Biblio

Within a framework for verifying parametric network protocols through induction, one needs to find invariants based on a protocol instance of a small number of nodes. In this paper, we propose a new approach to accelerate parameterized verification by adopting decision trees to represent the state space of a protocol instance. Such trees can be considered as a knowledge base that summarizes all behaviors of the protocol instance. With this knowledge base, we are able to efficiently construct an oracle to effectively assess candidates of invariants of the protocol, which are suggested by an invariant finder. With the discovered invariants, a formal proof for the correctness of the protocol can be derived in the framework after proper generalization. The effectiveness of our method is demonstrated by experiments with typical benchmarks.

Online privacy policies are lengthy and hard to comprehend. To address this problem, researchers have utilized machine learning (ML) to devise tools that automatically summarize online privacy policies for web users. One such tool is our free and publicly available browser extension, PrivacyCheck. In this paper, we enhance PrivacyCheck by adding a competitor analysis component-a part of PrivacyCheck that recommends other organizations in the same market sector with better privacy policies. We also monitored the usage patterns of about a thousand actual PrivacyCheck users, the first work to track the usage and traffic of an ML-based privacy analysis tool. Results show: (1) there is a good number of privacy policy URLs checked repeatedly by the user base; (2) the users are particularly interested in privacy policies of software services; and (3) PrivacyCheck increased the number of times a user consults privacy policies by 80%. Our work demonstrates the potential of ML-based privacy analysis tools and also sheds light on how these tools are used in practice to give users actionable knowledge they can use to pro-actively protect their privacy.

In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.

Security vulnerabilities in firmware/software pose an important threat ton power grid security, and thus electric utility companies should quickly decide how to remediate vulnerabilities after they are discovered. Making remediation decisions is a challenging task in the electric industry due to the many factors to consider, the balance to maintain between patching and service reliability, and the large amount of vulnerabilities to deal with. Unfortunately, remediation decisions are current manually made which take a long time. This increases security risks and incurs high cost of vulnerability management. In this paper, we propose a machine learning-based automation framework to automate remediation decision analysis for electric utilities. We apply it to an electric utility and conduct extensive experiments over two real operation datasets obtained from the utility. Results show the high effectiveness of the solution.

Industrial Internet of Things (IIoT) has arisen as an emerging trend in the industrial sector. Millions of sensors present in IIoT networks generate a massive amount of data that can open the doors for several cyber-attacks. An intrusion detection system (IDS) monitors real-time internet traffic and identify the behavior and type of network attacks. In this paper, we presented a deep random neural (DRaNN) based scheme for intrusion detection in IIoT. The proposed scheme is evaluated by using a new generation IIoT security dataset UNSW-NB15. Experimental results prove that the proposed model successfully classified nine different types of attacks with a low false-positive rate and great accuracy of 99.54%. To validate the feasibility of the proposed scheme, experimental results are also compared with state-of-the-art deep learning-based intrusion detection schemes. The proposed model achieved a higher attack detection rate of 99.41%.

Applying machine learning techniques to detect malicious encrypted network traffic has become a challenging research topic. Traditional approaches based on studying network patterns fail to operate on encrypted data, especially without compromising the integrity of encryption. In addition, the requirement of rendering network-wide intelligent protection in a timely manner further exacerbates the problem. In this paper, we propose to leverage ×86 multicore platforms provisioned at enterprises' network edge with the software accelerators to design an encrypted traffic analytics (ETA) system with accelerated speed. Specifically, we explore a suite of data features and machine learning models with an open dataset. Then we show that by using Intel DAAL and OpenVINO libraries in model training and inference, we are able to reduce the training and inference time by a maximum order of 31× and 46× respectively while retaining the model accuracy.

In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous mal ware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and linkable format when the training and test are done on the same platform. Meanwhile, it also achieves a 96% accuracy when training is carried out on a few popular IoT platforms but test is done on different platforms. Efficient malware prevention and mitigation solutions can be enabled based on the proposed method to prevent and mitigate IoT malware damages across different platforms.

