Biblio

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.

This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise.

The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).

The Ad-hoc vehicle networks (VANETs) recently stressed communications and networking technologies. VANETs vary from MANETs in tasks, obstacles, system architecture and operation. Smart vehicles and RSUs communicate through unsafe wireless media. By nature, they are vulnerable to threats that can lead to life-threatening circumstances. Due to potentially bad impacts, security measures are needed to recognize these VANET assaults. In this review paper of VANET security, the new VANET approaches are summarized by addressing security complexities. Second, we're reviewing these possible threats and literature recognition mechanisms. Finally, the attacks and their effects are identified and clarified and the responses addressed together.

Cryptographic protocols are utilized for establishing a secure session between “honest” agents which communicate strictly according to the protocol rules as well as for ensuring the authenticated and confidential transmission of messages. The specification of a cryptographic protocol is usually presented as a set of requirements for the sequences of transmitted messages including the format of such messages. Note that protocol can describe several execution scenarios. All these requirements lead to a huge formal specification for a real cryptographic protocol and therefore, it is difficult to verify the security of the whole cryptographic protocol at once. In this paper, to overcome this problem, we suggest verifying the protocol security for its fragments. Namely, we verify the security properties for a special set of so-called traces of the cryptographic protocol. Intuitively, a trace of the cryptographic protocol is a sequence of computations, value checks, and transmissions on the sides of “honest” agents permitted by the protocol. In order to choose such set of traces, we introduce an Adversary model and the notion of a similarity relation for traces. We then verify the security properties of selected traces with Tamarin Prover. Experimental results for the EAP and Noise protocols clearly show that this approach can be promising for automatic verification of large protocols.

Underwater communication is vital for a variety of defence and scientific purposes. Current optical and sonar based carriers can deliver high capacity data rates, but their range and reliability is hampered by heavy propagation loss. A vertical Molecular Communication via Buoyancy (MCvB) channel is experimentally investigated here, where the dominant propagation force is buoyancy. Sequential puffs representing modulated symbols are injected and after the initial loss of momentum, the signal is driven by buoyancy forces which apply to both upwards and downwards channels. Coupled with the complex interaction of turbulent and viscous diffusion, we experimentally demonstrate that sequential symbols exhibit a Gaussian velocity spatial distribution. Our experimental results use Particle Image Velocimetry (PIV) to trace molecular clusters and infer statistical characteristics of their velocity profile. We believe our experimental paper's results can be the basis for long range underwater vertical communication between a deep sea vehicle and a surface buoy, establishing a covert and reliable delay-tolerant data link. The statistical distribution found in this paper is akin to the antenna pattern and the knowledge can be used to improve physical security.

To secure a guest operating system running on a virtual machine (VM), a monitoring method using hardware breakpoints by a virtual machine monitor is required. However, debug registers are visible to guest operating systems; thus, malicious programs on a guest operating system can detect or disable the monitoring method. This paper presents a method to hide access to debug registers from programs running on a VM. Our proposed method detects programs' access to debug registers and disguises the access as having succeeded. The register's actual value is not visible or modifiable to programs, so the monitoring method is hidden. This paper presents the basic design and evaluation results of our method.

Software is playing a pivotal role in most enterprises, whether they realize it or not, and with the proliferation of Industrial Internet of Things (IoT) and other cyber/physical systems across our society and critical infrastructure and our collective love affair with automation, optimization, and ``smart'' devices, the role of these types of systems is only going to increase. This talk addresses the myriad of issues that underlie unsafe, insecure, and unreliable software and provides the insights of the Industrial Internet Consortium and other government and industry efforts on how to conquer them and pave the way to a marketplace of trustworthy software-enabled connected things.As the experience of several sectors has shown, the dependence on connected software needs to be met with a strong understanding of the risks to the overall trustworthiness of our software-based capabilities that we, our enterprises, and our world utilize. In many of these new connected systems issues of safety, reliability, and resilience rival or dominate concerns for security and privacy, the long-time focus of many in the IT world. Without a scalable and efficient method for managing these risks so our enterprises can continue to benefit from these advancements that powers our military, commercial industries, cities, and homes to new levels of efficiency, versatility, and cost effectiveness we face the potential for harm, death, and destructiveness.In such a marketplace, creating, exchanging, and integrating components that are trustworthy as well as entering into value-chain relationships with trustworthy partners and service suppliers will be common if we can provide a method for explicitly defining what is meant by the word trustworthy. The approach being pursued by these groups for applying Software Assurance to these systems and their Supply Chains by leveraging Structured Assurance Cases, Software Bill of Materials (the focus of this paper), and secure development practices applied to the evolving Agile and DevSecOps methodologies, is to explicitly identify the detailed requirements ``about what we need to know about something for it to be worthy of our trust'' and to do that in a way that we can convey that basis of trust to others that: can scale; is consistent within different workflows; is flexible to differing sets of hazards and environments; and is applicable to all sectors, domains, and industries.

