Biblio

This paper proposes a machine-learning-based advanced online dynamic security assessment (DSA) method, which provides a detailed evaluation of the system stability after a disturbance by predicting impending loss of synchronism (LOS) of generators. Voltage angles at generator buses are used as the features of the different random forest (RF) classifiers which are trained to consecutively predict LOS of the generators as a contingency proceeds and updated measurements become available. A wide range of contingencies for various topologies and operating conditions of the IEEE 118-bus system has been studied in offline analysis using the GE positive sequence load flow analysis (PSLF) software to create a comprehensive dataset for training and testing the RF models. The performances of the trained models are evaluated in the presence of measurement errors using various metrics. The results reveal that the trained models are accurate, fast, and robust to measurement errors.

This paper defines a set difference metric for comparing machine learning (ML) datasets and proposes the difference between datasets be a function of combinatorial coverage. We illustrate its utility for evaluating and predicting performance of ML models. Identifying and measuring differences between datasets is of significant value for ML problems, where the accuracy of the model is heavily dependent on the degree to which training data are sufficiently representative of data encountered in application. The method is illustrated for transfer learning without retraining, the problem of predicting performance of a model trained on one dataset and applied to another.

We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements.

The network output activation values for a given input can be employed to produce a sorted ranking. Adversarial attacks typically generate the least amount of perturbation required to change the classifier label. In that sense, generated adversarial attack perturbation only affects the output in the 1st sorted ranking position. We argue that meaningful information about the adversarial examples i.e., their original labels, is still encoded in the network output ranking and could potentially be extracted, using rule-based reasoning. To this end, we introduce a novel adversarial attack methodology inspired by the K-anonymity principles, that generates adversarial examples that are not only misclassified, but their output sorted ranking spreads uniformly along K different positions. Any additional perturbation arising from the strength of the proposed objectives, is regularized by a visual similarity-based term. Experimental results denote that the proposed approach achieves the optimization goals inspired by K-anonymity with reduced perturbation as well.

In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger patterns. Several approaches have been proposed to detect such attacks, but they make undesirable assumptions about the attack strategies or require direct access to the trained models, which restricts their utility in practice.This paper addresses these challenges by introducing a Meta Neural Trojan Detection (MNTD) pipeline that does not make assumptions on the attack strategies and only needs black-box access to models. The strategy is to train a meta-classifier that predicts whether a given target model is Trojaned. To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution. We then dynamically optimize a query set together with the meta-classifier to distinguish between Trojaned and benign models.We evaluate MNTD with experiments on vision, speech, tabular data and natural language text datasets, and against different Trojan attacks such as data poisoning attack, model manipulation attack, and latent attack. We show that MNTD achieves 97% detection AUC score and significantly outperforms existing detection approaches. In addition, MNTD generalizes well and achieves high detection performance against unforeseen attacks. We also propose a robust MNTD pipeline which achieves around 90% detection AUC even when the attacker aims to evade the detection with full knowledge of the system.

This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.

A wireless technology Mobile Ad hoc Network (MANET) that connects a group of mobile devices such as phones, laptops, and tablets suffers from critical security problems, so the traditional defense mechanism Intrusion Detection System (IDS) techniques are not sufficient to safeguard and protect MANET from malicious actions performed by intruders. Due to the MANET dynamic decentralized structure, distributed architecture, and rapid growing of MANET over years, vulnerable MANET does not need to change its infrastructure rather than using intelligent and advance methods to secure them and prevent intrusions. This paper focuses essentially on machine learning methodologies and algorithms to solve the shortage of the first line defense IDS to overcome the security issues MANET experience. Threads such as black hole, routing loops, network partition, selfishness, sleep deprivation, and denial of service (DoS), may be easily classified and recognized using machine learning methodologies and algorithms. Also, machine learning methodologies and algorithms help find ways to reduce and solve mischievous and harmful attacks against intimidation and prying. The paper describes few machine learning algorithms in detail such as Neural Networks, Support vector machine (SVM) algorithm and K-nearest neighbors, and how these methodologies help MANET to resolve their security problems.

In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.

‘Malicious software’ or malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. In the past, many solutions have proved ineffective and have resulted in many false positives. Having the ability to identify and classify malware will help prevent them from spreading and evolving. In this paper, we study the effectiveness of the proposed classification of the malware family using a pixel level as features. This study has implemented well-known machine learning and deep learning classifiers such as K-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree, and Random Forest. A binary file of 25 malware families is converted into a fixed grayscale image. The grayscale images were then extracted transforming the size 100x100 into a single format into 100000 columns. During this phase, none of the columns are removed as to remain the patterns in each malware family. The experimental results show that our approach achieved 92% accuracy in Random Forest, 88% in SVM, 81% in Decision Tree, 80% in k-NN and 56% in Naïve Bayes classifier. Overall, the pixel-based feature also reveals a promising technique for identifying the family of malware with great accuracy, especially using the Random Forest classifier.

