Biblio

The moving network target defense (MTD) based approach to security aims to design and develop capabilities to dynamically change the attack surfaces to make it more difficult for attackers to strike. One such capability is to dynamically change the IP addresses of subnetworks in unpredictable ways in an attempt to disrupt the ability of an attacker to collect the necessary reconnaissance information to launch successful attacks. In particular, Denial of Service (DoS) and worms represent examples of distributed attacks that can potentially propagate through networks very quickly, but could also be disrupted by MTD. Conversely, MTD are also disruptive to regular users. For example, when IP addresses are changed dynamically it is no longer effective to use DNS caches for IP address resolutions before any communication can be performed. In this work we take another approach. We note that the deployment of MTD could be triggered through the use of light-weight intrusion detection. We demonstrate that the neuro-evolution of augmented topologies algorithm (NEAT) has the capacity to construct detectors that operate on packet data and produce sparse topologies, hence are real-time in operation. Benchmarking under examples of DoS and worm attacks indicates that NEAT detectors can be constructed from relatively small amounts of data and detect attacks approx. 90% accuracy. Additional experiments with the open-ended evolution of code modules through genetic program teams provided detection rates approaching 100%. We believe that adopting such an approach to MTB a more specific deployment strategy that is less invasive to legitimate users, while disrupting the actions of malicious users.

We present algorithmic techniques for parallel PDE solvers that leverage numerical smoothness properties of physics simulation to detect and correct silent data corruption within local computations. We initially model such silent hardware errors (which are of concern for extreme scale) via injected DRAM bit flips. Our mitigation approach generalizes previously developed "robust stencils" and uses modified linear algebra operations that spatially interpolate to replace large outlier values. Prototype implementations for 1D hyperbolic and 3D elliptic solvers, tested on up to 2048 cores, show that this error mitigation enables tolerating orders of magnitude higher bit-flip rates. The runtime overhead of the approach generally decreases with greater solver scale and complexity, becoming no more than a few percent in some cases. A key advantage is that silent data corruption can be handled transparently with data in cache, reducing the cost of false-positive detections compared to rollback approaches.

Bitcoin provides two incentives for miners: block rewards and transaction fees. The former accounts for the vast majority of miner revenues at the beginning of the system, but it is expected to transition to the latter as the block rewards dwindle. There has been an implicit belief that whether miners are paid by block rewards or transaction fees does not affect the security of the block chain. We show that this is not the case. Our key insight is that with only transaction fees, the variance of the block reward is very high due to the exponentially distributed block arrival time, and it becomes attractive to fork a "wealthy" block to "steal" the rewards therein. We show that this results in an equilibrium with undesirable properties for Bitcoin's security and performance, and even non-equilibria in some circumstances. We also revisit selfish mining and show that it can be made profitable for a miner with an arbitrarily low hash power share, and who is arbitrarily poorly connected within the network. Our results are derived from theoretical analysis and confirmed by a new Bitcoin mining simulator that may be of independent interest.We discuss the troubling implications of our results for Bitcoin's future security and draw lessons for the design of new cryptocurrencies.

Red teams play a critical part in assessing the security of a network by actively probing it for weakness and vulnerabilities. Unlike penetration testing - which is typically focused on exploiting vulnerabilities - red teams assess the entire state of a network by emulating real adversaries, including their techniques, tactics, procedures, and goals. Unfortunately, deploying red teams is prohibitive: cost, repeatability, and expertise all make it difficult to consistently employ red team tests. We seek to solve this problem by creating a framework for automated red team emulation, focused on what the red team does post-compromise - i.e., after the perimeter has been breached. Here, our program acts as an automated and intelligent red team, actively moving through the target network to test for weaknesses and train defenders. At its core, our framework uses an automated planner designed to accurately reason about future plans in the face of the vast amount of uncertainty in red teaming scenarios. Our solution is custom-developed, built on a logical encoding of the cyber environment and adversary profiles, using techniques from classical planning, Markov decision processes, and Monte Carlo simulations. In this paper, we report on the development of our framework, focusing on our planning system. We have successfully validated our planner against other techniques via a custom simulation. Our tool itself has successfully been deployed to identify vulnerabilities and is currently used to train defending blue teams.

In ad-hoc networks, data messages are transmitted from a source wireless node to a destination one along a wireless multihop transmission route consisting of a sequence of intermediate wireless nodes. Each intermediate wireless node forwards data messages to its next-hop wireless node. Here, a wireless signal carrying the data message is broadcasted by using an omni antenna and it is not difficult for a eavesdropper wireless node to overhear the wireless signal to get the data message. Some researches show that it is useful to transmit noise wireless signal which collide to the data message wireless signal in order for interfering the overhearing. However, some special devices such as directional antennas and/or high computation power for complicated signal processing are required. For wireless multihop networks with huge number of wireless nodes, small and cheap wireless nodes are mandatory for construction of the network. This paper proposes the method for interfering the overhearing by the eavesdropper wireless nodes where routing protocol and data message transmission protocol with cooperative noise signal transmissions by 1-hop and 2-hop neighbor wireless nodes of each intermediate wireless node.

