Biblio

Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.

Functionally safe control logic design without full duplication is difficult due to the complexity of random control logic. The Reorder buffer (ROB) is a control logic function commonly used in high performance computing systems. In this study, we focus on a safe ROB design used in an industry quality Network-on-Chip (NoC) Advanced eXtensible Interface (AXI) Network Interface (NI) block. We developed and applied area efficient safe design techniques including partial duplication, Error Detection Code (EDC) and invariance checking with formal proofs and showed that we can achieve a desired safe Diagnostic Coverage (DC) requirement with small area and power overheads and no performance degradation.

This paper presents an access control modelling that integrates risk assessment elements in the attribute-based model to organize the identification, authentication and authorization rules. Access control is complex in integrated systems, which have different actors accessing different information in multiple levels. In addition, systems are composed by different components, much of them from different developers. This requires a complete supply chain trust to protect the many existent actors, their privacy and the entire ecosystem. The incorporation of the risk assessment element introduces additional variables like the current environment of the subjects and objects, time of the day and other variables to help produce more efficient and effective decisions in terms of granting access to specific objects. The risk-based attributed access control modelling was applied in a health platform, Project CityZen.

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ?642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

Network attacks have become a growing threat to the current Internet. For the enhancement of network security and accountability, it is urgent to find the origin and identity of the adversary who misbehaves in the network. Some studies focus on embedding users' identities into IPv6 addresses, but such design cannot support the Stateless Address Autoconfiguration (SLAAC) protocol which is widely deployed nowadays. In this paper, we propose SDN-Ti, a general solution to traceback and identification for attackers in IPv6 networks based on Software Defined Network (SDN). In our proposal, the SDN switch performs a translation between the source IPv6 address of the packet and its trusted ID-encoded address generated by the SDN controller. The network administrator can effectively identify the attacker by parsing the malicious packets when the attack incident happens. Our solution not only avoids the heavy storage overhead and time synchronism problems, but also supports multiple IPv6 address assignment scenarios. What's more, SDN-Ti does not require any modification on the end device, hence can be easily deployed. We implement SDN-Ti prototype and evaluate it in a real IPv6 testbed. Experiment results show that our solution only brings very little extra performance cost, and it shows considerable performance in terms of latency, CPU consumption and packet loss compared to the normal forwarding method. The results indicate that SDN-Ti is feasible to be deployed in practice with a large number of users.

Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed system system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.

The increase of the digitalization taking place in various industrial domains is leading developers towards the design and implementation of more and more complex networked control systems (NCS) supported by Wireless Sensor Networks (WSN). This naturally raises new challenges for the current WSN technology, namely in what concerns improved guarantees of technical aspects such as real-time communications together with safe and secure transmissions. Notably, in what concerns security aspects, several cryptographic protocols have been proposed. Since the design of these protocols is usually error-prone, security breaches can still be exposed and MALICIOUSly exploited unless they are rigorously analyzed and verified. In this paper we formally verify, using ProVerif, three cryptographic protocols used in WSN, regarding the security properties of secrecy and authenticity. The security analysis performed in this paper is more robust than the ones performed in related work. Our contributions involve analyzing protocols that were modeled considering an unbounded number of participants and actions, and also the use of a hierarchical system to classify the authenticity results. Our verification shows that the three analyzed protocols guarantee secrecy, but can only provide authenticity in specific scenarios.

In this paper, we show how practical the little theorem of witness functions is in detecting security flaws in some categories of cryptographic protocols. We convey a formal analysis of the Needham-Schroeder symmetric-key protocol in the theory of witness functions. We show how it helps to warn about a security vulnerability in a given step of this protocol where the value of security of a sensitive ticket in a sent message unexpectedly decreases compared with its value when received. This vulnerability may be exploited by an intruder to mount a replay attack as described by Denning and Sacco.

Blockchain may have a potential to prove its value for the new US FDA regulatory requirements defined in the Drug Supply Chain Security Act (DSCSA) as innovative solutions are needed to support the highly complex pharmaceutical industry supply chain as it seeks to comply. In this paper, we examine how blockchain can be applied to meet with the security compliance requirement for the pharmaceutical supply chain. We explore the online playground of Hyperledger Composer, a set of tools for building blockchain business networks, to model the data and access control rules for the drug supply chain. Our experiment shows that this solution can provide a prototyping opportunity for compliance checking with certain limitations.

