Biblio

In recent times, the world has seen a tremendous increase in the number of attacks on IoT devices. A majority of these attacks have been botnet attacks, where an army of compromised IoT devices is used to launch DDoS attacks on targeted systems. In this paper, we study how the choice of a dataset and the extracted features determine the performance of a Machine Learning model, given the task of classifying Linux Binaries (ELFs) as being benign or malicious. Our work focuses on Linux systems since embedded Linux is the more popular choice for building today’s IoT devices and systems. We propose using 4 different types of files as the dataset for any ML model. These include system files, IoT application files, IoT botnet files and general malware files. Further, we propose using static, dynamic as well as network features to do the classification task. We show that existing methods leave out one or the other features, or file types and hence, our model outperforms them in terms of accuracy in detecting these files. While enhancing the dataset adds to the robustness of a model, utilizing all 3 types of features decreases the false positive and false negative rates non-trivially. We employ an exhaustive scenario based method for evaluating a ML model and show the importance of including each of the proposed files in a dataset. We also analyze the features and try to explain their importance for a model, using observed trends in different benign and malicious files. We perform feature extraction using the open source Limon sandbox, which prior to this work has been tested only on Ubuntu 14. We installed and configured it for Ubuntu 18, the documentation of which has been shared on Github.

Modern technologies for searching viruses, cloud-edge computing, and also federated algorithms and machine learning architectures are shown. The architectures for searching malware based on the xor metric applied in the design and test of computing systems are proposed. A Federated ML method is proposed for searching for malware, which significantly speeds up learning without the private big data of users. A federated infrastructure of cloud-edge computing is described. The use of signature analysis and the assertion engine for searching malware is shown. The paradigm of LTF-computing for searching destructive components in software applications is proposed.

In this article we have developed a simulation model in the Mininet environment for analyzing the operation of a software-defined network (SDN) in cloud data centers. The results of the simulation model of the operation of the SDN network on the Mininet emulator and the results of the simulation of the traditional network in the Graphical Network Simulator 3 emulator are presented.

When a fault occurs in power grid, the analytic model for power grid fault diagnosis could generate multiple solutions under one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or one or more their alarm information failing. Hence, this paper, calling the electrical quantities, presents an optimal solution discrimination method, which determines the optimal solution by constructing the electrical criteria of suspicious faulty components. Furthermore, combining the established electrical criteria with the existing analytic model, a hierarchical fault diagnosis mode is proposed. It uses the analytic model for the first level diagnosis based on the switching quantities. Thereafter, aiming at multiple solutions, it applies the electrical criteria for the second level diagnosis to determine the diagnostic result. Finally, the examples of fault diagnosis demonstrate the feasibility and effectiveness of the developed method.

This paper presents a method to improve the diagnosability of power network under multiple faults. In this paper, the steps of fault diagnosis are as follows: first, constructing finite automata model of the diagnostic system; then, a fault diagnoser model is established through coupling operation and trajectory reasoning mechanism; finally, the diagnosis results are obtained through this model. In this paper, the judgment basis of diagnosability is defined. Then, based on the existing diagnosis results, the information available can be increased by adding sensor devices, to achieve the purpose of diagnosability in the case of multiple faults of the system.

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.

Reconfigurable Intelligent Surface (RIS) is one of the latest technologies in bringing a certain amount of control to the rather unpredictable and uncontrollable wireless channel. In this paper, RIS is introduced in a bidirectional system with two source nodes and a Decode and Forward (DF) relay node. It is assumed that there is no direct path between the source nodes. The relay node receives information from source nodes simultaneously. The Physical Layer Network Coding (PLNC) is applied at the relay node to assist in the exchange of information between the source nodes. Analytical expressions are derived for the average probability of errors at the source nodes and relay node of the proposed RIS-assisted bidirectional relay system. The Bit Error Rate (BER) performance is analyzed using both simulation and analytical forms. It is observed that RIS-assisted PLNC based bidirectional relay system performs better than the conventional PLNC based bidirectional system.

