Biblio

The high penetration of third-party intellectual property (3PIP) brings a high risk of malicious inclusions and data leakage in products due to the planted hardware Trojans, and system level security constraints have recently been proposed for MPSoCs protection against hardware Trojans. However, secret communication still can be established in the context of the proposed security constraints, and thus, another type of security constraints is also introduced to fully prevent such malicious inclusions. In addition, fulfilling the security constraints incurs serious overhead of schedule length, and a two-stage performance-constrained task scheduling algorithm is then proposed to maintain most of the security constraints. In the first stage, the schedule length is iteratively reduced by assigning sets of adjacent tasks into the same core after calculating the maximum weight independent set of a graph consisting of all timing critical paths. In the second stage, tasks are assigned to proper IP vendors and scheduled to time periods with a minimization of cores required. The experimental results show that our work reduces the schedule length of a task graph, while only a small number of security constraints are violated.

Digital signal processing (DSP) and multimedia based reusable Intellectual property (IP) cores form key components of system-on-chips used in consumer electronic devices. They represent years of valuable investment and hence need protection against prevalent threats such as IP cloning and fraudulent claim of ownership. This paper presents a novel crypto digital signature approach which incorporates multiple security modules such as encryption, hashing and encoding for protection of digital signature processing cores. The proposed approach achieves higher robustness (and reliability), in terms of lower probability of coincidence, at lower design cost than existing watermarking approaches for IP cores. The proposed approach achieves stronger proof of authorship (on average by 39.7%) as well as requires lesser storage hardware compared to a recent similar work.

Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces use of a sieve-valve as a security primitive to obfuscate bioassay implementations; (2) it shows how sieve-valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) it presents design rules and security metrics to design and measure obfuscated biochips. We assess the cost-security trade-offs associated with this solution and demonstrate practical sieve-valve based obfuscation on real-life biochips.

Modern multicore System-on-Chips (SoCs) are regularly designed with third-party Intellectual Properties (IPs) and software tools to manage the complexity and development cost. This approach naturally introduces major security concerns, especially for those SoCs used in critical applications and cyberinfrastructure. Despite approaches like split manufacturing, security testing and hardware metering, this remains an open and challenging problem. In this work, we propose a dynamic intrusion detection approach to address the security challenge. The proposed runtime system (SoCINT) systematically gathers information about untrusted IPs and strictly enforces the access policies. SoCINT surpasses the-state-of-the-art monitoring systems by supporting hardware tracing, for more robust analysis, together with providing smart counterintelligence strategies. SoCINT is implemented in an open source processor running on a commercial FPGA platform. The evaluation results validate our claims by demonstrating resilience against attacks exploiting erroneous or malicious IPs.

The penalization of the objective function due to energy deficits is a key element for determining the operational policy of hydroelectric reservoirs. Its definition impacts not only operations, but also system expansion. Brazil historically defined these penalties with basis on a proxy of the economic deficit cost, a value in \$/MWh obtained with aid of the Input-Output Matrix. We propose an approach where these penalties are obtained in order to minimize the operation cost and cost of rationing of the system, considering a criterion of security of supply. A case study with data from the Brazilian System illustrates its application.

The confidentiality of tenant's data is confronted with high risk when facing hardware attacks and privileged malicious software. Hardware-based memory encryption is one of the promising means to provide strong guarantees of data security. Recently AMD has proposed its new memory encryption hardware called SME and SEV, which can selectively encrypt memory regions in a fine-grained manner, e.g., by setting the C-bits in the page table entries. More importantly, SEV further supports encrypted virtual machines. This, intuitively, has provided a new opportunity to protect data confidentiality in guest VMs against an untrusted hypervisor in the cloud environment. In this paper, we first provide a security analysis on the (in)security of SEV and uncover a set of security issues of using SEV as a means to defend against an untrusted hypervisor. Based on the study, we then propose a software-based extension to the SEV feature, namely Fidelius, to address those issues while retaining performance efficiency. Fidelius separates the management of critical resources from service provisioning and revokes the permissions of accessing specific resources from the un-trusted hypervisor. By adopting a sibling-based protection mechanism with non-bypassable memory isolation, Fidelius embraces both security and efficiency, as it introduces no new layer of abstraction. Meanwhile, Fidelius reuses the SEV API to provide a full VM life-cycle protection, including two sets of para-virtualized I/O interfaces to encode the I/O data, which is not considered in the SEV hardware design. A detailed and quantitative security analysis shows its effectiveness in protecting tenant's data from a variety of attack surfaces, and the performance evaluation confirms the performance efficiency of Fidelius.

