Biblio

Confidentiality, authentication, privacy and integrity are the pillars of securing data. The most generic way of providing security is setting up passwords and usernames collectively known as login credentials. Operating systems use different techniques to ensure security of login credentials yet brute force attacks and dictionary attacks along with various other types which leads to success in passing or cracking passwords.The objective of proposed HS model is to enhance the protection of SAM file used by Windows Registry so that the system is preserved from intruders.

Insider threats refer to threats posed by individuals who intentionally or unintentionally destroy, exfiltrate, or leak sensitive information, or expose their organization to outside attacks. Surveys of organizations in government and industry consistently show that threats posed by insiders rival those posed by hackers, and that insider attacks are even more costly. Emerging U.S. government guidelines and policies for establishing insider threat programs tend to specify only minimum standards for insider threat monitoring, analysis, and mitigation programs. Arguably, one of the most serious challenges is to identify and integrate behavioral (sociotechnical) indicators of insider threat r isk in addition to cyber/technical indicators. That is, in focusing on data that are most readily obtained, insider threat programs most often miss the human side of the problem. This talk briefly describes research aiming to catalog human as well as technical factors associated with insider threat risk and summarizes several recent studies that seek to inform the development of more comprehensive, proactive approaches to insider threat assessment.

Cloud Computing is an important term of modern technology. The usefulness of Cloud is increasing day by day and simultaneously more and more security problems are arising as well. Two of the major threats of Cloud are improper authentication and multi-tenancy. According to the specialists both pros and cons belong to multi-tenancy. There are security protocols available but it is difficult to claim these protocols are perfect and ensure complete protection. The purpose of this paper is to propose an integrated model to ensure better Cloud security for Authentication and multi-tenancy. Multi-tenancy means sharing of resources and virtualization among clients. Since multi-tenancy allows multiple users to access same resources simultaneously, there is high probability of accessing confidential data without proper privileges. Our model includes Kerberos authentication protocol to enhance authentication security. During our research on Kerberos we have found some flaws in terms of encryption method which have been mentioned in couple of IEEE conference papers. Pondering about this complication we have elected Elliptic Curve Cryptography. On the other hand, to attenuate arose risks due to multi-tenancy we are proposing a Resource Allocation Manager Unit, a Control Database and Resource Allocation Map. This part of the model will perpetuate resource allocation for the users.

The borderless, dynamic, high dimensional and virtual natures of cyberspace have brought unprecedented hard situation for defenders. To fight uncertain challenges in versatile cyberspace, a security framework based on the cloud computing platform that facilitates containerization technology to create a security capability pool to generate and distribute security payload according to system needs. Composed by four subsystems of the security decision center, the image and container library, the decision rule base and the security event database, this framework distills structured knowledge from aggregated security events and then deliver security load to the managed network or terminal nodes directed by the decision center. By introducing such unified and standardized top-level security framework that is decomposable, combinable and configurable in a service-oriented manner, it could offer flexibility and effectiveness in reconstructing security resource allocation and usage to reach higher efficiency.

With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.

This paper deals with effects of current sensor bandwidth and time delays in a system controlled by a Phase-Shift Self-Oscillating Current Controller (PSSOCC). The robustness of this current controller has been proved in former works showing its good performances in a large range of applications including AC/DC and DC/AC converters, power factor correction, active filters, isolation amplifiers and motor control. As switching frequencies can be upper than 30kHz, time delays and bandwidth limitations cannot be neglected in comparison with former works on this robust current controller. Thus, several models are proposed in this paper to analyze system behaviours. Those models permit to find analytical expressions binding maximum oscillation frequency with time delay and/or additional filter parameters. Through current spectrums analysis, quality of analytical expressions is proved for each model presented in this work. An experimental approach shows that every element of the electronic board having a low-pass effect or delaying command signals need to be included in the model in order to have a perfect match between calculations, simulations and practical results.

The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.

