the smart grid, they are getting more prone to cyber-attacks. Among all the cyber-attack one of the most dangerous attack is false data injection attack. When this attack is performed with historical information of the data packet the attack goes undetected. As the false data is included for training and testing the model, the accuracy is decreased, and decision making is affected. In this paper we analyzed the impact of the false data injection attack(FDIA) on AI based smart grid. These analyses were performed using two different multi-layer perceptron architectures with one of the independent variables being compared and modified by the attacker. The root-mean squared values were compared with different models.

Bitcoin has received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the Bitcoin price usually fluctuates greatly. However, not enough scientific research has been done on these fluctuations. In this study, long short-term memory (LSTM) modeling from Recurrent Neural Networks, which is one of the deep learning methods, was applied on Bitcoin values. As a result of this application, anomaly detection was carried out in the values from the data set. With the LSTM network, a time-dependent representation of Bitcoin price can be captured, and anomalies can be selected. The factors that play a role in the formation of the model to be applied in the detection of anomalies with the experimental results were evaluated.

This study shows the effectiveness of anomaly-based IDS using long short-term memory(LSTM) based on the newly developed dataset called UNSW-NB15 while considering root mean square error and mean absolute error as evaluation metrics for accuracy. For each attack, 80% and 90% of samples were used as LSTM inputs and trained this model while increasing epoch values. Furthermore, this model has predicted attack points by applying test data and produced possible attack points for each attack at the 3rd time frame against the actual attack point. However, in the case of an Exploit attack, the consecutive overlapping attacks happen, there was ambiguity in the interpretation of the numerical values calculated by the LSTM. We presented a methodology for training data with binary values using LSTM and evaluation with RMSE metrics throughout this study.

Currently, more and more malicious insiders are making threats, and the detection of insider threats is becoming more challenging. The malicious insider often uses legitimate access privileges and mimic normal behaviors to evade detection, which is difficult to be detected via using traditional defensive solutions. In this paper, we propose DeepMIT, a malicious insider threat detection framework, which utilizes Recurrent Neural Network (RNN) to model user behaviors as time sequences and predict the probabilities of anomalies. This framework allows DeepMIT to continue learning, and the detections are made in real time, that is, the anomaly alerts are output as rapidly as data input. Also, our framework conducts further insight of the anomaly scores and provides the contributions to the scores and, thus, significantly helps the operators to understand anomaly scores and take further steps quickly(e.g. Block insider's activity). In addition, DeepMIT utilizes user-attributes (e.g. the personality of the user, the role of the user) as categorical features to identify the user's truly typical behavior, which help detect malicious insiders who mimic normal behaviors. Extensive experimental evaluations over a public insider threat dataset CERT (version 6.2) have demonstrated that DeepMIT has outperformed other existing malicious insider threat solutions.

Mitigating cyber threats requires adequate understanding of the attacker characteristics in particular their patterns. Such knowledge is essential in addressing the defensive measures that mitigate the attack. If the attacker enters in the network system, the game tree model generates resources by to counter such threat. This is done by altering the parity in the next game tree iteration which yield an adequate response to counter it. If an attacker enters a network system, and a game tree models the resources he must interface with, then that game tree can be altered, by changing the parity on the next to last iteration. This paper analyzes the sequence of patterns based on incoming attacks. The detection of attacker’s pattern and subsequent changes in iterations to counter threat can be viewed as adequate resource or know how in cyber threat mitigations It was realized that changing the game tree of the hacker deprives the attacker of network resources and hence would represent a defensive measure against the attack; that is changing varying or understanding attacker paths, creates an effective defensive measure to protect the system against the incoming threats.. In this paper we analyze a unique combination of CFR and MCTS that attempts to detect the behavior of a hacker. Counterfactual Regret (CFR) is a game theory concept that helps identify patterns of attacks. The pattern recognition concept of Monte Carlo Tree Search (MCTS) is used in harmony with CFR in order to enhance the detection of attacks.