An interactive method for segmentation and isosurface extraction of medical volume data is proposed. In conventional methods, users decompose a volume into multiple regions iteratively, segment each region using a threshold, and then manually clean the segmentation result by removing clutter in each region. However, this is tedious and requires many mouse operations from different camera views. We propose an alternative approach whereby the user simply applies painting operations to the volume using tools commonly seen in painting systems, such as flood fill and brushes. This significantly reduces the number of mouse and camera control operations. Our technical contribution is in the introduction of the threshold field, which assigns spatially-varying threshold values to individual voxels. This generalizes discrete decomposition of a volume into regions and segmentation using a constant threshold in each region, thereby offering a much more flexible and efficient workflow. This paper describes the details of the user interaction and its implementation. Furthermore, the results of a user study are discussed. The results indicate that the proposed method can be a few times faster than a conventional method.

Internet of Things(IoT) is the next big boom in the networking field. The vision of IoT is to connect daily used objects (which have the ability of sensing and actuation) to the Internet. This may or may or may not involve human. IoT field is still maturing and has many open issues. We build up on the security issues. As the devices have low computational power and low memory the existing security mechanisms (which are a necessity) should also be optimized accordingly or a clean slate approach needs to be followed. This is a survey paper to focus on the security aspects of IoT. We further also discuss the open challenges in this field.

Security threats may hinder the large scale adoption of the emerging Internet of Things (IoT) technologies. Besides efforts have already been made in the direction of data integrity preservation, confidentiality and privacy, several issues are still open. The existing solutions are mainly based on encryption techniques, but no attention is actually paid to key management. A clever key distribution system, along with a key replacement mechanism, are essentials for assuring a secure approach. In this paper, two popular key management systems, conceived for wireless sensor networks, are integrated in a real IoT middleware and compared in order to evaluate their performance in terms of overhead, delay and robustness towards malicious attacks.

The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes.

The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems.

Intrusion detection using multiple security devices has received much attention recently. The large volume of information generated by these tools, however, increases the burden on both computing resources and security administrators. Moreover, attack detection does not improve as expected if these tools work without any coordination. In this work, we propose a simple method to join information generated by security monitors with diverse data formats. We present a novel intrusion detection technique that uses unsupervised clustering algorithms to identify malicious behavior within large volumes of diverse security monitor data. First, we extract a set of features from network-level and host-level security logs that aid in detecting malicious host behavior and flooding-based network attacks in an enterprise network system. We then apply clustering algorithms to the separate and joined logs and use statistical tools to identify anomalous usage behaviors captured by the logs. We evaluate our approach on an enterprise network data set, which contains network and host activity logs. Our approach correctly identifies and prioritizes anomalous behaviors in the logs by their likelihood of maliciousness. By combining network and host logs, we are able to detect malicious behavior that cannot be detected by either log alone.

Cloud and its transactions have emerged as a major challenge. This paper aims to come up with an efficient and best possible way to transfer data in cloud computing environment. This goal is achieved with the help of Soft Computing Techniques. Of the various techniques such as fuzzy logic, genetic algorithm or neural network, the paper proposes an effective method of intrusion detection using genetic algorithm. The selection of the optimized path for the data transmission proved to be effective method in cloud computing environment. Network path optimization increases data transmission speed making intrusion in network nearly impossible. Intruders are forced to act quickly for intruding the network which is quite a tough task for them in such high speed data transmission network.

We discuss a key engineering challenge in implementing the Identifier- Locator Network Protocol (ILNP), as described in IRTF Experimental RFCs 6740–6748: enabling legacy applications that use the C sockets API. We have built the first two OS kernel implementations of ILNPv6 (ILNP as a superset of IPv6), in both the Linux OS kernel and the FreeBSD OS kernel. Our evaluation is in comparison with IPv6, in the context of a topical and challenging scenario: host mobility implemented as a purely end-to-end function. Our experiments show that ILNPv6 has excellent potential for deployment using existing IPv6 infrastructure, whilst offering the new properties and functionality of ILNP.

