Biblio

We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.

Control-Flow Hijacking attacks are the dominant attack vector against C/C++ programs. Control-Flow Integrity (CFI) solutions mitigate these attacks on the forward edge, i.e., indirect calls through function pointers and virtual calls. Protecting the backward edge is left to stack canaries, which are easily bypassed through information leaks. Shadow Stacks are a fully precise mechanism for protecting backwards edges, and should be deployed with CFI mitigations. We present a comprehensive analysis of all possible shadow stack mechanisms along three axes: performance, compatibility, and security. For performance comparisons we use SPEC CPU2006, while security and compatibility are qualitatively analyzed. Based on our study, we renew calls for a shadow stack design that leverages a dedicated register, resulting in low performance overhead, and minimal memory overhead, but sacrifices compatibility. We present case studies of our implementation of such a design, Shadesmar, on Phoronix and Apache to demonstrate the feasibility of dedicating a general purpose register to a security monitor on modern architectures, and Shadesmar's deployability. Our comprehensive analysis, including detailed case studies for our novel design, allows compiler designers and practitioners to select the correct shadow stack design for different usage scenarios. Shadow stacks belong to the class of defense mechanisms that require metadata about the program's state to enforce their defense policies. Protecting this metadata for deployed mitigations requires in-process isolation of a segment of the virtual address space. Prior work on defenses in this class has relied on information hiding to protect metadata. We show that stronger guarantees are possible by repurposing two new Intel x86 extensions for memory protection (MPX), and page table control (MPK). Building on our isolation efforts with MPX and MPK, we present the design requirements for a dedicated hardware mechanism to support intra-process memory isolation, and discuss how such a mechanism can empower the next wave of highly precise software security mitigations that rely on partially isolated information in a process.

Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.

The explosive growth of mobile traffic in the Internet makes content delivery a challenging issue to cope with. To promote efficiency of content distribution and reduce network cost, Internet Service Providers (ISPs) and content providers (CPs) are motivated to cooperatively work. As a clean-slate solution, nowadays Information-Centric Networking architectures have been proposed and widely researched, where the thought of in-network caching, especially edge caching, can be applied to mobile wireless networks to fundamentally address this problem. Considered the profit split issue between ISPs and CPs and the influence of content popularity is largely ignored, in this paper, we propose a Stackelberg-based optimal network profit split scheme for content delivery in information-centric wireless networks. Simulation results show that the performance of our proposed model is comparable to its centralized solution and obviously superior to current ISP-CP cooperative schemes without considering cache deployment in the network.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

Securing multi-robot teams against malicious activity is crucial as these systems accelerate towards widespread societal integration. This emerging class of ``physical networks'' requires research into new methods of security that exploit their physical nature. This paper derives a theoretical framework for securing multi-agent consensus against the Sybil attack by using the physical properties of wireless transmissions. Our frame-work uses information extracted from the wireless channels to design a switching signal that stochastically excludes potentially untrustworthy transmissions from the consensus. Intuitively, this amounts to selectively ignoring incoming communications from untrustworthy agents, allowing for consensus to the true average to be recovered with high probability if initiated after a certain observation time T0 that we derive. This work is different from previous work in that it allows for arbitrary malicious node values and is insensitive to the initial topology of the network so long as a connected topology over legitimate nodes in the network is feasible. We show that our algorithm will recover consensus and the true graph over the system of legitimate agents with an error rate that vanishes exponentially with time.

Network Function Virtualization (NFV) is an implementation of cloud computing that leverages virtualization technology to provide on-demand network functions such as firewalls, domain name servers, etc., as software services. One of the methods that help us understand the design and implementation process of such a new system in an abstract way is architectural modeling. Architectural modeling can be presented through UML diagrams to show the interaction between different components and its stakeholders. Also, it can be used to analyze the security threats and the possible countermeasures to mitigate the threats. In this paper, we show some of the possible threats that may jeopardize the security of NFV. We use misuse patterns to analyze misuses based on privilege escalation and VM escape threats. The misuse patterns are part of an ongoing catalog, which is the first step toward building a security reference architecture for NFV.