The 5G technology brings the substantial improvement on the quality of services (QoS), such as higher throughput, lower latency, more stable signal and more ultra-reliable data transmission, triggering a revolution for the wireless mobile network. But in a general traffic channel in the 5G-based wireless mobile network, an attacker can detect a message transmitted over a channel, or even worse, forge or tamper with the message. Building a secure channel over the two parties is a feasible solution to this uttermost data transmission security challenge in 5G-based wireless mobile network. However, how to authentication the identities of the both parties before establishing the secure channel to fully ensure the data confidentiality and integrity during the data transmission has still been a open issue. To establish a fully secure channel, in this paper, we propose a strongly secure pairing-free certificateless authenticated key agreement (PF-CL-AKA) protocol with two-way identity-based authentication before extracting the secure session key. Our protocol is provably secure in the Lippold model, which means our protocol is still secure as long as each party of the channel has at least one uncompromised partial private term. Finally, By the theoretical analysis and simulation experiments, we can observe that our scheme is practical for the real-world applications in the 5G-based wireless mobile network.

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.

Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.

Campus Companies and network operators perform risk assessment to inform policy-making, guide infrastructure investments or to comply with security standards such as ISO 27001. Due to the size and complexity of these networks, risk assessment techniques such as attack graphs or trees describe the attacker with a finite set of rules. This characterization of the attacker can easily miss attack vectors or overstate them, potentially leading to incorrect risk estimation. In this work, we propose the first methodology to justify a rule-based attacker model. Conceptually, we add another layer of abstraction on top of the symbolic model of cryptography, which reasons about protocols and abstracts cryptographic primitives. This new layer reasons about Internet-scale networks and abstracts protocols.We show, in general, how the soundness and completeness of a rule-based model can be ensured by verifying trace properties, linking soundness to safety properties and completeness to liveness properties. We then demonstrate the approach for a recently proposed threat model that quantifies the confidentiality of email communication on the Internet, including DNS, DNSSEC, and SMTP. Using off-the-shelf protocol verification tools, we discover two flaws in their threat model. After fixing them, we show that it provides symbolic soundness.

The advent of 5G, one of the most recent and promising technologies currently under deployment, fulfills the emerging needs of mobile subscribers by introducing several new technological advancements. However, this may lead to numerous attacks in the emerging 5G networks. Thus, to guarantee the secure transmission of user data, 5G Authentication protocols such as Extensible Authentication Protocol - Authenticated Key Agreement Protocol (EAP-AKA) were developed. These protocols play an important role in ensuring security to the users as well as their data. However, there exists no guarantees about the security of the protocols. Thus formal verification is necessary to ensure that the authentication protocols are devoid of vulnerabilities or security loopholes. Towards this goal, we formally verify the security of the 5G EAP-AKA protocol using an automated verification tool called ProVerif. ProVerif identifies traces of attacks and checks for security loopholes that can be accessed by the attackers. In addition, we model the complete architecture of the 5G EAP-AKA protocol using the language called typed pi-calculus and analyze the protocol architecture through symbolic model checking. Our analysis shows that some cryptographic parameters in the architecture can be accessed by the attackers which cause the corresponding security properties to be violated.

UPI (Unified Payments Interface) is a framework in India wherein customers can send payments to merchants from their smartphones. The framework consists of UPI servers that are connected to the banks at the sender and receiver ends. To send and receive payments, customers and merchants would have to first register themselves with UPI servers by executing a registration protocol using payment apps such as BHIM, PayTm, Google Pay, and PhonePe. Weaknesses were recently reported on these protocols that allow attackers to make money transfers on behalf of innocent customers and even empty their bank accounts. But the reported weaknesses were found after informal and manual analysis. However, as history has shown, formal analysis of cryptographic protocols often reveals flaws that could not be discovered with manual inspection. In this paper, we model UPI protocols in the pattern of traditional cryptographic protocols such that they can be rigorously studied and analyzed using formal methods. The modeling simplifies many of the complexities in the protocols, making it suitable to analyze and verify UPI protocols with popular analysis and verification tools such as the Constraint Solver, ProVerif and Tamarin. Our modeling could also be used as a general framework to analyze and verify many other financial payment protocols than just UPI protocols, giving it a broader applicability.

