Biblio

For a long time, the most important approach for detecting malicious applications was the use of static, hash-based signatures. This approach provides a fast response time, has a low performance overhead and is very stable due to its simplicity. However, with the rapid growth in the number of malware, as well as their increased complexity in terms of polymorphism and evasion, the era of reactive security solutions started to fade in favor of new, proactive approaches such as behavior based detection. We propose a novel approach that uses an interpreter virtual machine to run proactive behavior heuristics from bytecode signatures, thus combining the advantages of behavior based detection with those of signatures. Based on our approximation, using this approach we succeeded to reduce by 85% the time required to update a behavior based detection solution to detect new threats, while continuing to benefit from the versatility of behavior heuristics.

Lightweight cryptography has been widely utilized in resource constrained embedded devices of Cyber-Physical System (CPS) terminals. The hostile and unattended environment in many scenarios make those endpoints easy to be attacked by hardware based techniques. As a resource-efficient countermeasure against Fault Attacks, parity Concurrent Error Detection (CED) is preferably integrated with security-critical algorithm in CPS terminals. The parity bit changes if an odd number of faults occur during the cipher execution. In this paper, we analyze the effectiveness of fault detection of a parity CED protected cipher (PRESENT) using laser fault injection. The experimental results show that the laser perturbation to encryption can easily flip an even number of data bits, where the faults cannot be detected by parity. Due to the similarity of different parity structures, our attack can bypass almost all parity protections in block ciphers. Some suggestions are given to enhance the security of parity implementations.

The prevalence of memory corruption bugs in the past decades resulted in numerous defenses, such as stack canaries, control flow integrity (CFI), and memory-safe languages. These defenses can prevent entire classes of vulnerabilities, and help increase the security posture of a program. In this paper, we show that memory corruption defenses can be bypassed using speculative execution attacks. We study the cases of stack protectors, CFI, and bounds checks in Go, demonstrating under which conditions they can be bypassed by a form of speculative control flow hijack, relying on speculative or architectural overwrites of control flow data. Information is leaked by redirecting the speculative control flow of the victim to a gadget accessing secret data and acting as a side channel send. We also demonstrate, for the first time, that this can be achieved by stitching together multiple gadgets, in a speculative return-oriented programming attack. We discuss and implement software mitigations, showing moderate performance impact.

In this paper we discuss a simple and inexpensive method to introduce students to Newton's law of cooling using only their smartphones, according to the Bring-Your-Own-Device philosophy. A popular experiment in basic thermodynamics, both at a high-school and at University level, is the determination of the specific heat of solids and liquids using a water calorimeter, resourcing in many cases to a mercury thermometer. With our approach the analogical instrument is quickly turned into a digital device by analyzing the movement of the mercury with a video tracker. Thus, using very simple labware and the students' smartphones or tablets, it is possible to observe the decay behavior of the temperature of a liquid left to cool at room temperature. The dependence of the time constant with the mass and surface of the liquid can be easily probed, and the results of the different groups in the classroom can be brought together to observe the linear dependence1.

As the accessibility of high-resolution smartphone camera has increased and an improved computational speed, it is now convenient to build Business Card Readers on mobile phones. The project aims to design and develop a Business Card Reader (BCR) Application for iOS devices, using an open-source OCR Engine - Tesseract. The system accuracy was tested and evaluated using a dataset of 55 digital business cards obtained from an online repository. The accuracy result of the system was up to 74% in terms of both text recognition and data detection. A comparative analysis was carried out against a commercial business card reader application and our application performed vastly reasonable.

Return-oriented programming (ROP) is an effective code-reuse attack in which short code sequences (i.e., gadgets) ending in a ret instruction are found within existing binaries and then executed by taking control of the call stack. The shadow stack, control flow integrity (CFI) and code (re)randomization are three popular techniques for protecting programs against return address overwrites. However, existing runtime rerandomization techniques operate on concrete return addresses, requiring expensive pointer tracking. By adding one level of indirection, we introduce BarRA, the first shadow stack mechanism that applies continuous runtime rerandomization to abstract return addresses for protecting their corresponding concrete return addresses (protected also by CFI), thus avoiding expensive pointer tracking. As a nice side-effect, BarRA naturally combines the shadow stack, CFI and runtime rerandomization in the same framework. The key novelty of BarRA, however, is that once some abstract return addresses are leaked, BarRA will enforce the burn-after-reading property by rerandomizing the mapping from the abstract to the concrete return address space in the order of microseconds instead of seconds required for rerandomizing a concrete return address space. As a result, BarRA can be used as a superior replacement for the shadow stack, as demonstrated by comparing both using the 19 C/C++ benchmarks in SPEC CPU2006 (totalling 2,047,447 LOC) and analyzing a proof-of-concept attack, provided that we can tolerate some slight binary code size increases (by an average of 29.44%) and are willing to use 8MB of dedicated memory for holding up to 220 return addresses (on a 64-bit platform). Under an information leakage attack (for some return addresses), the shadow stack is always vulnerable but BarRA is significantly more resilient (by reducing an attacker's success rate to [1/(220)] on average). In terms of the average performance overhead introduced, both are comparable: 6.09% (BarRA) vs. 5.38% (the shadow stack).

Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work.

The contemporary period is scarred by the predominant place of social media in everyday life. Despite social media being a useful tool for communication and social gathering it also offers opportunities for harmful criminal activities. One of these activities is cyber-bullying enabled through the abuse and mistreatment of the internet as a means of bullying others virtually. As a way of minimising this occurrence, research into computer-based researched is carried out to detect cyber-bullying by the scientific research community. An extensive literature search shows that supervised learning techniques are the most commonly used methods for cyber-bullying detection. However, some non-supervised techniques and other approaches have proven to be effective towards cyber-bullying detection. This paper, therefore, surveys recent research on non-supervised techniques and offers some suggestions for future research in textual-based cyber-bullying detection including detecting roles, detecting emotional state, automated annotation and stylometric methods.

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into the training dataset. While these poison samples look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label poisoning attack against transfer learning, which creates poison images with their center close to the target image in the feature space. Our attack, Bullseye Polytope, improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end transfer learning, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) when crafting the poison samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without using extra poison samples.

With the traffic growth with different deterministic transport and isolation requirements in radio access networks (RAN), Flexible Ethernet (FlexE) over wavelength division multiplexing (WDM) network is as a candidate for next generation RAN transport, and the security issue in RAN transport is much more obvious, especially the eavesdropping attack in physical layer. Therefore, in this work, we put forward a cross-layer design for security enhancement through leveraging universal Hashing based FlexE data block permutation and multiple parallel fibre transmission for anti-eavesdropping in end-to-end FlexE over WDM network. Different levels of attack ability are considered for measuring the impact on network security and resource utilization. Furthermore, the trade-off problem between efficient resource utilization and guarantee of higher level of security is also explored. Numerical results demonstrate the cross-layer defense strategies are effective to struggle against intruders with different levels of attack ability.

Abstractions make building complex systems possible. Many facilities provided by a modern programming language are directly designed to build a certain style of abstraction. Abstractions also aim to enhance code reusability, thus enhancing programmer productivity and effectiveness. Real-world software systems can grow to have a complicated hierarchy of abstractions. Often, the hierarchy grows unnecessarily deep, because the programmers have envisioned the most generic use cases for a piece of code to make it reusable. Sometimes, the abstractions used in the program are not the appropriate ones, and it would be simpler for the higher level client to circumvent such abstractions. Another problem is the impedance mismatch between different pieces of code or libraries coming from different projects that are not designed to work together. Interoperability between such libraries are often hindered by abstractions, by design, in the name of hiding implementation details and encapsulation. These problems necessitate forms of abstraction that are easy to manipulate if needed. In this paper, we describe a powerful mechanism to create white-box abstractions, that encourage flatter hierarchies of abstraction and ease of manipulation and customization when necessary: program refinement. In so doing, we rely on the basic principle that writing directly in the host programming language is as least restrictive as one can get in terms of expressiveness, and allow the programmer to reuse and customize existing code snippets to address their specific needs.

As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).

Our vision in this paper is that agency, as the individual ability to intervene and tailor the system, is a crucial element in building trust in IoT technologies. Following up on this vision, we will first address the issue of agency, namely the individual capability to adopt free decisions, as a relevant driver in building trusted human-IoT relations, and how agency should be embedded in digital systems. Then we present the main challenges posed by existing approaches to implement this vision. We show then our proposal for a model-based approach that realizes the agency concept, including a prototype implementation.

In this research, we aim to suggest a method for designing trustworthy PRVAs (product recommendation virtual agents). We define an agent's trustworthiness as being operated by user emotion and knowledgeableness perceived by humans. Also, we suggest a user inner state transition model for increasing trust. To increase trust, we aim to cause user emotion to transition to positive by using emotional contagion and to cause user knowledgeableness perceived to become higher by increasing an agent's knowledge. We carried out two experiments to inspect this model. In experiment 1, the PRVAs recommended package tours and became highly knowledgeable in the latter half of ten recommendations. In experiment 2, the PRVAs recommended the same package tours and expressed a positive emotion in the latter half. As a result, participants' inner states transitioned as we expected, and it was proved that this model was valuable for PRVA recommendation.