This paper proposes a multi-modular AC-DC converter system using wireless communication for a rapid charger of electric vehicles (EVs). The multi-modular topology, which consists of multiple modules, has an advantage on the expandability regarding voltage and power. In the proposed system, the input current and output voltage are controlled by each decentralized controller, which wirelessly communicates to the main controller, on each module. Thus, high-speed communication between the main and modules is not required. As the results in a reduced number of signal lines. The fundamental effectiveness of the proposed system is verified with a 3-kW prototype. In the experimented results, the input current imbalance rate is reduced from 49.4% to 0.1%, where total harmonic distortion is less than 3%.

Big data platforms have grown popular for real-time stream processing on distributed clusters and clouds. However, execution of sensitive streaming applications on shared computing resources increases their vulnerabilities, and may lead to data leaks and injection of spurious logic that can compromise these applications. Here, we adopt Moving Target Defense (MTD) techniques into Fast Data platforms, and propose MTD strategies by which we can mitigate these attacks. Our strategies target the platform, application and data layers, which make these reusable, rather than the OS, virtual machine, or hardware layers, which are environment specific. We use Apache Storm as the canonical distributed stream processing platform for designing our MTD strategies, and offer a preliminary evaluation that indicates the feasibility and evaluates the performance overheads.

Static application security testing (SAST) detects vulnerability warnings through static program analysis. Fixing the vulnerability warnings tremendously improves software quality. However, SAST has not been fully utilized by developers due to various reasons: difficulties in handling a large number of reported warnings, a high rate of false warnings, and lack of guidance in fixing the reported warnings. In this paper, we collaborated with security experts from a commercial SAST product and propose a set of approaches (Priv) to help developers better utilize SAST techniques. First, Priv identifies preferred fix locations for the detected vulnerability warnings, and group them based on the common fix locations. Priv also leverages visualization techniques so that developers can quickly investigate the warnings in groups and prioritize their quality-assurance effort. Second, Priv identifies actionable vulnerability warnings by removing SAST-specific false positives. Finally, Priv provides customized fix suggestions for vulnerability warnings. Our evaluation of Priv on six web applications highlights the accuracy and effectiveness of Priv. For 75.3% of the vulnerability warnings, the preferred fix locations found by Priv are identical to the ones annotated by security experts. The visualization based on shared preferred fix locations is useful for prioritizing quality-assurance efforts. Priv reduces the rate of SAST-specific false positives significantly. Finally, Priv is able to provide fully complete and correct fix suggestions for 75.6% of the evaluated warnings. Priv is well received by security experts and some features are already integrated into industrial practice.

We present True2F, a system for second-factor authentication that provides the benefits of conventional authentication tokens in the face of phishing and software compromise, while also providing strong protection against token faults and backdoors. To do so, we develop new lightweight two-party protocols for generating cryptographic keys and ECDSA signatures, and we implement new privacy defenses to prevent cross-origin token-fingerprinting attacks. To facilitate real-world deployment, our system is backwards-compatible with today's U2F-enabled web services and runs on commodity hardware tokens after a firmware modification. A True2F-protected authentication takes just 57ms to complete on the token, compared with 23ms for unprotected U2F.

Compiler optimization level recognition can be applied to vulnerability discovery and binary analysis. Due to the exists of many different compilation optimization options, the difference in the contents of the binary file is very complicated. There are thousands of compiler optimization algorithms and multiple different processor architectures, so it is very difficult to manually analyze binary files and recognize its compiler optimization level with rules. This paper first proposes a CNN-based compiler optimization level recognition model: BinEye. The system extracts semantic and structural differences and automatically recognize the compiler optimization levels. The model is designed to be very suitable for binary file processing and is easy to understand. We built a dataset containing 80028 binary files for the model training and testing. Our proposed model achieves an accuracy of over 97%. At the same time, BinEye is a fully CNN-based system and it has a faster forward calculation speed, at least 8 times faster than the normal RNN-based model. Through our analysis of the model output, we successfully found the difference in assembly codes caused by the different compiler optimization level. This means that the model we proposed is interpretable. Based on our model, we propose a method to analyze the code differences caused by different compiler optimization levels, which has great guiding significance for analyzing closed source compilers and binary security analysis.