With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems. Nowadays, attackers generally design polymeric malware [1], it is usually a type of malware [2] that continuously changes its recognizable feature to fool detection techniques that uses typical signature based methods [3]. That is why the need for Machine Learning based detection arises. In this work, we are going to obtain behavioral-pattern that may be achieved through static or dynamic analysis, afterward we can apply dissimilar ML techniques to identify whether it's malware or not. Behavioral based Detection methods [4] will be discussed to take advantage from ML algorithms so as to frame social-based malware recognition and classification model.

In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.

In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy ($\approx$0.9167) on the dataset provided by VirusTotal.

Malicious software (malware) is designed to cause unwanted or destructive effects on computers. Since modern society is dependent on computers to function, malware has the potential to do untold damage. Therefore, developing techniques to effectively combat malware is critical. With the rise in popularity of polymorphic malware, conventional anti-malware techniques fail to keep up with the rate of emergence of new malware. This poses a major challenge towards developing an efficient and robust malware detection technique. One approach to overcoming this challenge is to classify new malware among families of known malware. Several machine learning methods have been proposed for solving the malware classification problem. However, these techniques rely on hand-engineered features extracted from malware data which may not be effective for classifying new malware. Deep learning models have shown paramount success for solving various classification tasks such as image and text classification. Recent deep learning techniques are capable of extracting features directly from the input data. Consequently, this paper proposes an end-to-end deep learning framework for multimodels (henceforth, multimodel learning) to solve the challenging malware classification problem. The proposed model utilizes three different deep neural network architectures to jointly learn meaningful features from different attributes of the malware data. End-to-end learning optimizes all processing steps simultaneously, which improves model accuracy and generalizability. The performance of the model is tested with the widely used and publicly available Microsoft Malware Challenge Dataset and is compared with the state-of-the-art deep learning-based malware classification pipeline. Our results suggest that the proposed model achieves comparable performance to the state-of-the-art methods while offering faster training using end-to-end multimodel learning.

Malware classification remains at the forefront of ongoing research as the prevalence of metamorphic malware introduces new challenges to anti-virus vendors and firms alike. One approach to malware classification is Static Analysis - a form of analysis which does not require malware to be executed before classification can be performed. For this reason, a lightweight classifier based on the features of a malware binary is preferred, with relatively low computational overhead. In this work a modified convolutional neural network (CNN) architecture was deployed which integrated a complexity-based evaluation based on box-counting. This was implemented by setting up max-pooling layers in parallel, and then extracting the fractal dimension using a polyscalar relationship based on the resolution of the measurement scale and the number of elements of a malware image covered in the measurement under consideration. To test the robustness and efficacy of our approach we trained and tested on over 9300 malware binaries from 25 unique malware families. This work was compared to other award-winning image recognition models, and results showed categorical accuracy in excess of 96.54%.

Honeypots are the information system resources used for capturing and analysis of cyber attacks. Highinteraction Honeypots are capable of capturing attacks in their totality and hence are an ideal choice for capturing multi-stage cyber attacks. The term multi-stage attack is an abstraction that refers to a class of cyber attacks consisting of multiple attack stages. These attack stages are executed either by malicious codes, scripts or sometimes even inbuilt system tools. In the work presented in this paper we have proposed a framework for capturing, analysis and modelling of multi-stage cyber attacks. The objective of our work is to devise an effective mechanism for the classification of multi-stage cyber attacks. The proposed framework comprise of a network of high interaction honeypots augmented with an attack analysis engine. The analysis engine performs rule based labeling of captured honeypot data. The labeling engine labels the attack data as generic events. These events are further fused to generate attack graphs. The hence generated attack graphs are used to characterize and later classify the multi-stage cyber attacks.