Sybil attacks, wherein a network is subverted by forging node identities, remains an open issue in wireless sensor networks (WSNs). This paper proposes a scheme, called Location and Communication ID (LCID) based detection, which employs residual energy, communication ID and location information of sensor nodes for Sybil attacks prevention. Moreover, LCID takes into account the resource constrained nature of WSNs and enhances energy conservation through hierarchical routing. Sybil nodes are purged before clusters formation to ensure that only legitimate nodes participate in clustering and data communication. CH selection is based on the average energy of the entire network to load-balance energy consumption. LCID selects a CH if its residual energy is greater than the average network energy. Furthermore, the workload of CHs is equally distributed among sensor nodes. A CH once selected cannot be selected again for 1/p rounds, where p is the CH selection probability. Simulation results demonstrate that, as compared to an eminent scheme, LCID has a higher Sybil attacks detection ratio, higher network lifetime, higher packet reception rate at the BS, lower energy consumption, and lower packet loss ratio.

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user's choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering.

Nowadays, a major challenge to network security is malicious codes. However, manual extraction of features is one of the characteristics of traditional detection techniques, which is inefficient. On the other hand, the features of the content and behavior of the malicious codes are easy to change, resulting in more inefficiency of the traditional techniques. In this paper, a K-Means Clustering Analysis is proposed based on Adaptive Weights (AW-MMKM). Identifying malicious codes in the proposed method is based on four types of network behavior that can be extracted from network traffic, including active, fault, network scanning, and page behaviors. The experimental results indicate that the AW-MMKM can detect malicious codes efficiently with higher accuracy.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

Conversational agents assist traditional teaching-learning instruments in proposing new designs for knowledge creation and learning analysis, across organizational environments. Means of building common educative background in both industry and academic fields become of interest for ensuring educational effectiveness and consistency. Such a context requires transferable practices and becomes the basis for the Agile adoption into Higher Education, at both curriculum and operational levels. The current work proposes a model for delivering Agile Scrum training through an assistive web-based conversational service, where analytics are collected to provide an overview on learners' knowledge path. Besides its specific applicability into Software Engineering (SE) industry, the model is to assist the academic SE curriculum. A user-acceptance test has been carried out among 200 undergraduate students and patterns of interaction have been depicted for 2 conversational strategies.

Industrial Internet-of-Things has been touted as the next revolution in the industrial domain, offering interconnectivity, independence, real-time operation, and self-optimization. Integration of smart systems, however, bridges the gap between information and operation technology, creating new avenues for attacks from the cyber domain. The dismantling of this air-gap, in conjunction with the devices' long lifespan -in the range of 20-30 years-, motivates us to bring the attention of the community to emerging advanced persistent threats. We demonstrate a threat that bridges the air-gap by leaking data from memory to analog peripherals through Direct Memory Access (DMA), delivered as a firmware modification through the supply chain. The attack automatically adapts to a target device by leveraging the Device Tree and resides solely in the peripherals, completely transparent to the main CPU, by judiciously short-circuiting specific components. We implement this attack on a commercial Programmable Logic Controller, leaking information over the available LEDs. We evaluate the presented attack vector in terms of stealthiness, and demonstrate no observable overhead on both CPU performance and DMA transfer speed. Since traditional anomaly detection techniques would fail to detect this firmware trojan, this work highlights the need for industrial control system-appropriate techniques that can be applied promptly to installed devices.

The confidentiality of data stored in embedded and handheld devices has become an urgent necessity more than ever before. Encryption of sensitive data is a well-known technique to preserve their confidentiality, however it comes with certain costs that can heavily impact the device processing resources. Utilizing multicore processors, which are equipped with current embedded devices, has brought a new era to enhance data confidentiality while maintaining suitable device performance. Encrypting the complete storage area, also known as Full Disk Encryption (FDE) can still be challenging, especially with newly emerging massive storage systems. Alternatively, since the most user sensitive data are residing inside persisting databases, it will be more efficient to focus on securing SQLite databases, through encryption, where SQLite is the most common RDBMS in handheld and embedded systems. This paper addresses the problem of ensuring data protection in embedded and mobile devices while maintaining suitable device performance by mitigating the impact of encryption. We presented here a proposed design for a parallel database encryption system, called SQLite-XTS. The proposed system encrypts data stored in databases transparently on-the-fly without the need for any user intervention. To maintain a proper device performance, the system takes advantage of the commodity multicore processors available with most embedded and mobile devices.