Anti-virus software (AVS) tools are used to detect Malware in a system. However, software-based AVS are vulnerable to attacks. A malicious entity can exploit these vulnerabilities to subvert the AVS. Recently, hardware components such as Hardware Performance Counters (HPC) have been used for Malware detection. In this paper, we propose PREEMPT, a zero overhead, high-accuracy and low-latency technique to detect Malware by re-purposing the embedded trace buffer (ETB), a debug hardware component available in most modern processors. The ETB is used for post-silicon validation and debug and allows us to control and monitor the internal activities of a chip, beyond what is provided by the Input/Output pins. PREEMPT combines these hardware-level observations with machine learning-based classifiers to preempt Malware before it can cause damage. There are many benefits of re-using the ETB for Malware detection. It is difficult to hack into hardware compared to software, and hence, PREEMPT is more robust against attacks than AVS. PREEMPT does not incur performance penalties. Finally, PREEMPT has a high True Positive value of 94% and maintains a low False Positive value of 2%.

Two-phase I/O is a well-known strategy for implementing collective MPI-IO functions. It redistributes I/O requests among the calling processes into a form that minimizes the file access costs. As modern parallel computers continue to grow into the exascale era, the communication cost of such request redistribution can quickly overwhelm collective I/O performance. This effect has been observed from parallel jobs that run on multiple compute nodes with a high count of MPI processes on each node. To reduce the communication cost, we present a new design for collective I/O by adding an extra communication layer that performs request aggregation among processes within the same compute nodes. This approach can significantly reduce inter-node communication contention when redistributing the I/O requests. We evaluate the performance and compare it with the original two-phase I/O on Cray XC40 parallel computers (Theta and Cori) with Intel KNL and Haswell processors. Using I/O patterns from two large-scale production applications and an I/O benchmark, we show our proposed method effectively reduces the communication cost and hence maintains the scalability for a large number of processes.

{Unikernel is smaller in size than existing operating systems and can be started and shut down much more quickly and safely, resulting in greater flexibility and security. Since unikernel does not include large modules like the file system in its library to reduce its size, it is common to choose offloading to handle file IO. However, the processing of IO offload of unikernel transfers the file IO command to the proxy of the file server and copies the file IO result of the proxy. This can result in a trade-off of rapid processing, an advantage of unikernel. In this paper, we propose a method to offload file IO and to perform file IO with direct copy from file server to unikernel}.

Communication is an essential part of everyday life, both as a social interaction and collaboration to achieve goals. Wireless technology has effectively release the users to roam more freely to achieving collaboration and communication. The principle attraction of mobile ad hoc networks (MANET) are their set-up less and decentralized action. However, mobile ad hoc networks are seen as relatively easy targets for attackers. Security in mobile ad hoc network is provided by encrypting the data when exchanging messages and key management. Cryptography is therefore vital to ensure privacy of message and robustness against disruption. The proposed scheme public string based threshold cryptography (PSTC) describes the new scheme based on threshold cryptography that provides reasonably secure and robust cryptography scheme for mobile ad hoc networks. The scheme is implemented and simulated in ns-2. The scheme is based on trust value and analyze against Denial of Service attack as node found the attacker, the node reject all packet from that attacker. In proposed scheme whole network is compromised only when all nodes of network is compromised because threshold nodes only sharing public string not the master private key. The scheme provides confidentiality and integrity. The default threshold value selected is 2 according to time and space analysis.

The mobile ad-hoc networks comprised of nodes that are communicated through dynamic request and also by static table driven technique. The dynamic route discovery in AODV routing creates an unsecure transmission as well as reception. The reason for insecurity is the route request is given to all the nodes in the network communication. The possibility of the intruder nodes are more in the case of dynamic route request. Wormhole attacks in MANETs are creating challenges in the field of network analysis. In this paper the wormhole scenario is realized using high power transmission. This is implemented using energy model of ns2 simulator. The Apptool simulator identifies the energy level of each node and track the node of high transmission power. The performance curves for throughput, node energy for different encrypted values, packet drop ratio, and end to end delay are plotted.

