Biblio

Numerous measurement researches have been performed to discover the IPv4 network security issues by leveraging the fast Internet-wide scanning techniques. However, IPv6 brings the 128-bit address space and renders brute-force network scanning impractical. Although significant efforts have been dedicated to enumerating active IPv6 hosts, limited by technique efficiency and probing accuracy, large-scale empirical measurement studies under the increasing IPv6 networks are infeasible now. To fill this research gap, by leveraging the extensively adopted IPv6 address allocation strategy, we propose a novel IPv6 network periphery discovery approach. Specifically, XMap, a fast network scanner, is developed to find the periphery, such as a home router. We evaluate it on twelve prominent Internet service providers and harvest 52M active peripheries. Grounded on these found devices, we explore IPv6 network risks of the unintended exposed security services and the flawed traffic routing strategies. First, we demonstrate the unintended exposed security services in IPv6 networks, such as DNS, and HTTP, have become emerging security risks by analyzing 4.7M peripheries. Second, by inspecting the periphery's packet routing strategies, we present the flawed implementations of IPv6 routing protocol affecting 5.8M router devices. Attackers can exploit this common vulnerability to conduct effective routing loop attacks, inducing DoS to the ISP's and home routers with an amplification factor of \textbackslashtextbackslashgt 200. We responsibly disclose those issues to all involved vendors and ASes and discuss mitigation solutions. Our research results indicate that the security community should revisit IPv6 network strategies immediately.

In this paper, we propose a very fast, yet compact, AES S-box, by applying two techniques to a composite field \$GF((2ˆ4)ˆ2)\$ fast AES S-box. The composite field fast S-box has three main components, namely the input transformation matrix, the inversion circuit, and the output transformation matrix. The core inversion circuit computes the multiplicative inverse over the composite field \$GF((2ˆ4)ˆ2)\$ and consists of three arithmetic blocks over subfield \$GF(2ˆ4)\$, namely exponentiation, subfield inverter, and output multipliers. For the first technique, we consider multiplication of the input of the composite field fast S-box by 255 nonzero 8-bit binary field elements. The multiplication constant increases the variety of the input and output transformation matrices of the S-box by a factor of 255, hence increasing the search space of the logic minimization algorithm correspondingly. For the second technique, we reduce the delay of the composite field fast S-box, by combining the output multipliers and the output transformation matrix. Moreover, we modify the architecture of the input transformation matrix and re-design the exponentiation block and the subfield inverter for lower delay and area. We find that 8 unique binary transformation matrices could be used to change from the binary field \$GF(2ˆ8)\$ to the composite field \$GF((2ˆ4)ˆ2)\$ at the input of the composite field S-box. We use Matla \$\textbackslashtextbackslashmathbfb\$ ® to derive all \$(255\textbackslashtextbackslashtimes 8=2040)\$ new input transformation matrices. We search the matrices for the fastest and lowest complexity implementation and the minimal one is selected for the proposed fast S-box. The proposed fast S-box is 24% faster (with 5% increase in area) than the composite field fast design and 10% faster (with about 1% increase in area) than the fastest S-box available in the literature, to the best of our knowledge.

A comparative analysis of the classical and approximation methods of spectral analysis of fast-variable processes in technical systems is carried out. It is shown that the approximation methods make it possible to substantially remove the contradiction between the requirements for spectrum smoothing and its frequency resolution. On practical examples of vibroacoustic signals, the effectiveness of approximation methods is shown. The Prony method was used to process the time series. The interactive frequency segmentation method and the direct identification method were used for approximation and frequency characteristics.

Research has been conducted on wireless network single link failures. However, cascaded link failures due to fraudulent attacks have not received enough attention, whereas this requires solutions. This research developed an enhanced genetic algorithm (EGA) focused on capacity efficiency and fast restoration to rapidly resolve link-link failures. On complex nodes network, this fault-tolerant model was tested for such failures. Optimal alternative routes and the bandwidth required for quick rerouting of video traffic were generated by the proposed model. Increasing cascaded link failures increases bandwidth usage and causes transmission delay, which slows down video traffic routing. The proposed model outperformed popular Dijkstra models, in terms of time complexity. The survived solution paths demonstrate that the proposed model works well in maintaining connectivity despite cascaded link failures and would therefore be extremely useful in pandemic periods on emergency matters. The proposed technology is feasible for current business applications that require high-speed broadband networks.