With the continuously development of smart meter-reading technologies for decades, remote information collection of electricity, water, gas and heat meters have been realized. Due to the difference of electrical interfaces and communication protocols among various types of meters, communication modes of meter terminals are not so compatible, it is difficult to realize communication optimization of electricity, water, gas and heat meters information collection services. In addition, with the development of power consumption information acquisition system, the number of acquisition terminals soars greatly and the data of terminal access is highly concurrent. Therefore, the risk of security access is increasing. This paper presents a light-weighted security access scheme of power line communication based on multi-source data acquisition of electricity, water, gas and heat meters, which separates multi-source data acquisition services and achieve services security isolation and channel security isolation. The communication reliability and security of the meter-reading service of "electricity, water, gas and heat" will be improved and the integrated meter service will be realized reliably.

In the paradigm of network coding, information-theoretic security is considered in the presence of wiretappers, who can access one arbitrary edge subset up to a certain size, referred to as the security level. Secure network coding is applied to prevent the leakage of the source information to the wiretappers. In this paper, we consider the problem of secure network coding for flexible pairs of information rate and security level with any fixed dimension (equal to the sum of rate and security level). We present a novel approach for designing a secure linear network code (SLNC) such that the same SLNC can be applied for all the rate and security-level pairs with the fixed dimension. We further develop a polynomial-time algorithm for efficient implementation and prove that there is no penalty on the required field size for the existence of SLNCs in terms of the best known lower bound by Guang and Yeung. Finally, by applying our approach as a crucial building block, we can construct a family of SLNCs that not only can be applied to all possible pairs of rate and security level but also share a common local encoding kernel at each intermediate node in the network.

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.

In this paper, we consider ways of organizing group authentication, as well as the features of constructing the isogeny of elliptic curves. The work includes the study of isogeny graphs and their application in postquantum systems. A hierarchical group authentication scheme has been developed using transformations based on the search for isogeny of elliptic curves.

In this paper, we consider one of the approaches to the study of the characteristics of an information system that is under the influence of various factors, and their management using neural networks and wavelet transforms based on determining the relationship between the modified state of the information system and the possibility of dynamic analysis of effects. At the same time, the process of influencing the information system includes the following components: impact on the components providing the functions of the information system; determination of the result of exposure; analysis of the result of exposure; response to the result of exposure. As an input signal, the characteristics of the means that affect are taken. The system includes an adaptive response unit, the input of which receives signals about the prerequisites for changes, and at the output, this unit generates signals for the inclusion of appropriate means to eliminate or compensate for these prerequisites or directly the changes in the information system.

In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.

This project enhances the security in which Ad Hoc On-Demand Distance Vector (AODV) routing protocol for MANETs with the game theoretical approach. This is achieved by using public key and private key for encryption and decryption processes. Proactive and reactive method is implemented in the proposed system. Reactive method is done in identification process but in proactive method is used to identify the nodes and also block the hackers node, then change the direction of data transmission to good nodes. This application can be used in military, research, confidential and emergency circumferences.

Several operational and economic factors impact the patching decisions of critical infrastructures. The constraints imposed by such factors could prevent organizations from fully remedying all of the vulnerabilities that expose their (critical) assets to risk. Therefore, an involved decision maker (e.g. security officer) has to strategically decide on the allocation of possible remediation efforts towards minimizing the inherent security risk. This, however, involves the use of comparative judgments to prioritize risks and remediation actions. Throughout this work, the security risk is quantified using the security metric Time-To-Compromise (TTC). Our main contribution is to provide a generic TTC estimator to comparatively assess the security posture of computer networks taking into account interdependencies between the network components, different adversary skill levels, and characteristics of (known and zero-day) vulnerabilities. The presented estimator relies on a stochastic TTC model and Monte Carlo simulation (MCS) techniques to account for the input data variability and inherent prediction uncertainties.