In this work a time evolution model is used to help categorize malicious samples. This method can be used in anti-malware testing procedures as well as in detecting cyber-attacks. The time evolution mathematical model can help security experts to better understand the behaviour of malware attacks and malware families. It can be used for estimating much better their spreading and for planning the required defence actions against them. The basic time dependent variable of this model is the Ratio of the malicious files within an investigated time window. To estimate the main characteristics of the time series describing the change of the Ratio values related to a specific malicious file, nonlinear, exponential curve fitting method is used. The free parameters of the model were determined by numerical searching algorithms. The three parameters can be used in the information security field to describe more precisely the behaviour of a piece of malware and a family of malware as well. In the case of malware families, the aggregation of these parameters can provide effective solution for estimating the cyberthreat trends.

With the increasing complexity, modern system-onchip (SoC) designs are becoming more susceptible to security attacks and require comprehensive security assurance. However, establishing a comprehensive assurance for security often involves knowledge of relevant security assets. Since modern SoCs contain myriad confidential assets, the identification of security assets is not straightforward. The number and types of assets change due to numerous embedded hardware blocks within the SoC and their complex interactions. Some security assets are easily identifiable because of their distinct characteristics and unique definitions, while others remain in the blind-spot during design and verification and can be utilized as potential attack surfaces to violate confidentiality, integrity, and availability of the SoC. Therefore, it is essential to automatically identify security assets in an SoC at pre-silicon design stages to protect them and prevent potential attacks. In this paper, we propose an automated CAD framework called SAF to identify an SoC's security assets at the register transfer level (RTL) through comprehensive vulnerability analysis under different threat models. Moreover, we develop and incorporate metrics with SAF to quantitatively assess multiple vulnerabilities for the identified security assets. We demonstrate the effectiveness of SAF on MSP430 micro-controller and CEP SoC benchmarks. Our experimental results show that SAF can successfully and automatically identify an SoC's most vulnerable underlying security assets for protection.

Anonymous Communication Networks (ACNs) are networks in which, beyond data confidentiality, also traffic flow confidentiality is provided. The most popular routing approach for ACNs also used in practice is Onion. Onion is based on multiple encryption wrapping combined with the proxy mechanism (relay nodes). However, it offers neither sender anonymity nor recipient anonymity in a global passive adversary model, simply because the adversary can observe (at the first relay node) the traffic coming from the sender, and (at the last relay node) the traffic delivered to the recipient. This may also cause a loss of relationship anonymity if timing attacks are performed. This paper presents Onion-Ring, a routing protocol that improves anonymity of Onion in the global adversary model, by achieving sender anonymity and recipient anonymity, and thus relationship anonymity.

In recent years, cyber security has become a challenging task to protect the networks and computing systems from various types of digital attacks. Therefore, to preserve these systems, various innovative methods have been reported and implemented in practice. However, still more research work needs to be carried out to have malware free computing system. In this paper, an attempt has been made to develop simple but reliable ML based malware detection systems which can be implemented in practice. Keeping this in view, the present paper has proposed and compared the performance of three ML based malware detection systems applicable for computer systems. The proposed methods include k-NN, RF and LR for detection purpose and the features extracted comprise of Byte and ASM. The performance obtained from the simulation study of the proposed schemes has been evaluated in terms of ROC, Log loss plot, accuracy, precision, recall, specificity, sensitivity and F1-score. The analysis of the various results clearly demonstrates that the RF based malware detection scheme outperforms the model based on k-NN and LR The efficiency of detection of proposed ML models is either same or comparable to deep learning-based methods.

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.

Content-centric networking is emerging as a credible alternative to host-centric networking, especially in scenarios of large-scale content distribution and where privacy requirements are crucial. Recently, research on content-centric networking has focused on security aspects and proposed solutions aimed to protect the network from attacks targeting the content delivery protocols. Content-centric networks are based on the strong assumption of being able to access genuine content from genuine nodes, which is however unrealistic and could open the door to disruptive attacks. Network node misbehavior, either due to poisoning attacks or malfunctioning, can act as a persistent threat that goes unnoticed and causes dangerous consequences. In this paper, we propose a novel certification methodology for content-centric networks that improves transparency and increases trustworthiness of the network and its nodes. The proposed approach builds on behavioral analysis and implements a continuous certification process that collects evidence from the network nodes and verifies their non-functional properties using a rule-based inference model. Utility, performance, and soundness of our approach have been experimentally evaluated on a simulated Named Data Networking (NDN) network targeting properties availability, integrity, and non-repudiation.

