Biblio
Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits known as hardware Trojans has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention and most approaches assume the existence of a “golden model”. Prior works suggest using reverse-engineering to identify such Trojan-free ICs for the golden model but they did not state how to do this efficiently. In this paper, we propose an innovative and robust reverseengineering approach to identify the Trojan-free ICs. We adapt a well-studied machine learning method, one-class support vector machine, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that our approach can detect hardware Trojans with high accuracy rate across different modeling and algorithm parameters.
Cloud-based communications system is now widely used in many application fields such as medicine, security, environment protection, etc. Its use is being extended to the most demanding services like multimedia delivery. However, there are a lot of constraints when cloud-based sensor networks use the standard IEEE 802.15.3 or IEEE 802.15.4 technologies. This paper proposes a channel characterization scheme combined to a cross-layer admission control in dynamic cloud-based multimedia sensor networks to share the network resources among any two nodes. The analysis shows the behavior of two nodes using different network access technologies and the channel effects for each technology. Moreover, the existence of optimal node arrival rates in order to improve the usage of dynamic admission control when network resources are used is also shown. An extensive simulation study was performed to evaluate and validate the efficiency of the proposed dynamic admission control for cloud-based multimedia sensor networks.
The strong development of the Internet of Things (IoT) is dramatically changing traditional perceptions of the current Internet towards an integrated vision of smart objects interacting with each other. While in recent years many technological challenges have already been solved through the extension and adaptation of wireless technologies, security and privacy still remain as the main barriers for the IoT deployment on a broad scale. In this emerging paradigm, typical scenarios manage particularly sensitive data, and any leakage of information could severely damage the privacy of users. This paper provides a concise description of some of the major challenges related to these areas that still need to be overcome in the coming years for a full acceptance of all IoT stakeholders involved. In addition, we propose a distributed capability-based access control mechanism which is built on public key cryptography in order to cope with some of these challenges. Specifically, our solution is based on the design of a lightweight token used for access to CoAP Resources, and an optimized implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA) inside the smart object. The results obtained from our experiments demonstrate the feasibility of the proposal and show promising in order to cover more complex scenarios in the future, as well as its application in specific IoT use cases.
Detecting hardware Trojan is a difficult task in general. The context is that of a fabless design house that sells IP blocks as GDSII hard macros, and wants to check that final products have not been infected by Trojan during the foundry stage. In this paper we analyzed hardware Trojan horses insertion and detection in Scalable Encryption Algorithm (SEA) crypto. We inserted Trojan at different levels in the ASIC design flow of SEA crypto and most importantly we focused on Gate level and layout level Trojan insertions. We choose path delays in order to detect Trojan at both levels in design phase. Because the path delays detection technique is cost effective and efficient method to detect Trojan. The comparison of path delays makes small Trojan circuits significant from a delay point of view. We used typical, fast and slow 90nm libraries in order to estimate the efficiency of path delay technique in different operating conditions. The experiment's results show that the detection rate on payload Trojan is 100%.
Efficient and secure search on encrypted data is an important problem in computer science. Users having large amount of data or information in multiple documents face problems with their storage and security. Cloud services have also become popular due to reduction in cost of storage and flexibility of use. But there is risk of data loss, misuse and theft. Reliability and security of data stored in the cloud is a matter of concern, specifically for critical applications and ones for which security and privacy of the data is important. Cryptographic techniques provide solutions for preserving the confidentiality of data but make the data unusable for many applications. In this paper we report a novel approach to securely store the data on a remote location and perform search in constant time without the need for decryption of documents. We use bloom filters to perform simple as well advanced search operations like case sensitive search, sentence search and approximate search.
This brief proposes a framework to analyze multiple faults based on multiple fault simulation in a particle swarm optimization environment. Experimentation shows that up to ten faults can be diagnosed in a reasonable time. However, the scheme does not put any restriction on the number of simultaneous faults.
Security is becoming a major concern in computing. New techniques are evolving every day; one of these techniques is Hash Visualization. Hash Visualization uses complex random generated images for security, these images can be used to hide data (watermarking). This proposed new technique improves hash visualization by using genetic algorithms. Genetic algorithms are a search optimization technique that is based on the evolution of living creatures. The proposed technique uses genetic algorithms to improve hash visualization. The used genetic algorithm was away faster than traditional previous ones, and it improved hash visualization by evolving the tree that was used to generate the images, in order to obtain a better and larger tree that will generate images with higher security. The security was satisfied by calculating the fitness value for each chromosome based on a specifically designed algorithm.