Currently the use and production of multimedia data such as digital images have increased due to its wide use within smart devices and open networks. Although this has some advantages, it has generated several issues related to the infraction of intellectual property. Digital image watermarking is a promissory solution to solve these issues. Considering the need to develop mechanisms to improve the information security as well as protect the intellectual property of the digital images, in this paper we propose a novel visible-imperceptible watermarking based on reversible data hiding with contrast enhancement. In this way, a watermark logo is embedded in the spatial domain of the original image imperceptibly, so that the logo is revealed applying reversible data hiding increasing the contrast of the watermarked image and the same time concealing a great amount of data bits, which are extracted and the watermarked image restored to its original conditions using the reversible functionality. Experimental results show the effectiveness of the proposed algorithm. A performance comparison with the current state-of-the-art is provided.

Despite the advances in research on usable secure email, the majority of mail user agents found in practice still violates best practices in UI design and uses ineffective and inhomogeneous design strategies to communicate and let users control the security status of an email message.We propose a novel interaction and design concept that we refer to as persuasive message design. Our approach is derived from heuristics and a systematic meta-study of existing HCI literature on email management, usable secure email and phishing research. Concluding on this body of knowledge we propose the design of interfaces that suppress weak cues and instead manipulate the display of emails according to their technical security level. Persuasive message design addresses several shortcomings of current secure email user interfaces and provides a consistent user experience that can be deployed even by email providers.

Technology and the use of online services are very prevalent across much of our everyday lives. As our digital interactions continue to grow, there is a need to improve public awareness of the risks to our personal online privacy and security. Designing for cyber security awareness has never been so important. In this work, we consider people's current impressions towards their privacy and security online. We also explore how abnormal network activity data can be visually conveyed to afford a heightened cyber security awareness. In detail, the paper documents the different effects of visual variables in an edge and node DoS visualisation to depict abnormally high volumes of traffic. The results from two studies show that people are generally becoming more concerned about their privacy and security online. Moreover, we have found that the more focus based visual techniques (i.e. blur) and geometry-based techniques (i.e. jaggedness and sketchiness) afford stronger impressions of uncertainty from abnormally high volumes of network traffic. In terms of security, these impressions and feelings alert in the end-user that something is not quite as it should be and hence develop a heightened cyber security awareness.

In this paper, we assess the vulnerability of network anomaly detection systems that use machine learning methods. Although the performance of these network anomaly detection systems is high in comparison to that of existing methods without machine learning methods, the use of machine learning methods for detecting vulnerabilities is a growing concern among researchers of image processing. If the vulnerabilities of machine learning used in the network anomaly detection method are exploited by attackers, large security threats are likely to emerge in the near future. Therefore, in this paper we clarify how vulnerability detection of machine learning network anomaly detection methods affects their performance.

The PyPI ecosystem has indexed millions of Python libraries to allow developers to automatically download and install dependencies of their projects based on the specified version constraints. Despite the convenience brought by automation, version constraints in Python projects can easily conflict, resulting in build failures. We refer to such conflicts as Dependency Conflict (DC) issues. Although DC issues are common in Python projects, developers lack tool support to gain a comprehensive knowledge for diagnosing the root causes of these issues. In this paper, we conducted an empirical study on 235 real-world DC issues. We studied the manifestation patterns and fixing strategies of these issues and found several key factors that can lead to DC issues and their regressions. Based on our findings, we designed and implemented Watchman, a technique to continuously monitor dependency conflicts for the PyPI ecosystem. In our evaluation, Watchman analyzed PyPI snapshots between 11 Jul 2019 and 16 Aug 2019, and found 117 potential DC issues. We reported these issues to the developers of the corresponding projects. So far, 63 issues have been confirmed, 38 of which have been quickly fixed by applying our suggested patches.

Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.

Visually similar characters, or homoglyphs, can be used to perform social engineering attacks or to evade spam and plagiarism detectors. It is thus important to understand the capabilities of an attacker to identify homoglyphs - particularly ones that have not been previously spotted - and leverage them in attacks. We investigate a deep-learning model using embedding learning, transfer learning, and augmentation to determine the visual similarity of characters and thereby identify potential homoglyphs. Our approach uniquely takes advantage of weak labels that arise from the fact that most characters are not homoglyphs. Our model drastically outperforms the Normal-ized Compression Distance approach on pairwise homoglyph identification, for which we achieve an average precision of 0.97. We also present the first attempt at clustering homoglyphs into sets of equivalence classes, which is more efficient than pairwise information for security practitioners to quickly lookup homoglyphs or to normalize confusable string encodings. To measure clustering performance, we propose a metric (mBIOU) building on the classic Intersection-Over-Union (IOU) metric. Our clustering method achieves 0.592 mBIOU, compared to 0.430 for the naive baseline. We also use our model to predict over 8,000 previously unknown homoglyphs, and find good early indications that many of these may be true positives. Source code and list of predicted homoglyphs are uploaded to Github: https://github.com/PerryXDeng/weaponizing\_unicode.

There are some modern mobile operation systems. The main two of them are iOS and Android. However, in the past, there were two more commonly used ones: Windows Mobile and Symbian. Each of these systems has its own pros and cons, whereas none of them is the best or the worst one in different criterions. In this paper the main criterions of operation system quality are discussed. The paper defines what the mobile operating system quality is.