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target's data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.

Modern Industrial Automation & Control Systems (IACS) are essential part of the critical infrastructures and services. They are used in health, power, water, and transportation systems, and the impact of cyberattacks on IACS could be severe, resulting, for example, in damage to the environment, public or employee safety or health. Thus, building IACS safe and secure against cyberattacks is extremely important. The attacker model is one of the key elements in risk assessment and other security related information system management tasks. The aim of the study is to specify the attacker's profile based on the analysis of network and system events. The paper presents an approach to the selection of attacker's profile attributes from raw network and system events of the Linux OS. To evaluate the approach the experiments were performed on data collected within the Global CPTC 2019 competition.

With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting “stealthy” attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.

Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craft a data observation whereby the data features purposefully trigger the classifier to yield incorrect responses. This has been observed in various image classification tasks, including falsifying road sign detection and facial recognition, which could have severe consequences in real-world deployment. In this work, we investigate how these attacks could impact on network traffic analysis, and how a system could perform misclassification of common network attacks such as DDoS attacks. Using the CICIDS2017 data, we examine how vulnerable the data features used for intrusion detection are to perturbation attacks using FGSM adversarial examples. As a result, our method provides a defensive approach for assessing feature robustness that seeks to balance between classification accuracy whilst minimising the attack surface of the feature space.

We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing the network traffic in order to distinguish the normal behaviour from the abnormal one. This approach is usually implemented in a central server where all the network traffic is analyzed which can rise privacy issues. In fact, with the increasing adoption of Cloud infrastructures, it is important to reduce as much as possible the outsourcing of such sensitive information to the several network nodes. A better approach is to ask each node to analyze its own data and then to exchange its learning finding (model) with a coordinator. In this paper, we investigate the application of federated learning for network-based intrusion detection. Our experiment was conducted based on the C ICIDS2017 dataset. We present a f ederated learning on a deep learning algorithm C NN based on model averaging. It is a self-learning system for detecting anomalies caused by malicious adversaries without human intervention and can cope with new and unknown attacks without decreasing performance. These experimentation demonstrate that this approach is effective in detecting intrusion.

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.

True random number generators (TRNG) are widely used to generate encryption keys in information security systems [1]–[2]. In TRNG, entropy source is a critical module who provides the source of randomness of output bit stream. The unavoidable electrical noise in circuit becomes an ideal entropy source due to its unpredictability. Among the methods of capturing electrical noise, ring oscillator-based entropy source makes the TRNG most robust to deterministic noise and 1/f noise which means the strongest anti-interference capability, so it is simple in structure and easy to integrate [3]. Thus, great research attention has focused on ring oscillator-based TRNGs [3] –[7]. In [4], a high-speed TRNG with 100Mbps output bit rate was proposed, but it took up too much power and area. A TRNG based on tetrahedral ring oscillator was proposed in [5]. Its power consumption was very low but the output bit rate was also very low. A ring oscillator-based TRNG with low output bit rate but high power was proposed in [7]. In a word, none of the above architectures achieve an appropriate compromise between bit rate and power consumption. This work presents a new feedback architecture of TRNG based on tetrahedral ring oscillator. The output random bit stream generates a relative random control voltage that acts on the transmission gates in oscillator through a feedback loop, thus increasing phase jitter of the oscillator and improving output bit rate. Furthermore, an XOR chain-based post-processing unit is added to eliminate the statistical deviations and correlations between raw bits.

Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions.

Third party mobile applications are dominating the business strategies of organisations and have become an integral part of personal life of individuals. These applications are used for financial transactions, sharing of sensitive data etc. The recent breaches in Android clearly indicate that use of third party applications have become a serious security threat. By design, Android framework keeps all these applications in untrusted domain. Due to this a common policy of resource control exists for all such applications. Further, user discretion in granting permissions to specific applications is not effective because users are not always aware of deep functionalities, mala fide intentions (in case of spywares) and bugs/flaws in these third-party applications. In this regard, we propose a security scheme to mitigate unauthorised access of resources by third party applications. Our proposed scheme is based on SEAndroid policies and achieves fine grained confinement with respect to access control for the third party applications. To the best of our knowledge, the proposed scheme is unique and first of its kind. The proposed scheme is integrated with Android Oreo 8.1.0 for performance and security analysis. It is compatible with any Android device with AOSP support.