Nowadays most of our data is stored on an electronic device. The risk of that device getting infected by Viruses, Malware, Worms, Trojan, Ransomware, or any unwanted invader has increased a lot these days. This is mainly because of easy access to the internet. Viruses and malware have evolved over time so identification of these files has become difficult. Not only by viruses and malware your device can be attacked by a click on forged URLs. Our proposed solution for this problem uses machine learning techniques and signature matching techniques. The main aim of our solution is to identify the malicious programs/URLs and act upon them. The core idea in identifying the malware is selecting the key features from the Portable Executable file headers using these features we trained a random forest model. This RF model will be used for scanning a file and determining if that file is malicious or not. For identification of the virus, we are using the signature matching technique which is used to match the MD5 hash of the file with the virus signature database containing the MD5 hash of the identified viruses and their families. To distinguish between benign and illegitimate URLs there is a logistic regression model used. The regression model uses a tokenizer for feature extraction from the URL that is to be classified. The tokenizer separates all the domains, sub-domains and separates the URLs on every `/'. Then a TfidfVectorizer (Term Frequency - Inverse Document Frequency) is used to convert the text into a weighted value. These values are used to predict if the URL is safe to visit or not. On the integration of all three modules, the final application will provide full system protection against malicious software.

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Wireless sensor networks (WSN) are used to perceive many data such as temperature, vibration, pressure in the environment and to produce results; it is widely used, including in critical fields such as military, intelligence and health. However, because of WSNs have different infrastructure and architecture than traditional networks, different security measures must be taken. In this study, an intrusion detection system (IDS) is modeled to ensure WSN security. Since the signature, misuse and anomaly based detection methods for intrusion detection systems are insufficient to provide security alone, a hybrid model is proposed in which these methods are used together. In the hybrid model, anomaly rules were defined for attack detection, and machine learning algorithms BayesNet, J48 and Random Forest were used to classify normal and abnormal traffic. Unlike the studies in the literature, CSE-CIC-IDS2018, the most up-to-date data set, was used to create attack profiles. Considering both hardware constraints and battery capacities of WSNs; the data was pre-processed in accordance with data mining principles. The results showed that the developed model has high accuracy and low false alarm rate.

Malware such as metamorphic virus changes its codes and it cannot be detected by pattern matching. Such malware can be detected by surface analysis, dynamic analysis or static analysis. We focused on surface analysis since neither virtual environments nor high level engineering is required. A representative method in surface analysis is n-gram with machine learning. On the other hand, important features are sometimes cut off by n-gram since n is not variable in some existing methods. Hence, scores of malware detection methods are not perfect. Moreover, creating n-gram features takes long time for comparing files. Furthermore, in some n-gram methods, invisible malware can be created when the methods are known to attackers. Therefore, we proposed a new malware subspecies detection method by suffix arrays and machine learning. We evaluated the method with four real malware subspecies families and succeeded to classify them with almost 100% accuracy.

6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account when applying the algorithms. While machine learning offers significant advantages for 6G, AI models’ security is normally ignored. Due to the many applications in the real world, security is a vital part of the algorithms. This paper proposes a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction using adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction. We also present the adversarial learning mitigation method’s performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model under attack are very close to the undefended model without attack.

One of the many methods for identifying malware is to disassemble the malware files and obtain the opcodes from them. Since malware have predominantly been found to contain specific opcode sequences in them, the presence of the same sequences in any incoming file or network content can be taken up as a possible malware identification scheme. Malware detection systems help us to understand more about ways on how malware attack a system and how it can be prevented. The proposed method analyses malware executable files with the help of opcode information by converting the incoming executable files to assembly language thereby extracting opcode information (opcode count) from the same. The opcode count is then converted into opcode frequency which is stored in a CSV file format. The CSV file is passed to various machine learning algorithms like Decision Tree Classifier, Random Forest Classifier and Naive Bayes Classifier. Random Forest Classifier produced the highest accuracy and hence the same model was used to predict whether an incoming file contains a potential malware or not.

