Biblio

A Network consists of many nodes among which there may be a presence of misbehavior nodes. Delay Tolerant Network (DTN) is a network where the disconnections occur frequently. Store, carry and forward method is followed in DTN. The serious threat against routing in DTN is the selfish behavior. The main intention of selfish node is to save its own energy. Detecting the selfish node in DTN is very difficult. In this paper, a probabilistic misbehavior detection scheme called MAXTRUST has been proposed. Trusted Authority (TA) has been introduced in order to detect the behavior of the nodes periodically based on the task, forwarding history and contact history evidence. After collecting all the evidences from the nodes, the TA would check the inspection node about its behavior. The actions such as punishment or compensation would be given to that particular node based on its behavior. The TA performs probabilistic checking, in order to ensure security at a reduced cost. To further improve the efficiency, dynamic probabilistic inspection has been demonstrated using game theory analysis. The simulation results show the effectiveness and efficiency of the MAXTRUST scheme.

In this paper a new physical layer security method is proposed against eavesdropping attacks. Our purpose is to demonstrate that performance of the legitimate receiver can be increased and performance of the eavesdropper can be decreased by matching between the roll of factors of root raised cosine filters in the transmitter and receiver. Through the matching between the roll of factors (a), a performance difference is generated between the legitimate receiver and the eavesdropper. By using three software defined radio nodes error vector magnitude of the legitimate receiver and the eavesdropper is measured according to roll of factors. Performance differences the receiver are demonstrated when the roll off factor is matched and mismatched.

Text-based CAPTCHAs are still commonly used to attempt to prevent automated access to web services. By displaying an image of distorted text, they attempt to create a challenge image that OCR software can not interpret correctly, but a human user can easily determine the correct response to. This work focuses on a CAPTCHA used by a popular Chinese language question-and-answer website and how resilient it is to modern machine learning methods. While the majority of text-based CAPTCHAs focus on transcription tasks, the CAPTCHA solved in this work is based on localization of inverted symbols in a distorted image. A convolutional neural network (CNN) was created to evaluate the likelihood of a region in the image belonging to an inverted character. It is used with a feature map and clustering to identify potential locations of inverted characters. Training of the CNN was performed using curriculum learning and compared to other potential training methods. The proposed method was able to determine the correct response in 95.2% of cases of a simulated CAPTCHA and 67.6% on a set of real CAPTCHAs. Potential methods to increase difficulty of the CAPTCHA and the success rate of the automated solver are considered.

Multicast distribution employs the model of many-to-many so that it is a more efficient way of data delivery compared to traditional one-to-one unicast distribution, which can benefit many applications such as media streaming. However, the lack of security features in its nature makes multicast technology much less popular in an open environment such as the Internet. Internet Service Providers (ISPs) take advantage of IP multicast technology's high efficiency of data delivery to provide Internet Protocol Television (IPTV) to their users. But without the full control on their networks, ISPs cannot collect revenue for the services they provide. Secure Internet Group Management Protocol (SIGMP), an extension of Internet Group Management Protocol (IGMP), and Group Security Association Management Protocol (GSAM), have been proposed to enforce receiver access control at the network level of IP multicast. In this paper, we analyze operational details and issues of both SIGMP and GSAM. An examination of the performance of both protocols is also conducted.

In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can offer a cost-effective power management and supply alternative to national power grid connections. RCSMG architectures handle communications over distributed lossy networks to minimize operation costs. However, the unreliable nature of lossy networks makes privacy an important consideration. Existing anonymisation works on data perturbation work mainly by distortion with additive noise. Apply these solutions to RCSMGs is problematic, because deliberate noise additions must be distinguishable both from system and adversarial generated noise. In this paper, we present a brief survey of privacy risks in RCSMGs centered on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.

The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general-purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of instability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power flow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeoff between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.

Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere. To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.

The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.