The front-end text processing module is considered as an essential part that influences the intelligibility and naturalness of a Mandarin text-to-speech system significantly. For commercial text-to-speech systems, the Mandarin front-end should meet the requirements of high accuracy and low time latency while also ensuring maintainability. In this paper, we propose a universal BERT-based model that can be used for various tasks in the Mandarin front-end without changing its architecture. The feature extractor and classifiers in the model are shared for several sub-tasks, which improves the expandability and maintainability. We trained and evaluated the model with polyphone disambiguation, text normalization, and prosodic boundary prediction for single task modules and multi-task learning. Results show that, the model maintains high performance for single task modules and shows higher accuracy and lower time latency for multi-task modules, indicating that the proposed universal front-end model is promising as a maintainable Mandarin front-end for commercial applications.

The accessibility of the internet and mobile platforms has risen dramatically due to digital technology innovations. Web applications have opened up a variety of market possibilities by supplying consumers with a wide variety of digital technologies that benefit from high accessibility and functionality. Around the same time, web application protection continues to be an important challenge on the internet, and security must be taken seriously in order to secure confidential data. The threat is caused by inadequate validation of user input information, software developed without strict adherence to safety standards, vulnerability of reusable software libraries, software weakness, and so on. Through abusing a website's vulnerability, introduers are manipulating the user's information in order to exploit it for their own benefit. Then introduers inject their own malicious code, stealing passwords, manipulating user activities, and infringing on customers' privacy. As a result, information is leaked, applications malfunction, confidential data is accessed, etc. To mitigate the aforementioned issues, stacking ensemble based classifier model for Cross-site scripting (XSS) attack detection is proposed. Furthermore, the stacking ensembles technique is used in combination with different machine learning classification algorithms like k-Means, Random Forest and Decision Tree as base-learners to reliably detect XSS attack. Logistic Regression is used as meta-learner to predict the attack with greater accuracy. The classification algorithms in stacking model explore the problem in their own way and its results are given as input to the meta-learner to make final prediction, thus improving the overall detection accuracy of XSS attack in stacking than the individual models. The simulation findings demonstrate that the proposed model detects XSS attack successfully.

Security databases such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) maintain diverse high-quality security concepts, which are treated as security entities. Meanwhile, security entities are documented with many potential relation types that profit for security analysis and comprehension across these three popular databases. To support reasoning security entity relationships, translation-based knowledge graph representation learning treats each triple independently for the entity prediction. However, it neglects the important semantic information about the neighbor entities around the triples. To address it, we propose a text-enhanced graph attention network model (text-enhanced GAT). This model highlights the importance of the knowledge in the 2-hop neighbors surrounding a triple, under the observation of the diversity of each entity. Thus, we can capture more structural and textual information from the knowledge graph about the security databases. Extensive experiments are designed to evaluate the effectiveness of our proposed model on the prediction of security entity relationships. Moreover, the experimental results outperform the state-of-the-art by Mean Reciprocal Rank (MRR) 0.132 for detecting the missing relationships.

Employees' compliance with information security policies (ISP) which may minimize the information security threats has always been a major concern for organizations. Numerous research and theoretical models had been investigated in the related field of study to identify factors that influence ISP compliance behavior. The study presented in this paper is the first to apply the Theory of Interpersonal Behavior (TIB) for predicting ISP compliance, despite a few studies suggested its strong explanatory power. Taking on the prior results of the literature review, we adopt the TIB and aim to further the theoretical advancement in this field of study. Besides, previous studies had only focused on individuals as well as organizations in which the role of government, from the aspect of its effectiveness in enforcing data protection regulation, so far has not been tested on its influence on individuals' intention to comply with ISP. Hence, we propose an exploratory study to integrate government effectiveness with TIB to explain ISP compliance in a Malaysian context. Our results show a significant influence of government effectiveness in ISP compliance, and the TIB is a promising model as well as posing strong explanatory power in predicting ISP compliance.

Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.

The growing adoption of IoT devices is creating a huge positive impact on human life. However, it is also making the network more vulnerable to security threats. One of the major threats is malicious traffic injection attack, where the hacked IoT devices overwhelm the application servers causing large-scale service disruption. To address such attacks, we propose a Software Defined Networking based predictive alarm manager solution for malicious traffic detection and mitigation at the IoT Gateway. Our experimental results with the proposed solution confirms the detection of malicious flows with nearly 95% precision on average and at its best with around 99% precision.