We present an architecture for the Security Behavior Observatory (SBO), a client-server infrastructure designed to collect a wide array of data on user and computer behavior from hundreds of participants over several years. The SBO infrastructure had to be carefully designed to fulfill several requirements. First, the SBO must scale with the desired length, breadth, and depth of data collection. Second, we must take extraordinary care to ensure the security of the collected data, which will inevitably include intimate participant behavioral data. Third, the SBO must serve our research interests, which will inevitably change as collected data is analyzed and interpreted. This short paper summarizes some of our design and implementation benefits and discusses a few hurdles and trade-offs to consider when designing such a data collection system.

The coming generation of Internet-of-Things (IoT) applications will process massive amounts of incoming data while supporting data mining and online learning. In cases with demanding real-time requirements, such systems behave as smart memories: a high-bandwidth service that captures sensor input, processes it using machine-learning tools, replicates and stores "interesting" data (discarding uninteresting content), updates knowledge models, and triggers urgently-needed responses. Derecho is a high-throughput library for building smart memories and similar services. At its core Derecho implements atomic multicast (Vertical Paxos) and state machine replication (the classic durable Paxos). Derecho's replicated\textbackslashtextlessT\textbackslashtextgreater template defines a replicated type; the corresponding objects are associated with subgroups, which can be sharded into key-value structures. The persistent\textbackslashtextlessT\textbackslashtextgreater and volatile\textbackslashtextlessT\textbackslashtextgreater storage templates implement version vectors with optional NVM persistence. These support time-indexed access, offering lock-free snapshot isolation that blends temporal precision and causal consistency. Derecho automates application management, supporting multigroup structures and providing consistent knowledge of the current membership mapping. A query can access data from many shards or subgroups, and consistency is guaranteed without any form of distributed locking. Whereas many systems run consensus on the critical path, Derecho requires consensus only when updating membership. By leveraging an RDMA data plane and NVM storage, and adopting a novel receiver-side batching technique, Derecho can saturate a 12.5GB RDMA network, sending millions of events per second in each subgroup or shard. In a single subgroup with 2–16 members, through-put peaks at 16 GB/s for large (100MB or more) objects. While key-value subgroups would typically use 2 or 3-member shards, unsharded subgroups could be large. In tests with a 128-member group, Derecho's multicast and Paxos protocols were just 3–5x slower than for a small group, depending on the traffic pattern. With network contention, slow members, or overlapping groups that generate concurrent traffic, Derecho's protocols remain stable and adapt to the available bandwidth.

In the Internet of Things (IoT), Internet-connected things provide an influx of data and resources that offer unlimited possibility for applications and services. Smart City IoT systems refer to the things that are distributed over wide physical areas covering a whole city. While the new breed of data and resources looks promising, building applications in such large scale IoT systems is a difficult task due to the distributed and dynamic natures of entities involved, such as sensing, actuating devices, people and computing resources. In this paper, we explore the process of developing Smart City IoT applications from a coordination-based perspective. We show that a distributed coordination model that oversees such a large group of distributed components is necessary in building Smart City IoT applications. In particular, we propose Adaptive Distributed Dataflow, a novel Dataflow-based programming model that focuses on coordinating city-scale distributed systems that are highly heterogeneous and dynamic.

Reliability and security tend to be treated separately because they appear orthogonal: reliability focuses on accidental failures, security on intentional attacks. Because of the apparent dissimilarity between the two, tools to detect and recover from different classes of failures and attacks are usually designed and implemented differently. So, integrating support for reliability and security in a single framework is a significant challenge.

Here, we discuss how to address this challenge in the context of cloud computing, for which reliability and security are growing concerns. Because cloud deployments usually consist of commodity hardware and software, efficient monitoring is key to achieving resiliency. Although reliability and security monitoring might use different types of analytics, the same sensing infrastructure can provide inputs to monitoring modules.

We split monitoring into two phases: logging and auditing. Logging captures data or events; it constitutes the framework’s core and is common to all monitors. Auditing analyzes data or events; it’s implemented and operated independently by each monitor. To support a range of auditing policies, logging must capture a complete view, including both actions and states of target systems. It must also provide useful, trustworthy information regarding the captured view.