This paper presents IPAS, an instruction duplication technique that protects scientific applications from silent data corruption (SDC) in their output. The motivation for IPAS is that, due to natural error masking, only a subset of SDC errors actually affects the output of scientific codes—we call these errors silent output corruption (SOC) errors. Thus applications require duplication only on code that, when affected by a fault, yields SOC. We use machine learning to learn code instructions that must be protected to avoid SOC, and, using a compiler, we protect only those vulnerable instructions by duplication, thus significantly reducing the overhead that is introduced by instruction duplication. In our experiments with five workloads, IPAS reduces the percentage of SOC by up to 90% with a slowdown that ranges between 1.04x and 1.35x, which corresponds to as much as 47% less slowdown than state-of-the-art instruction duplication techniques.

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.

Biometric is uses to identify authorized person based on specific physiological or behavioral features. Template protection is a crucial requirement when designing an authentication system, where the template could be modified by attacker. Hill Cipher is a block cipher and symmetric key algorithm it has several advantages such as simplicity, high speed and high throughput can be used to protect Biometric Template. Unfortunately, Hill Cipher has some disadvantages such as takes smaller sizes of blocks, very simple and vulnerable for exhaustive key search attack and known plain text attack, also the key matrix which entered should be invertible. This paper proposed an enhancement to overcome these drawbacks of Hill Cipher by using a large and random key with large data block, beside overcome the Invertible-key Matrix problem. The efficiency of encryption has been checked out by Normalized Correlation Coefficient (NCC) and running time.

A Mobile Ad hoc Network (MANET) is a spontaneous network consisting of wireless nodes which are mobile and self-configuring in nature. Devices in MANET can move freely in any direction independently and change its link frequently to other devices. MANET does not have centralized infrastructure and its characteristics makes this network vulnerable to various kinds of attacks. Data transfer is a major problem due to its nature of unreliable wireless medium. Commonly used technique for secure transmission in wireless network is cryptography. Use of cryptography key is often involved in most of cryptographic techniques. Key management is main component in security issues of MANET and various schemes have been proposed for it. In this paper, a study on various kinds of key management techniques in MANET is presented.

Today's cellular core, which connects the radio access network to the Internet, relies on fixed hardware appliances placed at a few dedicated locations and uses relatively static routing policies. As such, today's core design has key limitations—it induces inefficient provisioning tradeoffs and is poorly equipped to handle overload, failure scenarios, and diverse application requirements. To address these limitations, ongoing efforts envision "clean slate" solutions that depart from cellular standards and routing protocols; e.g., via programmable switches at base stations and per-flow SDN-like orchestration. The driving question of this work is to ask if a clean-slate redesign is necessary and if not, how can we design a flexible cellular core that is minimally disruptive. We propose KLEIN, a design that stays within the confines of current cellular standards and addresses the above limitations by combining network functions virtualization with smart resource management. We address key challenges w.r.t. scalability and responsiveness in realizing KLEIN via backwards-compatible orchestration mechanisms. Our evaluations through data-driven simulations and real prototype experiments using OpenAirInterface show that KLEIN can scale to billions of devices and is close to optimal for wide variety of traffic and deployment parameters.

Causality inference, such as dynamic taint anslysis, has many applications (e.g., information leak detection). It determines whether an event e is causally dependent on a preceding event c during execution. We develop a new causality inference engine LDX. Given an execution, it spawns a slave execution, in which it mutates c and observes whether any change is induced at e. To preclude non-determinism, LDX couples the executions by sharing syscall outcomes. To handle path differences induced by the perturbation, we develop a novel on-the-fly execution alignment scheme that maintains a counter to reflect the progress of execution. The scheme relies on program analysis and compiler transformation. LDX can effectively detect information leak and security attacks with an average overhead of 6.08% while running the master and the slave concurrently on separate CPUs, much lower than existing systems that require instruction level monitoring. Furthermore, it has much better accuracy in causality inference.

This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

From pencils to commercial aircraft, every man-made object must be designed and manufactured. When it is cheaper or easier to steal a design or a manufacturing process specification than to invent one's own, the incentive for theft is present. As more and more manufacturing data comes online, incidents of such theft are increasing. In this paper, we present a side-channel attack on manufacturing equipment that reveals both the form of a product and its manufacturing process, i.e., exactly how it is made. In the attack, a human deliberately or accidentally places an attack-enabled phone close to the equipment or makes or receives a phone call on any phone nearby. The phone executing the attack records audio and, optionally, magnetometer data. We present a method of reconstructing the product's form and manufacturing process from the captured data, based on machine learning, signal processing, and human assistance. We demonstrate the attack on a 3D printer and a CNC mill, each with its own acoustic signature, and discuss the commonalities in the sensor data captured for these two different machines. We compare the quality of the data captured with a variety of smartphone models. Capturing data from the 3D printer, we reproduce the form and process information of objects previously unknown to the reconstructors. On average, our accuracy is within 1 mm in reconstructing the length of a line segment in a fabricated object's shape and within 1 degree in determining an angle in a fabricated object's shape. We conclude with recommendations for defending against these attacks.