A popular form of software reuse is the use of open source software libraries hosted on centralized code repositories, such as Maven or npm. Developers only need to declare dependencies to external libraries, and automated tools make them available to the workspace of the project. Recent incidents, such as the Equifax data breach and the leftpad package removal, demonstrate the difficulty in assessing the severity, impact and spread of bugs in dependency networks. While dependency checkers are being adapted as a counter measure, they only provide indicative information. To remedy this situation, we propose a fine-grained dependency network that goes beyond packages and into call graphs. The result is a versioned ecosystem-level call graph. In this paper, we outline the process to construct the proposed graph and present a preliminary evaluation of a security issue from a core package to an affected client application.

Self-assembled semiconductor quantum dots possess an intrinsic geometric symmetry due to the crystal periodic structure. In order to systematically analyze the symmetric properties of quantum dots' bound states resulting only from geometric confinement, we apply group representation theory. We label each bound state for two kinds of popular quantum dot shapes: pyramid and half ellipsoid with the irreducible representation of the corresponding symmetric groups, i.e., C4v and C2v, respectively. Our study completes all the possible irreducible representation cases of groups C4v and C2v. Using the character theory of point groups, we predict the selection rule for electric dipole induced transitions. We also investigate the impact of quantum dot aspect ratio on the symmetric properties of the state wavefunction. This research provides a solid foundation to continue exploring quantum dot symmetry reduction or broken phenomena because of strain, band-mixing and shape irregularity. The results will benefit the researchers who are interested in quantum dot symmetry related effects such as absorption or emission spectra, or those who are studying quantum dots using analytical or numerical simulation approaches.

Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes. To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage-guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzer-generated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation and false positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. We have evaluated T-Fuzz on the DARPA Cyber Grand Challenge dataset, LAVA-M dataset and 4 real-world programs (pngfix, tiffinfo, magick and pdftohtml). For the CGC dataset, T-Fuzz finds bugs in 166 binaries, Driller in 121, and AFL in 105. In addition, found 3 new bugs in previously-fuzzed programs and libraries.

The benefits of data distribution to multiple storage platforms with different characteristics have been widely acknowledged. Such systems are more tolerant to outages and bottlenecks and allow for more flexible policies regarding cost reduction, security and workload diversity. To leverage platforms simultaneously additional orchestration steps are needed. Existing approaches either implement such steps in the application's source code, resulting to minimum reusability across applications, or handle them at the infrastructure level. The latter usually involves over-engineering to handle different application behaviors and binds the system to a specific infrastructure. In this paper we present a middle-ware that decouples the I/O path from the application's source code and performs in-transit processing before data lands on the storage platforms. Abstracting the I/O process as a graph of reusable components allows the developers to easily implement complex storage solutions without the burden of writing custom code. Similarly, the administrators can create their own graph that reflects the infrastructure setup and append it to the preceding graph, so that various policies and infrastructure-related changes can be performed transparently to the application. Users can also extend the graph chain to enhance the application's functionality by using plug-ins. Our approach eliminates the need for custom I/O management code and allows for the applications to evolve independently of the storage back-end. To evaluate our system we employed a secure web service scenario that was seamlessly adapted to the changes in its storage back-end.

IoT device usually has an associated application to facilitate customers' interactions with the device, and customers need to register an account to use this application as well. Due to the popularity of mobile phone, a customer is encouraged to register an account with his own mobile phone number. After binding the device to his account, the customer can control his device remotely with his smartphone. When a customer forgets his password, he can use his mobile phone to receive a verification code that is sent by the Short Message Service (SMS) to authenticate and reset his password. If an attacker gains this code, he can steal the victim's account (reset password or login directly) to control the IoT device. Although IoT device vendors have already deployed a set of security countermeasures to protect account such as setting expiration time for SMS authentication code, HTTP encryption, and application packing, this paper shows that existing IoT account password reset via SMS authentication code are still vulnerable to brute-force attacks. In particular, we present an automatic brute-force attack to bypass current protections and then crack IoT device user account. Our preliminary study on popular IoT devices such as smart lock, smart watch, smart router, and sharing car has discovered six account login zero-day vulnerabilities.