A large amount of malware families use the domain generation algorithms (DGA) to randomly generate a large amount of domain names. It is a good way to bypass conventional blacklists of domain names, because we cannot predict which of the randomly generated domain names are selected for command and control (C&C) communications. An effective approach for detecting known DGA families is to investigate the malware with reverse engineering to find the adopted generation algorithms. As reverse engineering cannot handle the variants of DGA families, some researches leverage supervised learning to find new variants. However, the explainability of supervised learning is low and cannot find previously unseen DGA families. In this paper, we propose a graph-based semi-supervised learning scheme to track the evolution of known DGA families and find previously unseen DGA families. With a domain relation graph, we can clearly figure out how new variants relate to known DGA domain names, which induces better explainability. We deployed the proposed scheme on real network scenarios and show that the proposed scheme can not only comprehensively and precisely find known DGA families, but also can find new DGA families which have not seen before.

Malware is a kind of software that, if installed on a malware victim's device, might carry malicious actions. The malicious actions might be data theft, system failure, or denial of service. Malware analysis is a process to identify whether a piece of software is a malware or not. However, with the advancement of malware technologies, there are several evasion techniques that could be implemented by malware developers to prevent analysis, such as polymorphic and oligomorphic. Therefore, this research proposes an automatic malware detection system. In the system, the malware characteristics data were obtained through both static and dynamic analysis processes. Data from the analysis process were classified using Naive Bayes algorithm to identify whether the software is a malware or not. The process of identifying malware and benign files using the Naive Bayes machine learning method has an accuracy value of 93 percent for the detection process using static characteristics and 85 percent for detection through dynamic characteristics.

In this era, where the volume and diversity of malware is rising exponentially, new techniques need to be employed for faster and accurate identification of the malwares. Manual heuristic inspection of malware analysis are neither effective in detecting new malware, nor efficient as they fail to keep up with the high spreading rate of malware. Machine learning approaches have therefore gained momentum. They have been used to automate static and dynamic analysis investigation where malware having similar behavior are clustered together, and based on the proximity unknown malwares get classified to their respective families. Although many such research efforts have been conducted where data-mining and machine-learning techniques have been applied, in this paper we show how the accuracy can further be improved using deep learning networks. As deep learning offers superior classification by constructing neural networks with a higher number of potentially diverse layers it leads to improvement in automatic detection and classification of the malware variants.In this research, we present a framework which extracts various feature-sets such as system calls, operational codes, sections, and byte codes from the malware files. In the experimental and result section, we compare the accuracy obtained from each of these features and demonstrate that feature vector for system calls yields the highest accuracy. The paper concludes by showing how deep learning approach performs better than the traditional shallow machine learning approaches.

Signature-based malware detection is not always effective at detecting polymorphic variants of known malware. Malware signatures are devised to counter known threats, which also limits efficacy against new forms of malware. However, existing signatures do present the ability to classify malware based upon known malicious behavior which occurs on a victim computer. In this paper we present a method of classifying malware by family type through behavioral analysis, where the frequency of system function calls is used to fingerprint the actions of specific malware families. This in turn allows us to demonstrate a machine learning classifier which is capable of distinguishing malware by family affiliation with high accuracy.

Network traffic attacks are increasingly common and varied, this is a big problem especially when the target network is centralized. The creation of IDS (Intrusion Detection Systems) capable of detecting various types of attacks is necessary. Machine learning algorithms are widely used in the classification of data, bringing a good result in the area of computer networks. In addition, the analysis of entropy and distance between data sets are also very effective in detecting anomalies. However, each technique has its limitations, so this work aims to study their combination in order to improve their performance and create a new intrusion detection system capable of well detect some of the most common attacks. Reliability indices will be used as metrics to the combination decision and they will be updated in each new dataset according to the decision made earlier.