As AI technology advances and its use increases, efforts to incorporate machine learning for malware detection are increasing. However, for malware learning, a standardized data set is required. Because malware is unstructured data, it cannot be directly learned. In order to solve this problem, many studies have attempted to convert unstructured data into structured data. In this study, the features and limitations of each were analyzed by investigating and analyzing the method of converting unstructured data proposed in each study into structured data. As a result, most of the data conversion techniques suggest conversion mechanisms, but the scope of each technique has not been determined. The resulting data set is not suitable for use as training data because it has infinite properties.

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.

To defend networks against security attacks, cyber defenders have to identify vulnerabilities that could be exploited by an attacker and fix them. However, vulnerabilities are constantly evolving and their number is rising. In addition, the resources required (i.e., time and cost) to patch all the identified vulnerabilities and update the affected assets are not always affordable. For these reasons, the defender needs to have a set of metrics that could be used to automatically map new discovered vulnerabilities to potential attack tactics. Using such a mapping to attack tactics, will allow security solutions to better respond inline to any vulnerabilities exploitation tentatives, by selecting and prioritizing suitable response strategy. In this work, we provide a multilabel classification approach to automatically map a detected vulnerability to the MITRE Adversarial Tactics that could be used by the attacker. The proposed approach will help cyber defenders to prioritize their defense strategies, ensure a rapid and efficient investigation process, and well manage new detected vulnerabilities. We evaluate a set of machine learning algorithms (BinaryRelevance, LabelPowerset, ClassifierChains, MLKNN, BRKNN, RAkELd, NLSP, and Neural Networks) and found out that ClassifierChains with RandomForest classifier is the best method in our experiment.

Network intrusion detection systems (NIDS) are widely used solutions targeting the security of any network device connected to the Internet and are taking the lead in the battle against intruders. This paper addresses the network security issues by implementing a hardware-based NIDS solution with a Naïve Bayes machine learning (ML) algorithm for classification using NSL Knowledge Discovery in Databases (KDD) dataset. The proposed FPGA implementation of the Naive Bayes classifier focuses on low latency and provides intrusion detection in just 240ns, with accuracy/precision of 70/97%, occupying 1 % of the Virtex7 VC709 FPGA chip area.

IoT networks today face a myriad of security vulnerabilities in their infrastructure due to its wide attack surface. Large-scale networks are increasingly adopting a Software-Defined Networking approach, it allows for simplified network control and management through network virtualization. Since traditional security mechanisms are incapable of handling virtualized environments, SDSec or Software-Defined Security is introduced as a solution to support virtualized infrastructure, specifically aimed at providing security solutions to SDN frameworks. To further aid large scale design and development of SDN frameworks, Model-Driven Engineering (MDE) has been proposed to be used at the design phase, since abstraction, automation and analysis are inherently key aspects of MDE. This provides an efficient approach to reducing large problems through models that abstract away the complex technicality of the total system. Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven security approach for SDSec networks that can self-adapt using machine learning to mitigate security threats. The overall design time changes can be monitored at run time through machine learning techniques (e.g. deep, reinforcement learning) for real time analysis. This approach can be tested in IoT simulation environments, for instance using the CAPS IoT modeling and simulation framework. Using self-adaptation of models and advanced machine learning for data analysis would ensure that the SDSec architecture adapts and improves over time. This largely reduces the overall attack surface to achieve improved end-to-end security in IoT environments.

In order to improve the big data management ability of IOT access control based on converged network structure, a security integration model of IOT access control based on machine learning and converged network structure is proposed. Combined with the feature analysis method, the storage structure allocation model is established, the feature extraction and fuzzy clustering analysis of big data are realized by using the spatial node rotation control, the fuzzy information fusion parameter analysis model is constructed, the frequency coupling parameter analysis is realized, the virtual inertia parameter analysis model is established, and the integrated processing of big data is realized according to the machine learning analysis results. The test results show that the method has good clustering effect, reduces the storage overhead, and improves the reliability management ability of big data.

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.

Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed.

Hardware counters are included in processors to count microarchitecture level events affecting performance. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. Embedded systems also have enhanced need for variable anomaly detection time in order to meet the system response time requirements. We propose a novel repurposing of Patt-Yeh two level branch predictor data structure for abstracting/hashing HW counter signatures to support such variable anomaly detection times. The proposed anomaly detection mechanism is evaluated on some generic benchmark programs and ArduPilot - a popular autopilot software. Experimental evaluation encompasses both Intel X86 and ARM Cortex M processors. DWT within Cortex M provides sufficiently interesting program level event counts to capture these control flow anomalies. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per anomaly check.