Biblio
Data have become an important asset for analysis and behavioral prediction, especially correlations between data. Privacy protection has aroused academic and social concern given the amount of personal sensitive information involved in data. However, existing works assume that the records are independent of each other, which is unsuitable for associated data. Many studies either fail to achieve privacy protection or lead to excessive loss of information while applying data correlations. Differential privacy, which achieves privacy protection by injecting random noise into the statistical results given the correlation, will improve the background knowledge of adversaries. Therefore, this paper proposes an information entropy differential privacy solution for correlation data privacy issues based on rough set theory. Under the solution, we use rough set theory to measure the degree of association between attributes and use information entropy to quantify the sensitivity of the attribute. The information entropy difference privacy is achieved by clustering based on the correlation and adding personalized noise to each cluster while preserving the correlations between data. Experiments show that our algorithm can effectively preserve the correlation between the attributes while protecting privacy.
In today's interconnected world, universities recognize the importance of protecting their information assets from internal and external threats. Being the possible insider threats to Information Security, employees are often coined as the weakest link. Both employees and organizations should be aware of this raising challenge. Understanding staff perception of compliance behaviour is critical for universities wanting to leverage their staff capabilities to mitigate Information Security risks. Therefore, this research seeks to get insights into staff perception based on factors adopted from several theories by using proposed constructs i.e. "perceived" practices/policies and "perceived" intention to comply. Drawing from the General Deterrence Theory, Protection Motivation Theory, Theory of Planned Behaviour and Information Reinforcement, within the context of Palestine universities, this paper integrates staff awareness of Information Security Policies (ISP) countermeasures as antecedents to ``perceived'' influencing factors (perceived sanctions, perceived rewards, perceived coping appraisal, and perceived information reinforcement). The empirical study is designed to follow a quantitative research approaches, use survey as a data collection method and questionnaires as the research instruments. Partial least squares structural equation modelling is used to inspect the reliability and validity of the measurement model and hypotheses testing for the structural model. The research covers ISP awareness among staff and seeks to assert that information security is the responsibility of all academic and administrative staff from all departments. Overall, our pilot study findings seem promising, and we found strong support for our theoretical model.
Multipath fading as well as shadowing is liable for the leakage of confidential information from the wireless channels. In this paper a solution to this information leakage is proposed, where a source transmits signal through a α-μ/α-μ composite fading channel considering an eavesdropper is present in the system. Secrecy enhancement is investigated with the help of two fading parameters α and μ. To mitigate the impacts of shadowing a α-μ distribution is considered whose mean is another α-μ distribution which helps to moderate the effects multipath fading. The mathematical expressions of some secrecy matrices such as the probability of non-zero secrecy capacity and the secure outage probability are obtained in closed-form to analyze security of the wireless channel in light of the channel parameters. Finally, Monte-Carlo simulations are provided to justify the correctness of the derived expressions.
We propose a new key sharing protocol executed through any constant parameter noiseless public channel (as Internet itself) without any cryptographic assumptions and protocol restrictions on SNR in the eavesdropper channels. This protocol is based on extraction by legitimate users of eigenvalues from randomly generated matrices. A similar protocol was proposed recently by G. Qin and Z. Ding. But we prove that, in fact, this protocol is insecure and we modify it to be both reliable and secure using artificial noise and privacy amplification procedure. Results of simulation prove these statements.
Correct compilers perform program transformations preserving input/output behaviours of programs. Yet, correctness does not prevent program optimisations from introducing information-flow leaks that would make the target program more vulnerable to side-channel attacks than the source program. To tackle this problem, we propose a notion of Information-Flow Preserving (IFP) program transformation which ensures that a target program is no more vulnerable to passive side-channel attacks than a source program. To protect against a wide range of attacks, we model an attacker who is granted arbitrary memory accesses for a pre-defined set of observation points. We propose a compositional proof principle for proving that a transformation is IFP. Using this principle, we show how a translation validation technique can be used to automatically verify and even close information-flow leaks introduced by standard compiler passes such as dead-store elimination and register allocation. The technique has been experimentally validated on the CompCert C compiler.
Spams are unsolicited and unnecessary messages which may contain harmful codes or links for activation of malicious viruses and spywares. Increasing popularity of social networks attracts the spammers to perform malicious activities in social networks. So an efficient spam detection method is necessary for social networks. In this paper, feed forward neural network with back propagation based spam detection model is proposed. The quality of the learning process is improved by tuning initial weights of feed forward neural network using proposed enhanced step size firefly algorithm which reduces the time for finding optimal weights during the learning process. The model is applied for twitter dataset and the experimental results show that, the proposed model performs well in terms of accuracy and detection rate and has lower false positive rate.
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.
In the future, mixed traffic Highly Automated Vehicles (HAV) will have to resolve interactions with human operated traffic. A particular problem for HAVs is the detection of human states influencing safety, critical decisions, and driving behavior of humans. We demonstrate the value proposition of neurophysiological sensors and driver models for optimizing performance of HAVs under safety constraints in mixed traffic applications.
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.
Nowadays network applications have more focus on content distribution which is hard to tackle in IP based Internet. Information Centric Network (ICN) have the ability to overcome this problem for various scenarios, specifically for Vehicular Ad Hoc Networks (VANETs). Conventional IP based system have issues like mobility management hence ICN solve this issue because data fetching is not dependent on a particular node or physical location. Many initial investigations have performed on an instance of ICN commonly known as Named Data Networking (NDN). However, NDN exposes the new type of security susceptibilities, poisoning cache attack, flooding Interest attack, and violation of privacy because the content in the network is called by the name. This paper focused on mitigation of Interest flooding attack by proposing new scheme, named Interest Flooding Attack Mitigation Scheme (IFAMS) in Vehicular Named Data Network (VNDN). Simulation results depict that proposed IFAMS scheme mitigates the Interest flooding attack in the network.