We address the need for security requirements to take into account risks arising from complex supply chains underpinning cyber-physical infrastructures such as industrial control systems (ICS). We present SEISMiC (SEcurity Industrial control SysteM supply Chains), a framework that takes into account the whole spectrum of security risks - from technical aspects through to human and organizational issues - across an ICS supply chain. We demonstrate the effectiveness of SEISMiC through a supply chain risk assessment of Natanz, Iran's nuclear facility that was the subject of the Stuxnet attack.

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.

An increasingly important tool for securing computer net- works is the use of deceptive decoy objects (e.g., fake hosts, accounts, or files) to detect, confuse, and distract attackers. One of the well-known challenges in using decoys is that it can be difficult to design effective decoys that are hard to distinguish from real objects, especially against sophisticated attackers who may be aware of the use of decoys. A key issue is that both real and decoy objects may have observable features that may give the attacker the ability to distinguish one from the other. However, a defender deploying decoys may be able to modify some features of either the real or decoy objects (at some cost) making the decoys more effective. We present a game-theoretic model of two-sided deception that models this scenario. We present an empirical analysis of this model to show strategies for effectively concealing decoys, as well as some limitations of decoys for cyber security. 

Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

In this paper, we formulate a combinatorial optimization problem that aims to maximize the accuracy of a lower bound estimate of the probability of security of a multi-robot system (MRS), while minimizing the computational complexity involved in its calculation. Security of an MRS is defined using the well-known control theoretic notion of left invertiblility, and the probability of security of an MRS can be calculated using binary decision diagrams (BDDs). The complexity of a BDD depends on the number of disjoint path sets considered during its construction. Taking into account all possible disjoint paths results in an exact probability of security, however, selecting an optimal subset of disjoint paths leads to a good estimate of the probability while significantly reducing computation. To deal with the dynamic nature of MRSs, we introduce two methods: (1) multi-point optimization, a technique that requires some a priori knowledge of the topology of the MRS over time, and (2) online optimization, a technique that does not require a priori knowledge, but must construct BDDs while the MRS is operating. Finally, our approach is validated on an MRS performing a rendezvous objective while exchanging information according to a noisy state agreement process.

Cyber-Physical System (CPS) and Cloud Computing are emerging and important research fields in recent years. It is a current trend that CPS combines with Cloud Computing. Compared with traditional CPS, Cloud can improve its performance, but Cloud failures occur occasionally. The existing cloud-based CPS architectures rely too much on the Cloud, ignoring the risk and problems caused by Cloud failures, thus making the reliability of CPS not guaranteed. In order to solve the risk and problems above, spare parts are involved based on the research of cloud-based CPS. An architecture of cloud-based CPS with spare parts is proposed and two solutions for spare parts are designed. Agricultural intelligent temperature control system is used as an example to model and simulate the proposed architecture and solutions using Simulink. The simulation results prove the effectiveness of the proposed architecture and solutions, which enhance the reliability of cloud-based CPS.

Most searchable attribute-based encryption schemes only support the search for single-keyword without attribute revocation, the data user cannot quickly detect the validity of the ciphertext returned by the cloud service provider. Therefore, this paper proposes an authorization of searchable CP-ABE scheme with attribute revocation and applies the scheme to the cloud computing environment. The data user to send the authorization information to the authorization server for authorization, assists the data user to effectively detect the ciphertext information returned by the cloud service provider while supporting the revocation of the user attribute in a fine-grained access control structure without updating the key during revocation stage. In the random oracle model based on the calculation of Diffie-Hellman problem, it is proved that the scheme can satisfy the indistinguishability of ciphertext and search trapdoor. Finally, the performance analysis shows that the scheme has higher computational efficiency.

