Biblio

The IEEE 802.11s amendment for wireless mesh networks does not provide incentives for stations to cooperate and is particularly vulnerable to selfish insider attacks in which a legitimate network participant hopes to increase its QoS at the expense of others. In this tutorial we describe various attacks that can be executed against 802.11s networks and also analyze existing attacks and identify new ones. We also discuss possible countermeasures and detection methods and attempt to quantify the threat of the attacks to determine which of the 802.11s vulnerabilities need to be secured with the highest priority.

This paper presents the development of a control circuit to enhance the performances of LED lamps. In this direction, a comparison between the luminous intensity of normal LED based lamps and mid-power ones, for both continuous and switching conditions has been made. The already well know control technologies were analyzed and a study was conducted to increase the lighting performances by rising the operating frequency and magnifying the contribution of remanence effect and thus increasing the efficiency of the light source. To achieve this, in the first stage of the project the power and control circuits have been modeled, related to desired parameters and tested in simulation software. In the second stage, the proposed circuit was implemented by functional blocks and in the last stage, tests were made on the circuit and on light sources in order to process the results. The power consumption has been decreased nearly to a half of it and the luminous flux raised with 15% due to overcurrent and remanence effect that we used.

Memory corruption bugs in software written in low-level languages like C or C++ are one of the oldest problems in computer security. The lack of safety in these languages allows attackers to alter the program's behavior or take full control over it by hijacking its control flow. This problem has existed for more than 30 years and a vast number of potential solutions have been proposed, yet memory corruption attacks continue to pose a serious threat. Real world exploits show that all currently deployed protections can be defeated. This paper sheds light on the primary reasons for this by describing attacks that succeed on today's systems. We systematize the current knowledge about various protection techniques by setting up a general model for memory corruption attacks. Using this model we show what policies can stop which attacks. The model identifies weaknesses of currently deployed techniques, as well as other proposed protections enforcing stricter policies. We analyze the reasons why protection mechanisms implementing stricter polices are not deployed. To achieve wide adoption, protection mechanisms must support a multitude of features and must satisfy a host of requirements. Especially important is performance, as experience shows that only solutions whose overhead is in reasonable bounds get deployed. A comparison of different enforceable policies helps designers of new protection mechanisms in finding the balance between effectiveness (security) and efficiency. We identify some open research problems, and provide suggestions on improving the adoption of newer techniques.

Infrastructure-as-a-Service (IaaS) clouds such as OpenStack consist of two kinds of nodes in their infrastructure: control nodes and compute nodes. While control nodes run all critical services, compute nodes host virtual machines of customers. Given the large number of compute nodes, and the fact that they are hosting VMs of (possibly malicious) customers, it is possible that some of the compute nodes may be compromised. This paper examines the impact of such a compromise. We focus on OpenStack, a popular open-source cloud plat- form that is widely adopted. We show that attackers com- promising a single compute node can extend their controls over the entire cloud infrastructure. They can then gain free access to resources that they have not paid for, or even bring down the whole cloud to affect all customers. This startling result stems from the cloud platform's misplaced trust, which does not match today's threats. To overcome the weakness, we propose a new system, called SOS , for hardening OpenStack. SOS limits trust on compute nodes. SOS consists of a framework that can enforce a wide range of security policies. Specifically, we applied mandatory access control and capabilities to con- fine interactions among different components. Effective confinement policies are generated automatically. Furthermore, SOS requires no modifications to the OpenStack. This has allowed us to deploy SOS on multiple versions of OpenStack. Our experimental results demonstrate that SOS is scalable, incurs negligible overheads and offers strong protection.

Bitcoin provides freshness properties by forming a blockchain where each block is associated with its timestamp and the previous block. Due to these properties, the Bitcoin protocol is being used as a decentralized, trusted, and secure timestamping service. Although Bitcoin participants which create new blocks cannot modify their order, they can manipulate timestamps almost undetected. This undermines the Bitcoin protocol as a reliable timestamping service. In particular, a newcomer that synchronizes the entire blockchain has a little guarantee about timestamps of all blocks. In this paper, we present a simple yet powerful mechanism that increases the reliability of Bitcoin timestamps. Our protocol can provide evidence that a block was created within a certain time range. The protocol is efficient, backward compatible, and surprisingly, currently deployed SSL/TLS servers can act as reference time sources. The protocol has many applications and can be used for detecting various attacks against the Bitcoin protocol.