Strong physical unclonable function (PUF) is a promising lightweight hardware security primitive for device authentication. However, it is vulnerable to machine learning attacks. This paper demonstrates that even a recently proposed dual-mode PUF is still can be broken. In order to improve the security, this paper proposes a highly flexible machine learning resistant configurable tristate (CT) PUF which utilizes the response generated in the working state of Arbiter PUF to XOR the challenge input and response output of other two working states (ring oscillator (RO) PUF and bitable ring (BR) PUF). The proposed CT PUF is implemented on Xilinx Artix-7 FPGAs and the experiment results show that the modeling accuracy of logistic regression and artificial neural network is reduced to the mid-50%.

In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best.

Internet of Things (IoT) combines the internet and physical objects to transfer information among the objects. In the emerging IoT networks, providing security is the major issue. IoT device is exposed to various security issues due to its low computational efficiency. In recent years, the Intrusion Detection System valuable tool deployed to secure the information in the network. This article exposes the Intrusion Detection System (IDS) based on deep learning and machine learning to overcome the security attacks in IoT networks. Long Short-Term Memory (LSTM) and K-Nearest Neighbor (KNN) are used in the attack detection model and performances of those algorithms are compared with each other based on detection time, kappa statistic, geometric mean, and sensitivity. The effectiveness of the developed IDS is evaluated by using Bot-IoT datasets.

In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now-a-days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber-attacks. For protecting the data's we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber-attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.

To harness the value of data generated from IoT, there is a crucial requirement of new mechanisms. Machine learning (ML) is among the most suitable paradigms of computation which embeds strong intelligence within IoT devices. Various ML techniques are being widely utilised for improving network security in IoT. These techniques include reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. This report aims to critically analyse the role played by supervised and unsupervised ML for the enhancement of IoT security.

With the continuous growth of Internet technology and the increasingly broadening applications of The Internet, network security incidents as well as cyber-attacks are also showing a growing trend. Consequently, computer network security is becoming increasingly important. TCP firewall is a computer network security system, and it allows or denies the transmission of data according to specific rules for providing security for the computer network. Traditional firewalls rely on network administrators to set security rules for them, and network administrators sometimes need to choose to allow and deny packets to keep computer networks secure. However, due to the huge amount of data on the Internet, network administrators have a huge task. Therefore, it is particularly important to solve this problem by using the machine learning method of computer technology. This study aims to predict TCP security action based on the TCP transmission characteristics dataset provided by UCI machine learning repository by implementing machine learning models such as neural network, support vector machine (SVM), AdaBoost, and Logistic regression. Processes including evaluating various models and interpretability analysis. By utilizing the idea of ensemble-learning, the final result has an accuracy score of over 98%.

Cyber security is becoming a vital part of many information technologies and computing systems. Increasingly, High-Performance Computing systems are used in scientific research, academia and industry. High-Performance Computing applications are specifically designed to take advantage of the parallel nature of High-Performance Computing systems. Current research into High-Performance Computing systems focuses on the improvements in software development, parallel algorithms and computer systems architecture. However, there are no significant efforts in developing common High-Performance Computing security standards. Security of the High-Performance Computing resources is often an add-on to existing varied institutional policies that do not take into account additional requirements for High-Performance Computing security. Also, the users' terminals or portals used to access the High-Performance Computing resources are frequently insecure or they are being used in unprotected networks. In this paper we present Bearicade - a Data-driven Security Orchestration Automation and Response system. Bearicade collects data from the HPC systems and its users, enabling the use of Machine Learning based solutions to address current security issues in the High-Performance Computing systems. The system security is achieved through monitoring, analysis and interpretation of data such as users' activity, server requests, devices used and geographic locations. Any anomaly in users' behaviour is detected using machine learning algorithms, and would be visible to system administrators to help mediate the threats. The system was tested on a university campus grid system by administrators and users. Two case studies, Anomaly detection of user behaviour and Classification of Malicious Linux Terminal Command, have demonstrated machine learning approaches in identifying potential security threats. Bearicade's data was used in the experiments. The results demonstrated that detailed information is provided to the HPC administrators to detect possible security attacks and to act promptly.