Industrial networks are the cornerstone of modern industrial control systems. Performing security checks of industrial communication processes helps detect unknown risks and vulnerabilities. Fuzz testing is a widely used method for performing security checks that takes advantage of automation. However, there is a big challenge to carry out security checks on industrial network due to the increasing variety and complexity of industrial communication protocols. In this case, existing approaches usually take a long time to model the protocol for generating test cases, which is labor-intensive and time-consuming. This becomes even worse when the target protocol is stateful. To help in addressing this problem, we employed a deep learning model to learn the structures of protocol frames and deal with the temporal features of stateful protocols. We propose a fuzzing framework named SeqFuzzer which automatically learns the protocol frame structures from communication traffic and generates fake but plausible messages as test cases. For proving the usability of our approach, we applied SeqFuzzer to widely-used Ethernet for Control Automation Technology (EtherCAT) devices and successfully detected several security vulnerabilities.

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

With the rapidly increasing connectivity in cyberspace, Insider Threat is becoming a huge concern. Insider threat detection from system logs poses a tremendous challenge for human analysts. Analyzing log files of an organization is a key component of an insider threat detection and mitigation program. Emerging machine learning approaches show tremendous potential for performing complex and challenging data analysis tasks that would benefit the next generation of insider threat detection systems. However, with huge sets of heterogeneous data to analyze, applying machine learning techniques effectively and efficiently to such a complex problem is not straightforward. In this paper, we extract a concise set of features from the system logs while trying to prevent loss of meaningful information and providing accurate and actionable intelligence. We investigate two unsupervised anomaly detection algorithms for insider threat detection and draw a comparison between different structures of the system logs including daily dataset and periodically aggregated one. We use the generated anomaly score from the previous cycle as the trust score of each user fed to the next period's model and show its importance and impact in detecting insiders. Furthermore, we consider the psychometric score of users in our model and check its effectiveness in predicting insiders. As far as we know, our model is the first one to take the psychometric score of users into consideration for insider threat detection. Finally, we evaluate our proposed approach on CERT insider threat dataset (v4.2) and show how it outperforms previous approaches.

For modern Automatic Test Equipment (ATE) one of the most daunting tasks is now Information Assurance (IA). What was once at most a secondary item consisting mainly of installing an Anti-Virus suite is now becoming one of the most important aspects of ATE. Given the current climate of IA it has become important to ensure ATE is kept safe from any breaches of security or loss of information. Even though most ATE are not on the Internet (or even on a local network for many) they are still vulnerable to some of the same attack vectors plaguing common computers and other electronic devices. This paper will discuss one method which can be used to ensure that modern ATE can continue to be used to test and detect faults in the systems they are designed to test. Most modern ATE include one or more Ethernet switches to allow communication to the many Instruments or devices contained within them. If the switches purchased are managed and support layer 2 or layer 3 of the Open Systems Interconnection (OSI) model they can also be used to help in the IA footprint of the station. Simple configurations such as limiting broadcast or multicast packets to the appropriate devices is the first step of limiting access to devices to what is needed. If the switch also includes some layer 3 like capabilities Virtual Local Area Networks can be created to further limit the communication pathways to only what is required to perform the required tasks. These and other simple switch configurations while not required can help limit the access of a virus or worm. This paper will discuss these and other configuration tools which can help prevent an ATE system from being compromised.

Network attacks continue to pose threats to missions in cyber space. To prevent critical missions from getting impacted or minimize the possibility of mission impact, active cyber defense is very important. Mission impact graph is a graphical model that enables mission impact assessment and shows how missions can be possibly impacted by cyber attacks. Although the mission impact graph provides valuable information, it is still very difficult for human analysts to comprehend due to its size and complexity. Especially when given limited resources, human analysts cannot easily decide which security measures to take first with respect to mission assurance. Therefore, this paper proposes to apply a ranking algorithm towards the mission impact graph so that the huge amount of information can be prioritized. The actionable conditions that can be managed by security admins are ranked with numeric values. The rank enables efficient utilization of limited resources and provides guidance for taking security countermeasures.