The aim of this work is to create a new style web browser. The other web browsers can have safety issues and have many ads and popups. The other web browsers can fill up cache with the logging of big history of visited web pages. This app is a light-weight web browser which is both secure and private with no ads and no popups, just the plain Internet shown in full screen. The app does not store all user data, so the navigation of webpages is done in incognito mode. The app was made to open any new HTML5 web page in a secure and private mode with big focus on loading speed of the web pages.

Metropolitan scale WiFi deployments face several challenges including controllability and management, which prohibit the provision of Seamless Access, Quality of Service (QoS) and Security to mobile users. Thus, they remain largely an untapped networking resource. In this work, a SDN-based network architecture is proposed; it is comprised of a distributed network-wide controller and a novel datapath for wireless access points. Virtualization of network functions is employed for configurable user access control as well as for supporting an IP-independent forwarding scheme. The proposed architecture is a flat network across the deployment area, providing seamless connectivity and reachability without the need of intermediary servers over the Internet, enabling thus a wide variety of localized applications, like for instance video surveillance. Also, the provided interface allows for transparent implementation of intra-network distributed cross-layer traffic control protocols that can optimize the multihop performance of the wireless network.
 

This turorial offers a rich blend of theory and practice regarding dimensionality reduction methods, to address the information overload problem in recommender systems. This problem affects our everyday experience while searching for knowledge on a topic. Naive Collaborative Filtering cannot deal with challenging issues such as scalability, noise, and sparsity. We can deal with all the aforementioned challenges by applying matrix and tensor decomposition methods. These methods have been proven to be the most accurate (i.e., Netflix prize) and efficient for handling big data. For each method (SVD, SVD++, timeSVD++, HOSVD, CUR, etc.) we will provide a detailed theoretical mathematical background and a step-by-step analysis, by using an integrated toy example, which runs throughout all parts of the tutorial, helping the audience to understand clearly the differences among factorisation methods.

Today's control systems such as smart environments have the ability to adapt to their environment in order to achieve a set of objectives (e.g., comfort, security and energy savings). This is done by changing their behaviour upon the occurrence of specific events. Building such a system requires to design and implement autonomic loops that collect events and measurements, make decisions and execute the corresponding actions.The design and the implementation of such loops are made difficult by several factors: the complexity of systems with multiple objectives, the risk of conflicting decisions between multiple loops, the inconsistencies that can result from communication errors and hardware failures and the heterogeneity of the devices.In this paper, we propose a design framework for reliable and self-adaptive systems, where multiple autonomic loops can be composed into complex managers, and we consider its application to smart environments. We build upon the proposed framework a generic autonomic loop which combines an automata-based controller that makes correct and coherent decisions, a transactional execution mechanism that avoids inconsistencies, and an abstraction layer that hides the heterogeneity of the devices.We propose patterns for composition of such loops, in parallel, coordinated, and hierarchically, with benefits from the leveraging of automata-based modular constructs, that provides for guarantees on the correct behaviour of the controlled system. We implement our framework with the transactional middleware LINC, the reactive language Heptagon/BZR and the abstraction framework PUTUTU. A case study in the field of building automation is presented to illustrate the proposed framework.

Shared Threat Intelligence is often imperfect. Especially so called Indicator of Compromise might not be well constructed. This might either be the case if the threat only appeared recently and recordings do not allow for construction of high quality Indicators or the threat is only observed by sharing partners lesser capable to model the threat. However, intrusion detection based on imperfect intelligence yields low quality results. Within this paper we illustrate how one is able to overcome these shortcomings in data quality and is able to achieve solid intrusion detection. This is done by assigning individual weights to observables listed in a STIX™ report to express their significance for detection. For evaluation, an automatized toolchain was developed to mimic the Threat Intelligence sharing ecosystem from initial detection over reporting, sharing, and determining compromise by STIX™-formated data. Multiple strategies to detect and attribute a specific threat are compared using this data, leading up to an approach yielding a F1-Score of 0.79.

Security has been a challenging aspect recently in the field of Web Development. A failure to obtain security in web applications may lead to complete destruction of the web application or may cause some loss to the user or the owner. To tackle this, a huge research on how to secure a web app has been going on for quite some time, yet to achieve security in today's modern era is a very difficult and no less than a challenge for web applications. All these things lead only to a vulnerable/faulty source code, formulated in coding such as PHP. Static Source Code analysis (SCSA) tools tend to give a solution to detect vulnerabilities, but they tend to detect vulnerabilities which actually are false positives, which leads to excess code reexamination. The proposed system will tackle the current situation of SCSA. This will be achieved by two additional modules to SCSA i.e. Taint analysis with False Positive Predictor which will detect and segregate the true vulnerable code from false positives respectively. The proposed system will be used by the Web Application programmers during testing of web application.