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery.

Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. These attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. The importance of attack recovery is emphasized by the need to mitigate the attack’s impact on a system and restore it to continue functioning. There are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. The framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. Third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. The results show that our method restores a system to continue functioning in presence of sensor attacks.

Securing communication between any two wireless devices or users is challenging without compromising sensitive/personal data. To address this problem, we have developed an artificial intelligence (AI) algorithm to secure communication on virtualized wireless networks. To detect cyberattacks in a virtualized environment is challenging compared to traditional wireless networks setting. However, we successfully investigate an efficient cyberattack detection algorithm using an AI algorithm in a Bayesian learning model for detecting cyberattacks on the fly. We have studied the results of Random Forest and deep neural network (DNN) models to detect the cyberattacks on a virtualized wireless network, having considered the required transmission power as a threshold value to classify suspicious activities in our model. We present both formal mathematical analysis and numerical results to support our claims. The numerical results show our accuracy in detecting cyberattacks in the proposed Bayesian model is better than Random Forest and DNN models. We have also compared both models in terms of detection errors. The performance comparison results show our proposed approach outperforms existing approaches in detection accuracy, precision, and recall.

Polymerization process is a process in the production of polyester fiber, and its reaction parameter intrinsic viscosity has an important influence on the properties of the final polyester fiber. In this paper, a feature selection extreme learning machine model based on binary encoding Atom Search Optimization algorithm is proposed and applied to the polymerization process of polyester fiber production. Firstly, the distance measure of K-NearestNeighbor algorithm, combined with binary coding, and Atom Search Optimization algorithm are used to select features of industrial data to obtain the optimal data set. According to the data set, atom search optimization algorithm is used to optimize the weight and threshold of extreme learning machine and the activation function of the improved extreme learning machine. A prediction model with root mean square error as fitness function was established and applied to polyester production process. The simulation results show that the model has good prediction accuracy, which can be used for reference in the follow-up industrial production.

With the rapid development of Internet scale and technology, people pay more and more attention to network security. At present, the general method in the field of network security is to use NSS(Network Security Situation) to describe the security situation of the target network. Because NSSA (Network Security Situation Awareness) has not formed a unified optimal solution in architecture design and algorithm design, many ideas have been put forward continuously, and there is still a broad research space. In this paper, the improved LSTM(long short-term memory) neural network is used to analyze and process NSS data, and effectively utilize the attack logic contained in sequence data. Build NSSF (Network Security Situation Forecast) framework based on NAWL-ILSTM. The framework is to directly output the quantified NSS change curve after processing the input original security situation data. Modular design and dual discrimination engine reduce the complexity of implementation and improve the stability. Simulation results show that the prediction model not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.

The purpose of the study is to improve the security of the network, and make the state of network security predicted in advance. First, the theory of neural networks is studied, and its shortcomings are analyzed by the standard Elman neural network. Second, the layers of the feedback nodes of the Elman neural network are improved according to the problems that need to be solved. Then, a network security perception system based on GA-Elman (Genetic Algorithm-Elman) neural network is proposed to train the network by global search method. Finally, the perception ability is compared and analyzed through the model. The results show that the model can accurately predict network security based on the experimental charts and corresponding evaluation indexes. The comparative experiments show that the GA-Elman neural network security perception system has a better prediction ability. Therefore, the model proposed can be used to predict the state of network security and provide early warnings for network security administrators.

The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our ``6 Agree'' technique.

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10 x on some samples for both of the target systems.