Networked control design is essential to enable normal operation and further accomplish performance improvement of the cyber-physical systems. In this work, a resilient control scheme is presented for the networked nonlinear system under the denial-of-service (DoS) attack and the system uncertainty. Through synthesizing a self regulation system, this scheme is capable of releasing the prescribed performance when attack is active and recovering that in finite-time after the attack is slept. Meanwhile, the neural network is employed to approximate the system uncertainty. Particularly, the update law possesses the non-certainty-equivalent (NCE) structure, and then the impact of the DoS attack is totally isolated. Finally, the numerical simulation is presented to illustrate the effectiveness and benefits of the estimation scheme and the control design.

In this paper, we present FireBugs for Finding and Repairing Bugs based on security patterns. For the common misuse patterns of cryptography APIs (crypto APIs), we encode common cryptography rules into the pattern representations for bug detection and program repair regarding cryptography rule violations. In the evaluation, we conducted a case study to assess the bug detection capability by applying FireBugs to datasets mined from both open source and commercial projects. Also, we conducted a user study with professional software engineers at Mutual of Omaha Insurance Company to estimate the program repair capability. This evaluation showed that FireBugs can help professional engineers develop various cryptographic requirements in a resilient application.

IoT honeypots that mimic the behavior of IoT devices for threat analysis are becoming increasingly important. Existing honeypot systems use devices with a specific version of firmware installed to monitor cyber attacks. However, honeypots frequently receive requests targeting devices and firmware that are different from themselves. When honeypots return an error response to such a request, the attack is terminated, and the monitoring fails.To solve this problem, we introduce FirmPot, a framework that automatically generates intelligent-interaction honeypots using firmware. This framework has a firmware emulator optimized for honeypot generation and learns the behavior of embedded applications by using machine learning. The generated honeypots continue to interact with attackers by a mechanism that returns the best from the emulated responses to the attack request instead of an error response.We experimented on embedded web applications of wireless routers based on the open-source OpenWrt. As a result, our framework generated honeypots that mimicked the embedded web applications of eight vendors and ten different CPU architectures. Furthermore, our approach to the interaction improved the session length with attackers compared to existing ones.

This paper presents a management system for a fleet of autonomous mobile robots performing logistics in security-heterogeneous factories. Loading and unloading goods and parts between workstations in these dynamic environments often demands from the mobile robots to share space and resources such as corridors, interlocked security doors and elevators among themselves. This model explores a dynamic task scheduling and assignment to the robots taking into account their location, tasks previously assigned and battery levels, all the while being aware of the physical constraints of the installation. The benefits of the proposed architecture were validated through a set of experiments in a mockup of INCM's shop-floor environment. During these tests 3 robots operated continuously for several hours, self-charging without any human intervention.

Electromagnetic emissions associated with the transmission of automotive controller area network (CAN) messages within a passenger car have been analysed and used to reconstruct the original CAN messages. Concurrent monitoring of the CAN traffic via a wired connection to the vehicle OBD-II port was used to validate the effectiveness of the reconstruction process. These results confirm the feasibility of reconstructing in-vehicle network data for forensic purposes, without the need for wired access, at distances of up to 1 m from the vehicle by using magnetic field measurements, and up to 3 m using electric field measurements. This capability has applications in the identification and resolution of EMI issues in vehicle data network, as well as possible implications for automotive cybersecurity.

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.

In this current age, stakeholders exchange legal documents, as well as documents that are official, sensitive and confidential via digital channels[1]. To securely communicate information between stakeholders is not an easy task considering the intentional or unintentional changes and possible attacks that can occur during communication. This paper focuses on protecting and securing data by hiding the data using steganography techniques, after encrypting the data to avoid unauthorized changes or modification made by adversaries to the data through using the Simplified Data Encryption Technique. By leveraging on these two approaches, secret data security intensifies to two levels and a steganography image of high quality is attained. Cryptography converts plaintext into cipher text (unreadable text); whereas steganography is the technique of hiding secret messages in other messages. First encryption of data is done using the Simplified Data Encryption Standard (S-DES) algorithm after which the message encrypted is embedded in the cover image by means of the Least Significant Bit (LSB) approach.