In recent years, there have been situations in which the zero-sequence protection of the transformer has been incorrectly operated due to the converter transformer energizing or fault recovery. For converter transformers, maloperation may also occur. However, there is almost no theoretical research on the zero-mode inrush currents of converter transformers. This paper studies the characteristics of the zero-mode inrush currents of the converter transformers, including the relationship between the amplitude and attenuation characteristics of the zero-mode inrush currents of converter transformers, and their relationship with the system resistance, remanence, and closing angle. First, based on the T-type equivalent circuit of the transformer, the equivalent circuit of the zero-mode inrush current of each transformer is obtained. On this basis, the amplitude relationship of the zero-mode inrush currents of different converter transformers is obtained: the zero-mode inrush current of the energizing pole YY transformer becomes larger than the YD transformer, the energized pole YD becomes greater than the YY transformer, and the YY transformer zero-mode inrush current rises from 0. It is also analyzed that the sympathetic interaction will make the attenuation of the converter transformer zero-mode inrush current slower. The system resistance mainly affects the initial attenuation speed, and the later attenuation speed is mainly determined by the converter transformer leakage reactance. Finally, PSCAD modeling and simulation are carried out to verify the accuracy of the theoretical analysis.

In a socio-technically connected environment, self-adaptive systems need to cooperate with others to collect information to provide context-dependent functionalities to users. A key component of ensuring safe and secure cooperation is finding trustworthy information and its providers. Trust is an emerging quality attribute that represents the level of belief in the cooperative environments and serves as a promising solution in this regard. In this research, we will focus on analyzing trust characteristics and defining trust-aware models through the trust-aware goal model and the provenance model. The trust-aware goal model is designed to represent the trust-related requirements and their relationships. The provenance model is analyzed as trust evidence to be used for the trust evaluation. The proposed approach contributes to build a comprehensive understanding of trust and design a trust-aware self-adaptive system. In order to show the feasibility of the proposed approach, we will conduct a case study with the crowd navigation system for an unmanned vehicle system.

The Robot Operation System (ROS) is widely used in academia as well as the industry to build custom robot applications. Successful cyberattacks on robots can result in a loss of control for the legitimate operator and thus have a severe impact on safety if the robot is moving uncontrollably. A high level of security thus needs to be mandatory. Neither ROS 1 nor 2 in their default configuration provide protection against network based attackers. Multiple protection mechanisms have been proposed that can be used to overcome this. Unfortunately, it is unclear how effective and usable each of them are. We provide a structured analysis of the requirements these protection mechanisms need to fulfill by identifying realistic, network based attacker models and using those to derive relevant security requirements and other evaluation criteria. Based on these criteria, we analyze the protection mechanisms available and compare them to each other. We find that none of the existing protection mechanisms fulfill all of the security requirements. For both ROS 1 and 2, we discuss which protection mechanism are most relevant and give hints on how to decide on one. We hope that the requirements we identify simplify the development or enhancement of protection mechanisms that cover all aspects of ROS and that our comparison helps robot operators to choose an adequate protection mechanism for their use case.

It is well-known, and often a topic of heated debates, that programs in some programming languages are more concise than in others. This is a relevant factor when comparing or aggregating volume-impacted metrics on source code written in a combination of programming languages. In this paper, we present a model for measuring the conciseness of programming languages in a consistent, objective and evidence-based way. We present the approach, explain how it is founded on information theoretical principles, present detailed analysis steps and show the quantitative results of applying this model to a large benchmark of diverse commercial software applications. We demonstrate that our metric for language conciseness is strongly correlated with both an alternative analytical approach, and with a large scale developer survey, and show how its results can be applied to improve software metrics for multi-language applications.