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our ''robust architecture Odyssey'' reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy ( 5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at https://github.com/gmh14/RobNets.

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

This study focuses on identifying the factors contributing to a sense of personal responsibility that could improve understanding of insecure cybersecurity behavior and guide research toward more effective messaging targeting non-adopting populations. Towards that, we ran a 2(account type) x2(usage scenario) x2(message type) between-group study with 237 United States adult participants on Amazon MTurk, and investigated how the non-adopting population allocates blame, and under what circumstances they blame the end user among the parties who hold responsibility: the software companies holding data, the attackers exposing data, and others. We find users primarily hold service providers accountable for breaches but they feel the same companies should not enforce stronger security policies on users. Results indicate that people do hold end users accountable for their behavior in the event of a breach, especially when the users' behavior affects others. Implications of our findings in risk communication is discussed in the paper.

In today's era, the probability of the wireless devices getting hacked has grown extensively. Due to the various WLAN vulnerabilities, hackers can break into the system. There is a lack of awareness among the people about security mechanisms. From the past experiences, the study reveals that router security encrypted protocol is often cracked using several ways like dictionary attack and brute force attack. The identified methods are costly, require extensive hardware, are not reliable and do not detect all the vulnerabilities of the system. This system aims to test all router protocols which are WEP, WPA, WPA2, WPS and detect the vulnerabilities of the system. Kali Linux version number 2.0 is being used over here and therefore the tools like airodump-ng, aircrack-ng are used to acquire access point pin which gives prevention methods for detected credulity and aims in testing various security protocols to make sure that there's no flaw which will be exploited.

The increasing complexity of modern computing devices has rendered security architectures vulnerable to recent side-channel and transient-execution attacks. We discuss the most relevant defenses as well as their drawbacks and how to overcome them for next-generation secure processor design.

Mobile Ad hoc networks (MANET) are becoming extremely popular because of the expedient features that also make them more exposed to various kinds of security attacks. The Wormhole attack is considered to be the most unsafe attack due to its unusual pattern of tunnel creation between two malevolent nodes. In it, one malevolent node attracts all the traffic towards the tunnel and forwards it to another malevolent node at the other end of the tunnel and replays them again in the network. Once the Wormhole tunnel is created it can launch different kind of other attacks such as routing attack, packet dropping, spoofing etc. In past few years a lot of research is done for securing routing protocols. Dynamic Source Routing (DSR) protocol is considered foremost MANET routing protocols. In this paper we are forming the wormhole tunnel in which malevolent nodes use different interfaces for communication in DSR protocol. NS3 simulator is being used for the analysis of the DSR routing protocol under the wormhole attack. This paper provides better understanding of the wormhole attack in DSR protocol which can benefit further research.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

Cross-site scripting (XSS) is one of the main threats of Web applications, which has great harm. How to effectively detect and defend against XSS attacks has become more and more important. Due to the malicious obfuscation of attack codes and the gradual increase in number, the traditional XSS detection methods have some defects such as poor recognition of malicious attack codes, inadequate feature extraction and low efficiency. Therefore, we present a novel approach to detect XSS attacks based on the attention mechanism of Long Short-Term Memory (LSTM) recurrent neural network. First of all, the data need to be preprocessed, we used decoding technology to restore the XSS codes to the unencoded state for improving the readability of the code, then we used word2vec to extract XSS payload features and map them to feature vectors. And then, we improved the LSTM model by adding attention mechanism, the LSTM-Attention detection model was designed to train and test the data. We used the ability of LSTM model to extract context-related features for deep learning, the added attention mechanism made the model extract more effective features. Finally, we used the classifier to classify the abstract features. Experimental results show that the proposed XSS detection model based on LSTM-Attention achieves a precision rate of 99.3% and a recall rate of 98.2% in the actually collected dataset. Compared with traditional machine learning methods and other deep learning methods, this method can more effectively identify XSS attacks.

People continue to store their sensitive information in their smart-phone applications. Users seldom read an app's privacy policy to see how their information is being collected, used, and shared. In this paper, using a reference list of over 600 Personally Identifiable Information (PII) attributes, we investigate the privacy policies of 100 popular health and fitness mobile applications in both Android and iOS app markets to find the set of personal information these apps collect, use and share. The reference list of PII was independently built from a longitudinal study at The University of Texas investigating thousands of identity theft and fraud cases where PII attributes and associated value and risks were empirically quantified. This research leverages the reference PII list to identify and analyze the value of personal information collected by the mobile apps and the risk of disclosing this information. We found that the set of PII collected by these mobile apps covers 35% of the entire reference set of PII and, due to dependencies between PII attributes, these mobile apps have a likelihood of indirectly impacting 70% of the reference PII if breached. For a specific app, we discovered the monetary loss could reach \$1M if the set of sensitive data it collects is breached. We finally utilize Bayesian inference to measure risks of a set of PII gathered by apps: the probability that fraudsters can discover, impersonate and cause harm to the user by misusing only the PII the mobile apps collected.