As malware family classification methods, image-based classification methods have attracted much attention. Especially, due to the fast classification speed and the high classification accuracy, Convolutional Neural Network (CNN)-based malware family classification methods have been studied. However, previous studies on CNN-based classification methods focused only on improving the classification accuracy of malware families. That is, previous studies did not consider the cases that the accuracy of CNN-based malware classification methods can be decreased under the existence of adversarial attacks. In this paper, we analyze the robustness of various CNN-based malware family classification models under adversarial attacks. While adding imperceptible non-random perturbations to the input image, we measured how the accuracy of the CNN-based malware family classification model can be affected. Also, we showed the influence of three significant visualization parameters(i.e., the size of input image, dimension of input image, and conversion color of a special character)on the accuracy variation under adversarial attacks. From the evaluation results using the Microsoft malware dataset, we showed that even the accuracy over 98% of the CNN-based malware family classification method can be decreased to less than 7%.

Android malware family classification is an advanced task in Android malware analysis, detection and forensics. Existing methods and models have achieved a certain success for Android malware detection, but the accuracy and the efficiency are still not up to the expectation, especially in the context of multiple class classification with imbalanced training data. To address those challenges, we propose an Android malware family classification model by analyzing the code's specific semantic information based on sensitive opcode sequence. In this work, we construct a sensitive semantic feature-sensitive opcode sequence using opcodes, sensitive APIs, STRs and actions, and propose to analyze the code's specific semantic information, generate a semantic related vector for Android malware family classification based on this feature. Besides, aiming at the families with minority, we adopt an oversampling technique based on the sensitive opcode sequence. Finally, we evaluate our method on Drebin dataset, and select the top 40 malware families for experiments. The experimental results show that the Total Accuracy and Average AUC (Area Under Curve, AUC) reach 99.50% and 98.86% with 45. 17s per Android malware, and even if the number of malware families increases, these results remain good.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

Not only the number of deployed IoT devices increases but also that of IoT malware increases. We eager to understand the threat made by IoT malware but we lack tools to observe, analyze and detect them. We design and implement an automatic, virtual machine-based profiling system to collect valuable IoT malware behavior, such as API call invocation, system call execution, etc. In addition to conventional profiling methods (e.g., strace and packet capture), the proposed profiling system adapts virtual machine introspection based API hooking technique to intercept API call invocation by malware, so that our introspection would not be detected by IoT malware. We then propose a method to convert the multiple sequential data (API calls) to a family behavior graph for further analysis.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.

Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.

Operating systems such as Linux break the power of the root user into separate privileges (which Linux calls capabilities) and give processes the ability to enable privileges only when needed and to discard them permanently when the program no longer needs them. However, there is no method of measuring how well the use of such facilities reduces the risk of privilege escalation attacks if the program has a vulnerability. This paper presents PrivAnalyzer, an automated tool that measures how effectively programs use Linux privileges. PrivAnalyzer consists of three components: 1) AutoPriv, an existing LLVM-based C/C++ compiler which uses static analysis to transform a program that uses Linux privileges into a program that safely removes them when no longer needed, 2) ChronoPriv, a new LLVM C/C++ compiler pass that performs dynamic analysis to determine for how long a program retains various privileges, and 3) ROSA, a new bounded model checker that can model the damage a program can do at each program point if an attacker can exploit the program and abuse its privileges. We use PrivAnalyzer to determine how long five privileged open source programs retain the ability to cause serious damage to a system and find that merely transforming a program to drop privileges does not significantly improve security. However, we find that simple refactoring can considerably increase the efficacy of Linux privileges. In two programs that we refactored, we reduced the percentage of execution in which a device file can be read and written from 97% and 88% to 4% and 1%, respectively.