Communication is considered as an essential part of our lives. Different medium was used for exchange of information, but due to advancement in field of technology, different network setup came into existence. One of the most suited in wireless field is Wireless Sensor Network (WSN). These networks are set up by self-organizing nodes which operate over radio environment. Since communication is done more rapidly, they are confined to many attacks which operate at different layers. In order to have efficient communication, some security measure must be introduced in the network ho have secure communication. In this paper, we describe various attacks functioning at different layers also one of the common network layer attack called Blackhole Attack with its mitigation technique using Intrusion Detection System (IDS) over network simulator ns2 has been discussed.
The RFID technology is now widely used and combined with everyday life. RFID Tag is a wireless device used to identify individuals and objects, in fact, it is a combination of the chip and antenna that sends the necessary information to an RFID Reader. On the other hand, an RFID Reader converts received radio waves into digital information and then provides facilities such as sending data to the computer and processing them. Radio frequency identification is a comprehensive processing technology that has led to a revolution in industry and medicine as an alternative to commercial barcodes. RFID Tag is used to tracking commodities and personal assets in the chain stores and even the human body and medical science. However, security and privacy problems have not yet been solved satisfactorily. There are many technical and economic challenges in this direction. In this paper, some of the latest technical research on privacy and security problems has been investigated in radio-frequency identification and security bit method, and it has been shown that in order to achieve this level of individual security, multiple technologies of RFID security development should combine with each other. These solutions should be cheap, efficient, reliable, flexible and long-term.
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.
the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.
The number of sensors and embedded devices in an urban area can be on the order of thousands. New low-power wide area (LPWA) wireless network technologies have been proposed to support this large number of asynchronous, low-bandwidth devices. Among them, the Cooperative UltraNarrowband (C-UNB) is a clean-slate cellular network technology to connect these devices to a remote site or data collection server. C-UNB employs small bandwidth channels, and a lightweight random access protocol. In this paper, a new application is investigated - the use of C-UNB wireless networks to support the Advanced Metering Infrastructure (AMI), in order to facilitate the communication between smart meters and utilities. To this end, we adapted a mathematical model for C-UNB, and implemented a network simulation module in NS-3 to represent C-UNB's physical and medium access control layer. For the application layer, we implemented the DLMS-COSEM protocol, or Device Language Message Specification - Companion Specification for Energy Metering. Details of the simulation module are presented and we conclude that it supports the results of the mathematical model.
Hybrid automata are an elegant formal model seamlessly integrating differential equations representing continuous dynamics with automata capturing switching behavior. Since the introduction of the computational model more than a quarter of a century ago [Maler et al. 1992], its algorithmic verification has been an area of intense research. Within this note, which is dedicated to Oded Maler (1957--2018) as one of the inventors of the model, we are trying to delineate major lines of attack to the reachability problem for hybrid automata. Due to its relation to system safety, the reachability problem is a prototypical verification problem for hybrid discrete-continuous system dynamics.
As Cyber-Physical Systems (CPSs) become more autonomous, it becomes harder for humans who interact with the CPSs to understand the behavior of the systems. Particularly for CPSs that must perform tasks while optimizing for multiple quality objectives and acting under uncertainty, it can be difficult for humans to understand the system behavior generated by an automated planner. This work-in-progress presents an approach at clarifying system behavior through interactive explanation by allowing end-users to ask Why and Why-Not questions about specific behaviors of the system, and providing answers in the form of contrastive explanation.
As Cyber-Physical Systems (CPSs) become more autonomous, it becomes harder for humans who interact with the CPSs to understand the behavior of the systems. Particularly for CPSs that must perform tasks while optimizing for multiple quality objectives and acting under uncertainty, it can be difficult for humans to understand the system behavior generated by an automated planner. This work-in-progress presents an approach at clarifying system behavior through interactive explanation by allowing end-users to ask Why and WhyNot questions about specific behaviors of the system, and providing answers in the form of contrastive explanation.
We performed a driving simulator study to investigate merging decisions with respect to an interaction partner in time-critical situations. The experimental paradigm was a two-alternative forced choice, where the subjects could choose to merge before human vehicles or highly automated vehicles (HAV). Under time pressure, subjects showed a significantly higher gap acceptance during merging situations when interacting with HAV. This confirmed our original hypothesis that when interacting with HAV, drivers would exploit the HAV's technological advantages and defensive programming in time-critical situations.
A term systems of systems (SoS) refers to a setup in which a number of independent systems collaborate to create a value that each of them is unable to achieve independently. Complexity of a SoS structure is higher compared to its constitute systems that brings challenges in analyzing its critical properties such as security. An SoS can be seen as a set of connected systems or services that needs to be adequately protected. Communication between such systems or services can be considered as a service itself, and it is the paramount for establishment of a SoS as it enables connections, dependencies, and a cooperation. Given that reliable and predictable communication contributes directly to a correct functioning of an SoS, communication as a service is one of the main assets to consider. Protecting it from malicious adversaries should be one of the highest priorities within SoS design and operation. This study aims to investigate the attack propagation problem in terms of service-guarantees through the decomposition into sub-services enriched with preconditions and postconditions at the service levels. Such analysis is required as a prerequisite for an efficient SoS risk assessment at the design stage of the SoS development life cycle to protect it from possibly high impact attacks capable of affecting safety of systems and humans using the system.