With the advancement of computing and communication technologies, data transmission in the internet are getting bigger and faster. However, it is necessary to secure the data to prevent fraud and criminal over the internet. Furthermore, most of the data related to statistics requires to be analyzed securely such as weather data, health data, financial and other services. This paper presents an implementation of cloud security using homomorphic encryption for data analytic in the cloud. We apply the homomorphic encryption that allows the data to be processed without being decrypted. Experimental results show that, for the polynomial degree 26, 28, and 210, the total executions are 2.2 ms, 4.4 ms, 25 ms per data, respectively. The implementation is useful for big data security such as for environment, financial and hospital data analytics.

User tracking on the Internet can come in various forms, e.g., via cookies or by fingerprinting web browsers. A technique that got less attention so far is user tracking based on TLS and specifically based on the TLS session resumption mechanism. To the best of our knowledge, we are the first that investigate the applicability of TLS session resumption for user tracking. For that, we evaluated the configuration of 48 popular browsers and one million of the most popular websites. Moreover, we present a so-called prolongation attack, which allows extending the tracking period beyond the lifetime of the session resumption mechanism. To show that under the observed browser configurations tracking via TLS session resumptions is feasible, we also looked into DNS data to understand the longest consecutive tracking period for a user by a particular website. Our results indicate that with the standard setting of the session resumption lifetime in many current browsers, the average user can be tracked for up to eight days. With a session resumption lifetime of seven days, as recommended upper limit in the draft for TLS version 1.3, 65% of all users in our dataset can be tracked permanently.

The target of this venture is to give the protected correspondence among the associated frameworks in the system. It gives the vital validation to the record moving in the system transmission. It comprises of 3 modules in particular encryption and unscrambling module, secret key verification to the information that needs to transmit through system. In this system, File Transfer Protocol can be used to execute Server-client innovation and the document can be scrambled and unscrambled by sending the end client through attachment programming of the end client.

Globalization of IC manufacturing has led to increased security concerns, notably IP theft. Several logic locking techniques have been developed for protecting designs, but they typically display very large overhead, and are generally susceptible to deciphering attacks. In this paper, we propose latch-based logic locking, which manipulates both the flow of data and logic in the design. This method converts an interconnected subset of existing flip-flops to pairs of latches with programmable phase. In tandem, decoy latches and logic are added, inhibiting an attacker from determining the actual design functionality. To validate this technique, we developed and verified a locking insertion flow, analyzed PPA and ATPG overhead on benchmark circuits and industry cores, extended existing attacks to account for the technique, and taped out a demonstration chip. Importantly, we show that the design overhead with this approach is significantly less than with previous logic locking schemes, while resisting model checker-based, oracle-driven attacks. With minimal delay overhead, large numbers of decoy latches can be added, cheaply increasing attack resistance.

Effective Personalized Mobile Search Using KNN, implements an architecture to improve user's personalization effectiveness over large set of data maintaining security of the data. User preferences are gathered through clickthrough data. Clickthrough data obtained is sent to the server in encrypted form. Clickthrough data obtained is classified into content concepts and location concepts. To improve classification and minimize processing time, KNN(K Nearest Neighborhood) algorithm is used. Preferences identified(location and content) are merged to provide effective preferences to the user. System make use of four entropies to balance weight between content concepts and location concepts. System implements client server architecture. Role of client is to collect user queries and to maintain them in files for future reference. User preference privacy is ensured through privacy parameters and also through encryption techniques. Server is responsible to carry out the tasks like training, reranking of the search results obtained and the concept extraction. Experiments are carried out on Android based mobile. Results obtained through experiments show that system significantly gives improved results over previous algorithm for the large set of data maintaining security.

Data sharing is a significant functionality in cloud storage. These cloud storage provider are answerable for keeping the data obtainable and available in addition to the physical environment protected and running. Here we can securely, efficiently, and flexibly share data with others in cloud storage. A new public-key cryptosystems is planned which create constant-size cipher texts such that efficient allocation of decryption rights for any set of cipher texts are achievable. The uniqueness means that one can aggregate any set of secret keys and make them as packed in as a single key, but encircling the power of all the keys being aggregated. This packed in aggregate key can be easily sent to others or be stored in a smart card with very restricted secure storage. In KAC, users encrypt a file with single key, that means every file have each file, also there will be aggregate keys for two or more files, which formed by using the tree structure. Through this, the user can share more files with a single key at a time.