Cyber-Physical systems (CPSs) are exposed to cyber- physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes of the systems.We define two probabilistic metrics to estimate the physical impact of attacks targeting cyber-physical systems formalised in terms of a probabilistic hybrid extension of Hennessy and Regan's Timed Process Language. Our impact metrics estimate the impact of cyber-physical attacks taking into account: (i) the severity of the inflicted damage in a given amount of time, and (ii) the probability that these attacks are actually accomplished, according to the dynamics of the system under attack. In doing so, we pay special attention to stealthy attacks, i. e., attacks that cannot be detected by intrusion detection systems. As further contribution, we show that, under precise conditions, our metrics allow us to estimate the impact of attacks targeting a complex CPS in a compositional way, i.e., in terms of the impact on its sub-systems.

The fifth-generation (5G) standard is the last telecommunication technology, widely considered to have the most important characteristics in the future network industry. The 5G system infrastructure contains three principle interfaces, each one follows a set of protocols defined by the 3rd Generation Partnership Project group (3GPP). For the next generation network, 3GPP specified two authentication methods systematized in two protocols namely 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP). Such protocols are provided to ensure the authentication between system entities. These two protocols are critical systems, thus their reliability and correctness must be guaranteed. In this paper, we aim to formally re-examine 5G-AKA protocol using micro Common Representation Language 2 (mCRL2) language to verify such a security protocol. The mCRL2 language and its associated toolset are formal tools used for modeling, validation, and verification of concurrent systems and protocols. In this context, the authentication protocol 5G-AKA model is built using Algebra of Communication Processes (ACP), its properties are specified using Modal mu-Calculus and the properties analysis exploits Model-Checker provided with mCRL2. Indeed, we propose a new mCRL2 model of 3GPP specification considering 5G-AKA protocol and we specify some properties that describe necessary requirements to evaluate the correctness of the protocol where the parsed properties of Deadlock Freedom, Reachability, Liveness and Safety are positively assessed.

Always-On Denial of Service (DoS) Trojans with power drain payload can be disastrous in systems where on-chip power resources are limited. These Trojans are designed so that they have no impact on system behavior and hence, harder to detect. A formal verification method is presented to detect sequential always-on DoS Trojans in pipelined circuits and pipelined microprocessors. Since the method is proof-based, it provides a 100% accurate classification of sequential Trojan components. Another benefit of the approach is that it does not require a reference model, which is one of the requirements of many Trojan detection techniques (often a bottleneck to practical application). The efficiency and scalability of the proposed method have been evaluated on 36 benchmark circuits. The most complex of these benchmarks has as many as 135,898 gates. Detection times are very efficient with a 100% rate of detection, i.e., all Trojan sequential elements were detected and all non-trojan sequential elements were classified as such.

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.

Today’s edge networks continue to see an increasing number of deployed IoT devices. These IoT devices aim to increase productivity and efficiency; however, they are plagued by a myriad of vulnerabilities. Industry and academia have proposed protecting these devices by deploying a “bolt-on” security gateway to these edge networks. The gateway applies security protections at the network level. While security gateways are an attractive solution, they raise a fundamental concern: Can the bolt-on security gateway be trusted? This paper identifies key challenges in realizing this goal and sketches a roadmap for providing trust in bolt-on edge IoT security gateways. Specifically, we show the promise of using a micro-hypervisor driven approach for delivering practical (deployable today) trust that is catered to both end-users and gateway vendors alike in terms of cost, generality, capabilities, and performance. We describe the challenges in establishing trust on today’s edge security gateways, formalize the adversary and trust properties, describe our system architecture, encode and prove our architecture trust properties using the Alloy formal modeling language. We foresee our trustworthy security gateway architecture becoming a practical and extensible formal foundation towards realizing robust trust properties on today’s edge security gateway implementations.