In this paper, the rotor unbalanced magnetic pull (UMP) characteristics of different field winding inter-turn short-circuit (FWISC) positions in turbo-generator are studied. Firstly, the qualitative analysis on the air gap magnetic flux density (MFD), as well as the rotor UMPs in X-direction and Y-direction, is carried out. Then the finite element numerical simulations are respectively taken to calculate the quantitative data of rotor UMP under normal condition and three different short-circuit positions. Finally, the variation rules based on rotor UMP characteristics by experimental analysis are obtained. It is shown that the occurrence of FWISC will induce generally fundamental-frequency UMP acting on the rotor in X-direction. Moreover, the different positions of FWISC are found to be sensitive to the rotor UMP amplitudes. The closer the short-circuit position is to the big teeth, the larger the rotor UMP amplitudes in X-direction will be.

The steady decline of IP transit prices in the past two decades has helped fuel the growth of traffic demands in the Internet ecosystem. Despite the declining unit pricing, bandwidth costs remain significant due to ever-increasing scale and reach of the Internet, combined with the price disparity between the Internet's core hubs versus remote regions. In the meantime, cloud providers have been auctioning underutilized computing resources in their marketplace as spot instances for a much lower price, compared to their on-demand instances. This state of affairs has led the networking community to devote extensive efforts to cloud-assisted networks - the idea of offloading network functionality to cloud platforms, ultimately leading to more flexible and highly composable network service chains.We initiate a critical discussion on the economic and technological aspects of leveraging cloud-assisted networks for Internet-scale interconnections and data transfers. Namely, we investigate the prospect of constructing a large-scale virtualized network provider that does not own any fixed or dedicated resources and runs atop several spot instances. We construct a cloud-assisted overlay as a virtual network provider, by leveraging third-party cloud spot instances. We identify three use case scenarios where such approach will not only be economically and technologically viable but also provide performance benefits compared to current commercial offerings of connectivity and transit providers.

Deep neural networks (DNNs) exhibit excellent performance in machine learning tasks such as image recognition, pattern recognition, speech recognition, and intrusion detection. However, the usage of adversarial examples, which are intentionally corrupted by noise, can lead to misclassification. As adversarial examples are serious threats to DNNs, both adversarial attacks and methods of defending against adversarial examples have been continuously studied. Zero-day adversarial examples are created with new test data and are unknown to the classifier; hence, they represent a more significant threat to DNNs. To the best of our knowledge, there are no analytical studies in the literature of zero-day adversarial examples with a focus on attack and defense methods through experiments using several scenarios. Therefore, in this study, zero-day adversarial examples are practically analyzed with an emphasis on attack and defense methods through experiments using various scenarios composed of a fixed target model and an adaptive target model. The Carlini method was used for a state-of-the-art attack, while an adversarial training method was used as a typical defense method. We used the MNIST dataset and analyzed success rates of zero-day adversarial examples, average distortions, and recognition of original samples through several scenarios of fixed and adaptive target models. Experimental results demonstrate that changing the parameters of the target model in real time leads to resistance to adversarial examples in both the fixed and adaptive target models.

Self-Adaptive Systems (SAS) are revolutionizing many aspects of our society. From server clusters to autonomous vehicles, SAS are becoming more ubiquitous and essential to our world. Security is frequently a priority for these systems as many SAS conduct mission-critical operations, or work with sensitive information. Fortunately, security is being more recognized as an indispensable aspect of virtually all aspects of computing systems, in all phases of software development. Despite the growing prominence in security, from computing education to vulnerability detection systems, it is just another concern of creating good software. Despite how critical security is, it is a quality attribute like other aspects such as reliability, stability, or adaptability in a SAS.