We applied these principles when designing HyperTap, a hypervisor-level monitoring framework for virtual machines (VMs). Unlike most VM-monitoring techniques, HyperTap employs hardware architectural invariants (hardware invariants, for short) to establish the root of trust for logging. Hardware invariants are properties defined and enforced by a hardware platform (for example, the x86 instruction set architecture). Additionally, HyperTap supports continuous, event-driven VM monitoring, which enables both capturing the system state and responding rapidly to actions of interest.

Physically Unclonable Functions (PUFs) promise to be a critical hardware primitive to provide unique identities to billions of connected devices in Internet of Things (IoTs). In traditional authentication protocols a user presents a set of credentials with an accompanying proof such as password or digital certificate. However, IoTs need more evolved methods as these classical techniques suffer from the pressing problems of password dependency and inability to bind access requests to the “things” from which they originate. Additionally, the protocols need to be lightweight and heterogeneous. Although PUFs seem promising to develop such mechanism, it puts forward an open problem of how to develop such mechanism without needing to store the secret challenge-response pair (CRP) explicitly at the verifier end. In this paper, we develop an authentication and key exchange protocol by combining the ideas of Identity based Encryption (IBE), PUFs and Key-ed Hash Function to show that this combination can help to do away with this requirement. The security of the protocol is proved formally under the Session Key Security and the Universal Composability Framework. A prototype of the protocol has been implemented to realize a secured video surveillance camera using a combination of an Intel Edison board, with a Digilent Nexys-4 FPGA board consisting of an Artix-7 FPGA, together serving as the IoT node. We show, though the stand-alone video camera can be subjected to man-in-the-middle attack via IP-spoofing using standard network penetration tools, the camera augmented with the proposed protocol resists such attacks and it suits aptly in an IoT infrastructure making the protocol deployable for the industry.

Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality. This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way. We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.

We develop a novel model-based hardware-in-the-loop (HIL) framework for optimising energy consumption of embedded software controllers. Controller and plant models are specified as networks of parameterised timed input/output automata and translated into executable code. The controller is encoded into the target embedded hardware, which is connected to a power monitor and interacts with the simulation of the plant model. The framework then generates a power consumption model that maps controller transitions to distributions over power measurements, and is used to optimise the timing parameters of the controller, without compromising a given safety requirement. The novelty of our approach is that we measure the real power consumption of the controller and use thus obtained data for energy optimisation. We employ timed Petri nets as an intermediate representation of the executable specification, which facilitates efficient code generation and fast simulations. Our framework uniquely combines the advantages of rigorous specifications with accurate power measurements and methods for online model estimation, thus enabling automated design of correct and energy-efficient controllers.

Cyber security management of systems in the cyberspace has been a challenging problem for both practitioners and the research community. Their proprietary nature along with the complexity renders traditional approaches rather insufficient and creating the need for the adoption of a holistic point of view. This paper draws upon the principles theory game in order to present a novel systemic approach towards cyber security management, taking into account the complex inter-dependencies and providing cost-efficient defense solutions.

This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunneling Obfuscations dataset. The dataset captured legitimate and obfuscated malicious TCP communications on selected vulnerable network services. The multiclass classification NIDS is able to distinguish obfuscated and direct network intrusion with up to 95% accuracy.

File timestamps do not receive much attention from information security specialists and computer forensic scientists. It is believed that timestamps are extremely easy to fake, and the system time of a computer can be changed. However, operating system for synchronizing processes and working with file objects needs accurate time readings. The authors estimate that several million timestamps can be stored on the logical partition of a hard disk with the NTFS. The MFT stores four timestamps for each file object in \$STANDARDİNFORMATION and \$FILE\_NAME attributes. Furthermore, each directory in the İNDEX\_ROOT or İNDEX\_ALLOCATION attributes contains four more timestamps for each file within it. File timestamps are set and changed as a result of file operations. At the same time, some file operations differently affect changes in timestamps. This article presents the results of the tool-based observation over the creation and update of timestamps in the MFT resulting from the basic file operations. Analysis of the results is of interest with regard to computer forensic science.

Logging mechanisms that capture detailed traces of user activity, including creating, reading, updating, and deleting (CRUD) data, facilitate meaningful forensic analysis following a security or privacy breach. However, software requirements often inadequately and inconsistently state “what” user actions should be logged, thus hindering meaningful forensic analysis. In this talk, we will explore a variety of techniques for building a software system that supports forensic analysis. We will discuss systematic heuristics-driven and patterns-driven processes for identifying log events that must be logged based on user actions and potential accidental and malicious use, as described in natural language software artifacts. We then discuss systematic process for creating a black-box test suite for verifying the identified log events are logged. Using the results of executing the black-box test suite, we propose and evaluate a security metric for measuring the forensic-ability of user activity logs.