The zero-day attack in networks exploits an undiscovered vulnerability, in order to affect/damage networks or programs. The term “zero-day” refers to the number of days available to the software or the hardware vendor to issue a patch for this new vulnerability. Currently, the best-known defense mechanism against the zero-day attacks focuses on detection and response, as a prevention effort, which typically fails against unknown or new vulnerabilities. To the best of our knowledge, this attack has not been widely investigated for Software-Defined Networks (SDNs). Therefore, in this work we are motivated to develop anew zero-day attack detection and prevention mechanism, which is designed and implemented for SDN using a modified sandbox tool, named Cuckoo. Our experiments results, under UNIX system, show that our proposed design successfully stops zero-day malwares by isolating the infected client, and thus, prevents these malwares from infesting other clients.

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.

Various research efforts have focused on the problem of customer privacy protection in the smart grid arising from the large deployment of smart energy meters. In fact, the deployed smart meters distribute accurate profiles of home energy use, which can reflect the consumers' behaviour. This paper proposes a privacy-preserving lattice-based homomorphic aggregation scheme. In this approach, the smart household appliances perform the data aggregation while the smart meter works as relay node. Its role is to authenticate the exchanged messages between the home area network appliances and the related gateway. Security analysis show that our scheme guarantees consumer privacy and messages confidentiality and integrity in addition to its robustness against several attacks. Experimental results demonstrate the efficiency of our proposed approach in terms of communication complexity.

A country drives by their people and the electricity energy, the availability of the electricity power reflects the strength of that country. All most everything depends on the electricity energy, So it is become very important that we use the available energy very efficiently, and here the energy management come in the picture and Non Intrusive appliance Load monitoring (NIALM) is the part of energy management, in which the energy consumption by the particular load is monitored without any intrusion of wire/circuit. In literature, NIALM has been discussed as a monitoring process for conservation of energy using single point sensing (SPS) for extraction of aggregate signal of the appliances' features, ignoring the second function of demand response (DR) assuming that it would be manual or sensor-based. This assumption is not implementable in developing countries like India, because of requirement of extra cost of sensors, and privacy concerns. Surprisingly, despite decades of research on NIALM, none of the suggested procedures has resulted in commercial application. This paper highlights the causes behind non- commercialization, and proposes a viable and easy solution worthy of commercial exploitation both for monitoring and DR management for outage reduction in respect of Indian domestic consumers. Using a approach of multi point sensing (MPS), combined with Independent Component Analysis (ICA), experiments has been done in laboratory environment and CPWD specification has been followed.

The ongoing progressions in wireless innovation have lead to the advancement of another remote framework called Mobile Ad hoc Networks. The Mobile Ad hoc Network is a self arranging system of wireless gadgets associated by wireless connections. The traditional protocol, for example, TCP/IP has restricted use in Mobile impromptu systems in light of the absence of portability and assets. This has lead to the improvement of many steering conventions, for example, proactive, receptive and half breed. One intriguing examination zone in MANET is steering. Steering in the MANETs is a testing assignment and has gotten a colossal measure of consideration from examines. An uncommon consideration is paid on to feature the combination of MANET with the critical highlights of IPv6, for example, coordinated security, start to finish correspondence. This has prompted advancement of various directing conventions for MANETs, and every creator of each developed convention contends that the technique proposed gives an improvement over various distinctive systems considered in the writing for a given system situation. In this way, it is very hard to figure out which conventions may perform best under various diverse system situations, for example, expanding hub thickness and traffic. In this paper, we give the ongoing expository investigation on MANETs for IPV6 systems.

In the last few years, the usage and dependency on web applications and websites has significantly increased across a number of different areas such as online banking, shopping, financial transactions etc. amongst the several other areas. This has even directly multiplied the threat of SQL injection issue. A number of past studies have suggested that SQL injection should be handled as effectively as possible in order to avoid long term threats and dangers. This paper in specific attempts to discuss and evaluate some of the main SQL injection prevention methods.