Mobile ad-hoc networks are a new field in networking because it works as an autonomous network. Application of mobile ad-hoc networks are increasing day by day in recent year now a days. So it important is increasing to provide suitable routing protocol and security from attacker. Mobile ad-hoc network now a days faces many problems such as small bandwidth, energy, security, limited computational and high mobility. The main problem in mobile ad-hoc networks is that wireless networks, Infrastructure wireless networks have larger bandwidth, larger memory, power backup and different routing protocol easily applies. But in case of mobile ad-hoc networks some of these application failed due to mobility and small power backup so it is required such type of routing protocol which is take small energy during the transfer of packet. So we see that still there are many challenging works in mobile ad-hoc networks remained and to research in this area related to routing protocol, security issues, solving energy problem and many more which is feasible to it. Our research most probably will be dedicated to Authentication in mobile ad-hoc network.
Many surveillance cameras are using everywhere, the videos or images captured by these cameras are still dumped but they are not processed. Many methods are proposed for tracking and detecting the objects in the videos but we need the meaningful content called semantic content from these videos. Detecting Human activity recognition is quite complex. The proposed method called Semantic Content Extraction (SCE) from videos is used to identify the objects and the events present in the video. This model provides useful methodology for intruder detecting systems which provides the behavior and the activities performed by the intruder. Construction of ontology enhances the spatial and temporal relations between the objects or features extracted. Thus proposed system provides a best way for detecting the intruders, thieves and malpractices happening around us.
With the rise in the underground Internet economy, automated malicious programs popularly known as malwares have become a major threat to computers and information systems connected to the internet. Properties such as self healing, self hiding and ability to deceive the security devices make these software hard to detect and mitigate. Therefore, the detection and the mitigation of such malicious software is a major challenge for researchers and security personals. The conventional systems for the detection and mitigation of such threats are mostly signature based systems. Major drawback of such systems are their inability to detect malware samples for which there is no signature available in their signature database. Such malwares are known as zero day malware. Moreover, more and more malware writers uses obfuscation technology such as polymorphic and metamorphic, packing, encryption, to avoid being detected by antivirus. Therefore, the traditional signature based detection system is neither effective nor efficient for the detection of zero-day malware. Hence to improve the effectiveness and efficiency of malware detection system we are using classification method based on structural information and behavioral specifications. In this paper we have used both static and dynamic analysis approaches. In static analysis we are extracting the features of an executable file followed by classification. In dynamic analysis we are taking the traces of executable files using NtTrace within controlled atmosphere. Experimental results obtained from our algorithm indicate that our proposed algorithm is effective in extracting malicious behavior of executables. Further it can also be used to detect malware variants.
Cyber systems play a critical role in improving the efficiency and reliability of power system operation and ensuring the system remains within safe operating margins. An adversary can inflict severe damage to the underlying physical system by compromising the control and monitoring applications facilitated by the cyber layer. Protection of critical assets from electronic threats has traditionally been done through conventional cyber security measures that involve host-based and network-based security technologies. However, it has been recognized that highly skilled attacks can bypass these security mechanisms to disrupt the smooth operation of control systems. There is a growing need for cyber-attack-resilient control techniques that look beyond traditional cyber defense mechanisms to detect highly skilled attacks. In this paper, we make the following contributions. We first demonstrate the impact of data integrity attacks on Automatic Generation Control (AGC) on power system frequency and electricity market operation. We propose a general framework to the application of attack resilient control to power systems as a composition of smart attack detection and mitigation. Finally, we develop a model-based anomaly detection and attack mitigation algorithm for AGC. We evaluate the detection capability of the proposed anomaly detection algorithm through simulation studies. Our results show that the algorithm is capable of detecting scaling and ramp attacks with low false positive and negative rates. The proposed model-based mitigation algorithm is also efficient in maintaining system frequency within acceptable limits during the attack period.
A distributed cyber control system comprises various types of assets, including sensors, intrusion detection systems, scanners, controllers, and actuators. The modeling and analysis of these components usually require multi-disciplinary approaches. This paper presents a modeling and dynamic analysis of a distributed cyber control system for situational awareness by taking advantage of control theory and time Petri net. Linear time-invariant systems are used to model the target system, attacks, assets influences, and an anomaly-based intrusion detection system. Time Petri nets are used to model the impact and timing relationships of attacks, vulnerability, and recovery at every node. To characterize those distributed control systems that are perfectly attackable, algebraic and topological attackability conditions are derived. Numerical evaluation is performed to determine the impact of attacks on distributed control system.