In this paper, we present a combinatorial testing methodology for testing web applications in regards to SQL injection vulnerabilities. We describe three attack grammars that were developed and used to generate concrete attack vectors. Furthermore, we present and evaluate two different oracles used to observe the application's behavior when subjected to such attack vectors. We also present a prototype tool called SQLInjector capable of automated SQL injection vulnerability testing for web applications. The developed methodology can be applied to any web application that uses server side scripting and HTML for handling user input and has a SQL database backend. Our approach relies on the use of a database proxy, making this a gray-box testing method. We establish the effectiveness of the proposed tool with the WAVSEP verification framework and conduct a case study on real-world web applications, where we are able to discover both known vulnerabilities and additional previously undiscovered flaws.

The need for cybersecurity knowledge and skills is constantly growing as our lives become more integrated with the digital world. In order to meet this demand, educational institutions must continue to innovate within the field of cybersecurity education and make this educational process as effective and efficient as possible. We seek to accomplish this goal by taking an existing cybersecurity educational technology and adding automated grading and assessment functionality to it. This will reduce costs and maximize scalability by reducing or even eliminating the need for human graders.

This paper introduces a secured and distributed Big Data storage scheme with multiple authorizations. It divides the Big Data into small chunks and distributes them through multiple Cloud locations. The Shamir's Secret Sharing and Secure Hash Algorithm are employed to provide the security and authenticity of this work. The proposed methodology consists of two phases: the distribution and retrieving phases. The distribution phase comprises three operations of dividing, encrypting, and distribution. The retrieving phase performs collecting and verifying operations. To increase the security level, the encryption key is divided into secret shares using Shamir's Algorithm. Moreover, the Secure Hash Algorithm is used to verify the Big Data after retrieving from the Cloud. The experimental results show that the proposed design can reconstruct a distributed Big Data with good speed while conserving the security and authenticity properties.

The Dark Web, a conglomerate of services hidden from search engines and regular users, is used by cyber criminals to offer all kinds of illegal services and goods. Multiple Dark Web offerings are highly relevant for the cyber security domain in anticipating and preventing attacks, such as information about zero-day exploits, stolen datasets with login information, or botnets available for hire. In this work, we analyze and discuss the challenges related to information gathering in the Dark Web for cyber security intelligence purposes. To facilitate information collection and the analysis of large amounts of unstructured data, we present BlackWidow, a highly automated modular system that monitors Dark Web services and fuses the collected data in a single analytics framework. BlackWidow relies on a Docker-based micro service architecture which permits the combination of both preexisting and customized machine learning tools. BlackWidow represents all extracted data and the corresponding relationships extracted from posts in a large knowledge graph, which is made available to its security analyst users for search and interactive visual exploration. Using BlackWidow, we conduct a study of seven popular services on the Deep and Dark Web across three different languages with almost 100,000 users. Within less than two days of monitoring time, BlackWidow managed to collect years of relevant information in the areas of cyber security and fraud monitoring. We show that BlackWidow can infer relationships between authors and forums and detect trends for cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding leaked data and preparation for malicious activity.

With the recent boom in the cryptocurrency market, hackers have been on the lookout to find novel ways of commandeering users' machine for covert and stealthy mining operations. In an attempt to expose such under-the-hood practices, this paper explores the issue of browser cryptojacking, whereby miners are secretly deployed inside browser code without the knowledge of the user. To this end, we analyze the top 50k websites from Alexa and find a noticeable percentage of sites that are indulging in this exploitative exercise often using heavily obfuscated code. Furthermore, mining prevention plug-ins, such as NoMiner, fail to flag such cleverly concealed instances. Hence, we propose a machine learning solution based on hardware-assisted profiling of browser code in real-time. A fine-grained micro-architectural footprint allows us to classify mining applications with \textbackslashtextgreater99% accuracy and even flags them if the mining code has been heavily obfuscated or encrypted. We build our own browser extension and show that it outperforms other plug-ins. The proposed design has negligible overhead on the user's machine and works for all standard off-the-shelf CPUs.