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.

With the development of new technologies in the world, governments have tendency to make a communications with people and business with the help of such technologies. Electronic government (e-government) is defined as utilizing information technologies such as electronic networks, Internet and mobile phones by organizations and state institutions in order to making wide communication between citizens, business and different state institutions. Development of e-government starts with making website in order to share information with users and is considered as the main infrastructure for further development. Website assessment is considered as a way for improving service quality. Different international researches have introduced various indexes for website assessment, they only see some dimensions of website in their research. In this paper, the most important indexes for website quality assessment based on accurate review of previous studies are "Web design", "navigation", services", "maintenance and Support", "Citizens Participation", "Information Quality", "Privacy and Security", "Responsiveness", "Usability". Considering mentioned indexes in designing the website facilitates user interaction with the e-government websites.

Technology development has led to rapid increase in demands for multimedia applications. Due to this demand, digital archives are increasingly used to store these multimedia contents. Cloud is the commonly used archive to store, transmit, receive and share multimedia contents. Cloud makes use of internet to perform these tasks due to which data becomes more prone to attacks. Data security and privacy are compromised. This can be avoided by limiting data access to authenticated users and by hiding the data from cloud services that cannot be trusted. Hiding data from the cloud services involves encrypting the data before storing it into the cloud. Data to be shared with other users can be encrypted by utilizing Cipher Text-Policy Attribute Based Encryption (CP-ABE). CP-ABE is used which is a cryptographic technique that controls access to the encrypted data. The pairing-based computation based on bilinearity is used in ABE due to which the requirements for resources like memory and power supply increases rapidly. Most of the devices that we use today have limited memory. Therefore, an efficient pairing free CP- ABE access control scheme using elliptic curve cryptography has been used. Pairing based computation is replaced with scalar product on elliptic curves that reduces the necessary memory and resource requirements for the users. Even though pairing free CP-ABE is used, it is easier to retrieve the plaintext of a secret message if cryptanalysis is used. Therefore, this paper proposes to combine cryptography with steganography in such a way by embedding crypto text into an image to provide increased level of data security and data ownership for sub-optimal multimedia applications. It makes it harder for a cryptanalyst to retrieve the plaintext of a secret message from a stego-object if steganalysis were not used. This scheme significantly improved the data security as well as data privacy.

Smart cities comprise multiple critical infrastructures, two of which are the power grid and communication networks, backed by centralized data analytics and storage. To effectively model the interdependencies between these infrastructures and enable a greater understanding of how communities respond to and impact them, large amounts of varied, real-world data on residential and commercial consumer energy consumption, load patterns, and associated human behavioral impacts are required. The dissemination of such data to the research communities is, however, largely restricted because of security and privacy concerns. This paper creates an opportunity for the development and dissemination of synthetic energy consumption data which is inherently anonymous but holds similarities to the properties of real data. This paper explores a framework using mixture density network (MDN) model integrated with a multi-layered Long Short-Term Memory (LSTM) network which shows promise in this area of research. The model is trained using an initial sample recorded from residential smart meters in the state of Florida, and is used to generate fully synthetic energy consumption data. The synthesized data will be made publicly available for interested users.

In this paper, we present the enhancement of a lightweight key-policy attribute-based encryption (KP-ABE) scheme designed for the Internet of Things (IoT). The KP-ABE scheme was claimed to achieve ciphertext indistinguishability under chosen-plaintext attack in the selective-set model but we show that the KP-ABE scheme is insecure even in the weaker security notion, namely, one-way encryption under the same attack and model. In particular, we show that an attacker can decrypt a ciphertext which does not satisfy the policy imposed on his decryption key. Subsequently, we propose an efficient fix to the KP-ABE scheme as well as extending it to be a hierarchical KP-ABE (H-KP-ABE) scheme that can support role delegation in IoT applications. An example of applying our H-KP-ABE on an IoT-connected healthcare system is given to highlight the benefit of the delegation feature. Lastly, using the NIST curves secp192k1 and secp256k1, we benchmark the fixed (hierarchical) KP-ABE scheme on an Android phone and the result shows that the scheme is still the fastest in the literature.