A distributed cyber control system comprises various types of assets, including sensors, intrusion detection systems, scanners, controllers, and actuators. The modeling and analysis of these components usually require multi-disciplinary approaches. This paper presents a modeling and dynamic analysis of a distributed cyber control system for situational awareness by taking advantage of control theory and time Petri net. Linear time-invariant systems are used to model the target system, attacks, assets influences, and an anomaly-based intrusion detection system. Time Petri nets are used to model the impact and timing relationships of attacks, vulnerability, and recovery at every node. To characterize those distributed control systems that are perfectly attackable, algebraic and topological attackability conditions are derived. Numerical evaluation is performed to determine the impact of attacks on distributed control system.
Wireless mesh networks (WMNs) are attracting more and more real time applications. This kind of applications is constrained in terms of Quality of Service (QoS). Existing works in this area are mostly designed for mobile ad hoc networks, which, unlike WMNs, are mainly sensitive to energy and mobility. However, WMNs have their specific characteristics (e.g. static routers and heavy traffic load), which require dedicated QoS protocols. This paper proposes a novel traffic regulation scheme for multimedia support in WMNs. The proposed scheme aims to regulate the traffic sending rate according to the network state, based on the buffer evolution at mesh routers and on the priority of each traffic type. By monitoring the buffer evolution at mesh routers, our scheme is able to predict possible congestion, or QoS violation, early enough before their occurrence; each flow is then regulated according to its priority and to its QoS requirements. The idea behind the proposed scheme is to maintain lightly loaded buffers in order to minimize the queuing delays, as well as, to avoid congestion. Moreover, the regulation process is made smoothly in order to ensure the continuity of real time and interactive services. We use the interval type-2 fuzzy logic system (IT2 FLS), known by its adequacy to uncertain environments, to make suitable regulation decisions. The performance of our scheme is proved through extensive simulations in different network and traffic load scales.
Watermarking is a recently developed technique which is currently dominating the world of security and digital processing in order to ensure the protection of digitized trade. The purpose of this work is twofold. It is firstly to establish a state of the art that goes through the existing watermarking methods and their performances. And secondly to design, implement and evaluate a new watermarking solution that aims to optimize the compromise robustness-invisibility-capacity. The proposed approach consists on applying a frequency watermarking based on singular value decomposition (SVD) and exploiting the mosaic made from all video frames as well as inserting a double signature in order to increase watermarking algorithm capacity.
Vehicular ad-hoc networks (VANETs) provides infrastructure less, rapidly deployable, self-configurable network connectivity. The network is the collection vehicles interlinked by wireless links and willing to store and forward data for their peers. As vehicles move freely and organize themselves arbitrarily, message routing is done dynamically based on network connectivity. Compared with other ad-hoc networks, VANETs are particularly challenging due to the part of the vehicles' high rate of mobility and the numerous signal-weakening barrier, such as buildings, in their environments. Due to their enormous potential, VANET have gained an increasing attention in both industry and academia. Research activities range from lower layer protocol design to applications and implementation issues. A secure VANET system, while exchanging information should protect the system against unauthorized message injection, message alteration, eavesdropping. The security of VANET is one of the most critical issues because their information transmission is propagated in open access (wireless) environments. A few years back VANET has received increased attention as the potential technology to enhance active and preventive safety on the road, as well as travel comfort Safekeeping and privacy are mandatory in vehicular communications for a grateful acceptance and use of such technology. This paper is an attempt to highlight the problems occurred in Vehicular Ad hoc Networks and security issues.
The use of side-channel measurements and fingerprinting, in conjunction with statistical analysis, has proven to be the most effective method for accurately detecting hardware Trojans in fabricated integrated circuits. However, these post-fabrication trust evaluation methods overlook the capabilities of advanced design skills that attackers can use in designing sophisticated Trojans. To this end, we have designed a Trojan using power-gating techniques and demonstrate that it can be masked from advanced side-channel fingerprinting detection while dormant. We then propose a real-time trust evaluation framework that continuously monitors the on-board global power consumption to monitor chip trustworthiness. The measurements obtained corroborate our frameworks effectiveness for detecting Trojans. Finally, the results presented are experimentally verified by performing measurements on fabricated Trojan-free and Trojan-infected variants of a reconfigurable linear feedback shift register (LFSR) array.
The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. An information-theoretic framework is presented, to quantitatively measure trust and build a novel trust model (FAPtrust) with multiple trust decision factors. These decision factors are incorporated to reflect trust relationship's complexity and uncertainty in various angles. The weight of these factors is set up using fuzzy analytic hierarchy process theory based on entropy weight method, which makes the model has a better rationality. Moreover, the fuzzy logic rules prediction mechanism is adopted to update a node's trust for future decision-making. As an application of this model, a novel reactive trust-based multicast routing protocol is proposed. This new trusted protocol provides a flexible and feasible approach in routing decision-making, taking into account both the trust constraint and the malicious node detection in multi-agent systems. Comprehensive experiments have been conducted to evaluate the efficiency of trust model and multicast trust enhancement in the improvement of network interaction quality, trust dynamic adaptability, malicious node identification, attack resistance and enhancements of system's security.
Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of >45% and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.
Privacy preservation is very essential in various real life applications such as medical science and financial analysis. This paper focuses on implementation of an asymmetric secure multi-party computation protocol using anonymization and public-key encryption where all parties have access to trusted third party (TTP) who (1) doesn't add any contribution to computation (2) doesn't know who is the owner of the input received (3) has large number of resources (4) decryption key is known to trusted third party (TTP) to get the actual input for computation of final result. In this environment, concern is to design a protocol which deploys TTP for computation. It is proposed that the protocol is very proficient (in terms of secure computation and individual privacy) for the parties than the other available protocols. The solution incorporates protocol using asymmetric encryption scheme where any party can encrypt a message with the public key but decryption can be done by only the possessor of the decryption key (private key). As the protocol works on asymmetric encryption and packetization it ensures following: (1) Confidentiality (Anonymity) (2) Security (3) Privacy (Data).
We propose a method for analysis of surveillance video by using low rank and sparse decomposition (LRSD) with low latency combined with compressive sensing to segment the background and extract moving objects in a surveillance video. Video is acquired by compressive measurements, and the measurements are used to analyze the video by a low rank and sparse decomposition of a matrix. The low rank component represents the background, and the sparse component, which is obtained in a tight wavelet frame domain, is used to identify moving objects in the surveillance video. An important feature of the proposed low latency method is that the decomposition can be performed with a small number of video frames, which reduces latency in the reconstruction and makes it possible for real time processing of surveillance video. The low latency method is both justified theoretically and validated experimentally.
Wireless Sensor Networks (WSNs) are used in many applications in military, environmental, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is important in WSNs. However, WSNs suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this paper, we try to explore security issue in WSN. First, the constraints, security requirements and attacks with their corresponding countermeasures in WSNs are explained. Individual sensor nodes are subject to compromised security. An adversary can inject false reports into the networks via compromised nodes. Furthermore, an adversary can create a Gray hole by compromised nodes. If these two kinds of attacks occur simultaneously in a network, some of the existing methods fail to defend against those attacks. The Ad-hoc On Demand Distance (AODV) Vector scheme for detecting Gray-Hole attack and Statistical En-Route Filtering is used for detecting false report. For increasing security level, the Elliptic Curve Cryptography (ECC) algorithm is used. Simulations results obtain so far reduces energy consumption and also provide greater network security to some extent.
Mobile Ad-hoc Network is highly susceptible towards the security attacks due to its dynamic topology, resource constraint, energy constraint operations, limited physical security and lack of infrastructure. Misleading routing attack (MIRA) in MANET intend to delay packet to its fullest in order to generate time outs at the source as packets will not reach in time. Its main objective is to generate delay and increase network overhead. It is a variation to the sinkhole attack. In this paper, we have proposed a detection scheme to detect the malicious nodes at route discovery as well as at packet transmissions. The simulation results of MIRA attack indicate that though delay is increased by 91.30% but throughput is not affected which indicates that misleading routing attack is difficult to detect. The proposed detection scheme when applied to misleading routing attack suggests a significant decrease in delay.
Formal techniques provide exhaustive design verification, but computational margins have an important negative impact on its efficiency. Sequential equivalence checking is an effective approach, but traditionally it has been only applied between circuit descriptions with one-to-one correspondence for states. Applying it between RTL descriptions and high-level reference models requires removing signals, variables and states exclusive of the RTL description so as to comply with the state correspondence restriction. In this paper, we extend a previous formal methodology for RTL verification with high-level models, to check also the signals and protocol implemented in the RTL design. This protocol implementation is compared formally to a description captured from the specification. Thus, we can prove thoroughly the sequential behavior of a design under verification.
A novel short-time Fourier transform (STFT) domain adaptive filtering scheme is proposed that can be easily combined with nonlinear post filters such as residual echo or noise reduction in acoustic echo cancellation. Unlike normal STFT subband adaptive filters, which suffers from aliasing artifacts due to its poor prototype filter, our scheme achieves good accuracy by exploiting the relationship between the linear convolution and the poor prototype filter, i.e., the STFT window function. The effectiveness of our scheme was confirmed through the results of simulations conducted to compare it with conventional methods.