To enhance the security of digital circuits, there is often a desire to dynamically generate, rather than statically store, random values used for identification and authentication purposes. Physically Unclonable Functions (PUFs) provide the means to realize this feature in an efficient and reliable way by utilizing commonly overlooked process variations that unintentionally occur during the manufacturing of integrated circuits (ICs) due to the imperfection of fabrication process. When given a challenge, PUFs produce a unique response. However, PUFs have been found to be vulnerable to modeling attacks where by using a set of collected challenge response pairs (CRPs) and training a machine learning model, the response can be predicted for unseen challenges. To combat this vulnerability, researchers have proposed techniques such as Challenge Obfuscation. However, as shown in this paper, this technique can be compromised via modeling the PUF's power side-channel. We first show the vulnerability of a state-of-the-art Challenge Obfuscated PUF (CO-PUF) against power analysis attacks by presenting our attack results on the targeted CO-PUF. Then we propose two countermeasures, as well as their hybrid version, that when applied to the CO-PUFs make them resilient against power side-channel based modeling attacks. We also provide some insights on the proper design metrics required to be taken when implementing these mitigations. Our simulation results show the high success of our attack in compromising the original Challenge Obfuscated PUFs (success rate textgreater 98%) as well as the significant improvement on resilience of the obfuscated PUFs against power side-channel based modeling when equipped with our countermeasures.

Intrusion alerts continue to grow in volume, variety, and complexity. Its cryptic nature requires substantial time and expertise to interpret the intended consequence of observed malicious actions. To assist security analysts in effectively diagnosing what alerts mean, this work develops a novel machine learning approach that translates alert descriptions to intuitively interpretable Action-Intent-Stages (AIS) with only 1% labeled data. We combine transfer learning, active learning, and pseudo labels and develop the Pseudo-Active Transfer Learning (PATRL) process. The PATRL process begins with an unsupervised-trained language model using MITRE ATT&CK, CVE, and IDS alert descriptions. The language model feeds to an LSTM classifier to train with 1% labeled data and is further enhanced with active learning using pseudo labels predicted by the iteratively improved models. Our results suggest PATRL can predict correctly for 85% (top-1 label) and 99% (top-3 labels) of the remaining 99% unknown data. Recognizing the need to build confidence for the analysts to use the model, the system provides Monte-Carlo Dropout Uncertainty and Pseudo-Label Convergence Score for each of the predicted alerts. These metrics give the analyst insights to determine whether to directly trust the top-1 or top-3 predictions and whether additional pseudo labels are needed. Our approach overcomes a rarely tackled research problem where minimal amounts of labeled data do not reflect the truly unlabeled data's characteristics. Combining the advantages of transfer learning, active learning, and pseudo labels, the PATRL process translates the complex intrusion alert description for the analysts with confidence.

Computer networks are exposed to cyber related attacks due to the common usage of internet, as the result of such, several intrusion detection systems (IDSs) were proposed by several researchers. Among key research issues in securing network is detecting intrusions. It helps to recognize unauthorized usage and attacks as a measure to ensure the secure the network's security. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, nature inspired meta-heuristic techniques, swarm smart algorithm, and Markov neural network. Over years, the various works being carried out were evaluated on different datasets. This paper presents a thorough review on various research articles that employed single, hybrid and ensemble classification algorithms. The results metrics, shortcomings and datasets used by the studied articles in the development of IDS were compared. A future direction for potential researches is also given.

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and poten-tial vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.

The performance prediction of user equipment (UE) metrics has many applications in the 5G era and beyond. For instance, throughput prediction can improve carrier selection, adaptive video streaming's quality of experience (QoE), and traffic latency. Many studies suggest distributed learning algorithms (e.g., federated learning (FL)) for this purpose. However, in a multi-tier design, features are measured in different tiers, e.g., UE tier, and gNodeB (gNB) tier. On one hand, neglecting the measurements in one tier results in inaccurate predictions. On the other hand, transmitting the data from one tier to another improves the prediction performance at the expense of increasing network overhead and privacy risks. In this paper, we propose cascaded FL to enhance UE throughput prediction with minimum network footprint and privacy ramifications (if any). The idea is to introduce feedback to conventional FL, in multi-tier architectures. Although we use cascaded FL for UE prediction tasks, the idea is rather general and can be used for many prediction problems in multi-tier architectures, such as cellular networks. We evaluate the performance of cascaded FL by detailed and 3GPP compliant simulations of London's city center. Our simulations show that the proposed cascaded FL can achieve up to 54% improvement over conventional FL in the normalized gain, at the cost of 1.8 MB (without quantization) and no cost with quantization.