The revelations of Snowden show that hardware and software of devices may corrupt users' machine to compromise the security in various ways. To address this concern, Mironov and Stephen-Davidowitz introduce the Cryptographic Reverse Firewall (CRF) concept that is able to resist the ex-filtration of secret information for some compromised machine (Eurocrypt 2015). There are some applications of CRF deployed in many cryptosystems, but less studied and deployed in Attribute-Based Encryption (ABE) field, which attracts a wide range of attention and is employed in real-world scenarios (i.e., data sharing in cloud). In this work, we focus how to give a CRF security protection for a multi-authority ABE scheme and hence propose a multi-authority key-policy ABE scheme with CRF (acronym, MA-KP-ABE-CRF), which supports attribute distribution and non-monotonic access structure. To achieve this, beginning with revisiting a MA-KP-ABE with non-trivial combining non-monotonic formula, we then give the randomness of ciphertexts and secret keys with reverse firewall and give formal security analysis. Finally, we give a simulation on our MA-KP-ABE-CRF system based on Charm library whose the experimental results demonstrate practical efficiency.

The readout of large pixellated detectors with good spectroscopic quality represents a challenge for both front-end and back-end electronics. The TRISTAN project for the search of the Sterile neutrino in the keV-scale, envisions the operation of 21 detection modules equipped with a monolithic array of 166 SDDs each, for β-decay spectroscopy in the KATRIN experiment's spectrometer. Since the trace of the sterile neutrino existence would manifest as a kink of \textbackslashtextless; 1ppm in the continuous spectrum, high accuracy in the acquisition is required. Within this framework, we present the design of a multichannel scalable analog processing and DAQ system named Kerberos, aimed to provide a simple and low-cost multichannel readout option in the early phase of the TRISTAN detector development. It is based on three 16-channel integrated programmable analog pulse processors (SFERA ASICs), high linearity ADCs, and an FPGA. The platform is able to acquire data from up to 48 pixels in parallel, providing also different readout and multiplexing strategies. The use of an analog ASIC-based solution instead of a Digital Pulse Processor, represents a viable and scalable processing solution at the price of slightly limited versatility and count rate.

Research into the established area of ITS is evolving into the Internet of Vehicles (IoV), itself a fast-moving research area, fuelled in part by rapid changes in computing and communication technologies. Using pseudonym certificate is a popular way to address privacy issues in IoV. Therefore, the certificate management scheme is considered as a feasible technique to manage system and maintain the lifecycle of certificate. In this paper, we propose an efficient pseudonym certificate management scheme in IoV. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks.

Internet of Things (IoT) is becoming increasingly important to intelligent transportation system stakeholders, including cloud-based vehicular cloud (VC) and internet of vehicles (IoV) paradigms. This new trend involves communication and data exchange between several objects within different layers of control. Security in such a deployment is pivotal to realize the general IoT-based smart city. However, the evaluation of the degree of security regarding these paradigms remains a challenge. This study aims to discover and identify common security criteria (CSC) from a context-based analysis pattern and later to discuss, compare, and aggregate a conceptual model of CSC impartially. A privacy granularity classification that maintains data confidentiality is proposed alongside the security selection criteria.

Industrial Control systems traditionally achieved security by using proprietary protocols to communicate in an isolated environment from the outside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, a device acting as a connection between the operational technology network and information technology network is proposed. The device is an intrusion detection system related to legacy systems that is able to collect and reporting data to and from industrial IoT devices. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. At a later stage, it collect and aggregate data from and to IoT domain. A simple set up is considered to prove the effectiveness of the approach.

With the proposal of the national industrial 4.0 strategy, the integration of industrial control network and Internet technology is getting higher and higher. At the same time, the closeness of industrial control networks has been broken to a certain extent, making the problem of industrial control network security increasingly serious. S7 protocol is a private protocol of Siemens Company in Germany, which is widely used in the communication process of industrial control network. In this paper, an industrial control intrusion detection model based on S7 protocol is proposed. Traditional protocol parsing technology cannot resolve private industrial control protocols, so, this model uses deep analysis algorithm to realize the analysis of S7 data packets. At the same time, in order to overcome the complexity and portability of static white list configuration, this model dynamically builds a white list through white list self-learning algorithm. Finally, a composite intrusion detection method combining white list detection and abnormal behavior detection is used to detect anomalies. The experiment proves that the method can effectively detect the abnormal S7 protocol packet in the industrial control network.