Zero-day Use-After-Free (UAF) vulnerabilities are increasingly popular and highly dangerous, but few mitigations exist. We introduce a new pointer-analysis-based static analysis, CRed, for finding UAF bugs in multi-MLOC C source code efficiently and effectively. CRed achieves this by making three advances: (i) a spatio-temporal context reduction technique for scaling down soundly and precisely the exponential number of contexts that would otherwise be considered at a pair of free and use sites, (ii) a multi-stage analysis for filtering out false alarms efficiently, and (iii) a path-sensitive demand-driven approach for finding the points-to information required. We have implemented CRed in LLVM-3.8.0 and compared it with four different state-of-the-art static tools: CBMC (model checking), Clang (abstract interpretation), Coccinelle (pattern matching), and Supa (pointer analysis) using all the C test cases in Juliet Test Suite (JTS) and 10 open-source C applications. For the ground-truth validated with JTS, CRed detects all the 138 known UAF bugs as CBMC and Supa do while Clang and Coccinelle miss some bugs, with no false alarms from any tool. For practicality validated with the 10 applications (totaling 3+ MLOC), CRed reports 132 warnings including 85 bugs in 7.6 hours while the existing tools are either unscalable by terminating within 3 days only for one application (CBMC) or impractical by finding virtually no bugs (Clang and Coccinelle) or issuing an excessive number of false alarms (Supa).

Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a cost-efficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.

Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.

The recent development of IoT technologies and cloud storages, many types of information including private information have been gradually outsourced. For such a situation, new convenient functionalities such as arithmetic and keyword search on ciphertexts are required to allow users to retrieve information without leaking any information. Especially, searchable encryptions have been paid much attention to realize a keyword search on an encrypted domain. In addition, an architecture of searchable symmetric encryption (SSE) is a suitable and efficient solution for data outsourcing. In this paper, we focus on an SSE scheme which employs a secure index for searching a keyword with optimal search time. In the conventional studies, it has been widely considered that the scheme searches whether a queried keyword is contained in encrypted documents. On the other hand, we additionally take into account the location of a queried keyword in documents by targeting a matrix-type data format. It enables a manager to search personal information listed per line or column in CSV-like format data.

Cyber-physical systems (CPS) and their Internet of Things (IoT) components are repeatedly subject to various attacks targeting weaknesses in their firmware. For that reason emerges an imminent demand for secure update mechanisms that not only include specific systems but cover all parts of the critical infrastructure. In this paper we introduce a theoretical concept for a secure CPS device update and verification mechanism and provide information on handling hardware-based security incorporating trusted platform modules (TPM) on those CPS devices. We will describe secure communication channels by state of the art technology and also integrity measurement mechanisms to ensure the system is in a known state. In addition, a multi-level fail-over concept is presented, ensuring continuous patching to minimize the necessity of restarting those systems.

Reliance on off-site untrusted fabrication facilities has given rise to several threats such as intellectual property (IP) piracy, overbuilding and hardware Trojans. Logic locking is a promising defense technique against such malicious activities that is effected at the silicon layer. Over the past decade, several logic locking defenses and attacks have been presented, thereby, enhancing the state-of-the-art. Nevertheless, there has been little research aiming to demonstrate the applicability of logic locking with large-scale multi-million-gate industrial designs consisting of multiple IP blocks with different security requirements. In this work, we take on this challenge to successfully lock a multi-million-gate system-on-chip (SoC) provided by DARPA by taking it all the way to GDSII layout. We analyze how specific features, constraints, and security requirements of an IP block can be leveraged to lock its functionality in the most appropriate way. We show that the blocks of an SoC can be locked in a customized manner at 0.5%, 15.3%, and 1.5% chip-level overhead in power, performance, and area, respectively.

With wide application of networked control systems(N CSs), NCSs security have encountered severe challenges. In this paper, we propose a robust event-triggered controller design method under replay attacks, and the control signal on the plant is updated only when the event-triggering condition is satisfied. We develop a general random replay attack model rather than predetermined specific patterns for the occurrences of replay attacks, which allows to obtain random states to replay. We show that the proposed event-triggered control (ETC) scheme, if well designed, can tolerate some consecutive replay attacks, without affecting the corresponding closed-loop system stability and performance. A numerical examples is finally given to illustrate the effectiveness of our method.

Recently, big data and artificial intelligence (AI) have been introduced into medical services. When personal information is stored in a shared database, that data must be encrypted, which, in turn, makes it difficult to extract only the necessary information. Searchable encryption has now been proposed to extract, or search, encrypted data without decrypting it. However, all previous studies regarding searchable encryption are software-based. This paper proposes a searchable encryption system embedded in dedicated hardware and evaluates its circuit size.