Blockchain technology is useful with the record keeping of digital transactions, IoT, supply chain management etc. However, we have observed that the traditional attacks are possible on blockchain due to lack of robust identity management. We found that Sybil attack can cause severe impact in public/permissionless blockchain, in which an attacker can subvert the blockchain by creating a large number of pseudonymous identities (i.e. Fake user accounts) and push legitimate entities in the minority. Such virtual nodes can act like genuine nodes to create disproportionately large influence on the network. This may lead to several other attacks like DoS, DDoS etc. In this paper, a Sybil attack is demonstrated on a blockchain test bed with its impact on the throughput of the system. We propose a solution directive, in which each node monitors the behavior of other nodes and checks for the nodes which are forwarding the blocks of only particular user. Such nodes are quickly identified, blacklisted and notified to other nodes, and thus the Sybil attack can be restricted. We analyze experimental results of the proposed solution.

Internet of Things (IoT) combines the internet and physical objects to transfer information among the objects. In the emerging IoT networks, providing security is the major issue. IoT device is exposed to various security issues due to its low computational efficiency. In recent years, the Intrusion Detection System valuable tool deployed to secure the information in the network. This article exposes the Intrusion Detection System (IDS) based on deep learning and machine learning to overcome the security attacks in IoT networks. Long Short-Term Memory (LSTM) and K-Nearest Neighbor (KNN) are used in the attack detection model and performances of those algorithms are compared with each other based on detection time, kappa statistic, geometric mean, and sensitivity. The effectiveness of the developed IDS is evaluated by using Bot-IoT datasets.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

The Internet of Things (IoT) technology has a potential to bring the benefits of intelligently interconnecting not just computers and humans, but most of everyday things. IoT has a promise of opening significant business process improvement opportunities leading to economic growth and cost reductions. However, there are many challenges facing IoT, including significant scalability and security challenges due to the integration of potentially huge number of things into the network. Many of scalability and security issues stem from a centralized, primarily client/server, architecture of IoT systems and frameworks. Blockchain technology, as a relativelly new approach to decentralized computation and assets management and transfer, has a potential to help solve a number of scalability and security issues that IoT is facing, primarilly through the removal of centralized points of failure for such systems. As such, blockchain technology and IoT integration provides a promising direction and it has recently generated significant research interest, e.g., [4]. In this talk, we present our experiences based on our recent project in enhancing security and privacy in decentralized energy trading in smart grids using blockchain, multi-signatures and anonymous messaging streams [1], that has built upon our previous work on Bitcoin-based decentralized carbon emissions trading infrastructure model [2]. In particular, we present the blockchain systems security issues within the context of IoT security and privacy requirements [3]. This is done with the intention of producing an early integrated security model for blockchain-powered IoT systems [5]. The presentation is constrained to the discussion of the architecture-level requirements [6]. Finally, we will present the main opportunity loss if the integration ignores the full realization of the real-world asset transaction paradigm.

This Work-In-Progress Paper for the Innovative Practice Category presents a novel experiment in active learning of cybersecurity. We introduced a new workshop on hacking for an existing science-popularizing program at our university. The workshop participants, 28 teenagers, played a cybersecurity game designed for training undergraduates and professionals in penetration testing. Unlike in learning environments that are simplified for young learners, the game features a realistic virtual network infrastructure. This allows exploring security tools in an authentic scenario, which is complemented by a background story. Our research aim is to examine how young players approach using cybersecurity tools by interacting with the professional game. A preliminary analysis of the game session showed several challenges that the workshop participants faced. Nevertheless, they reported learning about security tools and exploits, and 61% of them reported wanting to learn more about cybersecurity after the workshop. Our results support the notion that young learners should be allowed more hands-on experience with security topics, both in formal education and informal extracurricular events.

Tensile residual stresses combined with an applied tensile stress can reduce the reliability of steel components. Nondestructive evaluation of residual stress is thus important to avoid unintended fatigue or cracking. Because magnetic hysteresis properties of ferromagnetic materials are sensitive to stress, nondestructive evaluation of residual stress through magnetic properties can be expected. The spatial mapping of local magnetic hysteresis properties becomes possible by using the acoustically stimulated electromagnetic (ASEM) method and the tensile stress dependence of the hysteresis properties has been investigated in steel. It is found that the coercivity Hc and the remanent magnetization signal Vr monotonically decrease with increasing the tensile stress. In this work, we verified the detection of residual stresses through the ASEM response in a welded steel plate. Tensile stresses are intentionally introduced on the opposite side of the partially welded face by controlling welding temperatures. We found that Hc and Vr clearly decrease in the welded region, suggesting that the presence of tensile residual stresses is well detected by the hysteresis parameters.