Biblio

As demonstrated recently, Wireless Physical Layer Security (WPLS) has the potential to offer substantial advantages for key management for small resource-constrained and, therefore, low-cost IoT-devices, e.g., the widely applied 8-bit MCU 8051. In this paper, we present a WPLS testbed implementation for independent performance and security evaluations. The testbed is based on off-the-shelf hardware and utilizes the IEEE 802.15.4 communication standard for key extraction and secret key rate estimation in real-time. The testbed can include generically multiple transceivers to simulate legitimate parties or eavesdropper. We believe with the testbed we provide a first step to make experimental-based WPLS research results comparable. As an example, we present evaluation results of several test cases we performed, while for further information we refer to https://pls.rub.de.

In cognitive radio network, the primary user (PU) network and the secondary user (SU) network interfered with each other because of sharing the spectral resource. Also interference among multi-downlinks in SU network decreased the sum rate in SU network and the eavesdropper in PU network decreased the secrecy rate in PU network. Focusing on above problem, this paper raised two channel selection and beamforming methods based on singular value decomposition (SVD) and uplink-downlink duality respectively, and then analyzed the performance of them in physical layer security.

In this paper, we address the physical layer security problem for Wireless Sensor Networks in the presence of passive eavesdroppers, i.e., the eavesdroppers' channels are unknown to the transmitter. We use a multi-antenna relay to guarantee physical layer security. Different from the existing work, we consider that the relay works in full duplex mode and transmits artificial noise (AN) in both stages of the decode-and-forward (DF) cooperative strategy. We proposed two optimal power allocation strategies for power constrained and power unconstrained systems respectively. For power constrained system, our aim is to minimize the secrecy rate outage probability. And for power unconstrained systems, we obtain the optimal power allocation to minimize the total power under the quality of service and secrecy constraints. We also consider the secrecy outage probability for different positions of eavesdropper. Simulation results are presented to show the performance of the proposed strategies.

The cyclostationary characteristics of signals has some important applications in such as blind channel equalization, blind adaptive beamforming, and system identification. However, the cyclostationary characteristics also can be a weak link in physical layer security. With high-order cyclostationary theory, some system information can be obtained easily. In this paper, we proposed a new algorithm based on constellation phase rotation and amplitude randomization, during which the cyclostationary feature of signals can be suppressed.

In this paper, we analyze the security-reliability tradeoff (SRT) performance of the multi-relay cooperative networks over Nakagami-m fading channels. By considering the reliability of the first phase from the source to relay, a cooperative jamming (CJ) assisted secure transmission scheme is investigated to improve the security performance of the considered system. Specifically, we derive the approximate closed-form expression of the outage probability (OP) and exact closed-form expression of the intercepted probability (IP) for the CJ scheme to evaluate the SRT performance of the system. Finally, the simulation results verify the validity of our theoretical derivations and the advantage of the CJ scheme compared to the traditional scheme with no cooperative jammer.

In this work, we analyze the feasibility of a physically secure implementation of the quantum-resistant supersingular isogeny Diffie-Hellman (SIDH) protocol. Notably, we analyze the defense against timing attacks, simple power analysis, differential power analysis, and fault attacks. Luckily, the SIDH protocol closely resembles its predecessor, the elliptic curve Diffie-Hellman (ECDH) key exchange. As such, much of the extensive literature in side-channel analysis can also apply to SIDH. In particular, we focus on a hardware implementation that features a true random number generator, ALU, and controller. SIDH is composed of two rounds containing a double-point multiplication to generate a secret kernel point and an isogeny over that kernel to arrive at a new elliptic curve isomorphism. To protect against simple power analysis and timing attacks, we recommend a constant-time implementation with Fermat's little theorem inversion. Differential power analysis targets the power output of the SIDH core over many runs. As such, we recommend scaling the base points by secret scalars so that each iteration has a unique power signature. Further, based on recent oracle attacks on SIDH, we cannot recommend the use of static keys from both parties. The goal of this paper is to analyze the tradeoffs in elliptic curve theory to produce a cryptographically and physically secure implementation of SIDH.

Attacks on web services are major concerns and can expose organizations valuable information resources. Despite there are increasing awareness in secure programming, we still find vulnerabilities in web services. To protect deployed web services, it is important to have defense techniques. Signaturebased Intrusion Detection Systems (IDS) have gained popularity to protect applications against attacks. However, signature IDSs have limited number of attack signatures. In this paper, we propose a Genetic Algorithm (GA)-based attack signature generation approach and show its application for web services. GA algorithm has the capability of generating new member from a set of initial population. We leverage this by generating new attack signatures at SOAP message level to overcome the challenge of limited number of attack signatures. The key contributions include defining chromosomes and fitness functions. The initial results show that the GA-based IDS can generate new signatures and complement the limitation of existing web security testing tools. The approach can generate new attack signatures for injection, privilege escalation, denial of service and information leakage.

Anomaly detection for cyber-security defence hasgarnered much attention in recent years providing an orthogonalapproach to traditional signature-based detection systems.Anomaly detection relies on building probability models ofnormal computer network behaviour and detecting deviationsfrom the model. Most data sets used for cyber-security havea mix of user-driven events and automated network events,which most often appears as polling behaviour. Separating theseautomated events from those caused by human activity is essentialto building good statistical models for anomaly detection. This articlepresents a changepoint detection framework for identifyingautomated network events appearing as periodic subsequences ofevent times. The opening event of each subsequence is interpretedas a human action which then generates an automated, periodicprocess. Difficulties arising from the presence of duplicate andmissing data are addressed. The methodology is demonstrated usingauthentication data from Los Alamos National Laboratory'senterprise computer network.

One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare a defense. The ability to anticipate attacks and prepare defenses before they occur would be a significant scientific and technological development with practical applications in cybersecurity. In this paper, we present a method to augment machine learning-based malware detection systems by predicting signatures of future malware variants and injecting these variants into the defensive system as a vaccine. Our method uses deep learning to learn patterns of malware evolution from family histories. These evolution patterns are then used to predict future family developments. Our experiments show that a detection system augmented with these future malware signatures is able to detect future malware variants that could not be detected by the detection system alone. In particular, it detected 11 new malware variants without increasing false positives, while providing up to 5 months of lead time between prediction and attack.

The large number of malicious files that are produced daily outpaces the current capacity of malware analysis and detection. For example, Intel Security Labs reported that during the second quarter of 2016, their system found more than 40M of new malware [1]. The damage of malware attacks is also increasingly devastating, as witnessed by the recent Cryptowall malware that has reportedly generated more than \$325M in ransom payments to its perpetrators [2]. In terms of defense, it has been widely accepted that the traditional approach based on byte-string signatures is increasingly ineffective, especially for new malware samples and sophisticated variants of existing ones. New techniques are therefore needed for effective defense against malware. Motivated by this problem, the paper investigates a new defense technique against malware. The technique presented in this paper is utilized for automatic identification of malware packers that are used to obfuscate malware programs. Signatures of malware packers and obfuscators are extracted from the CFGs of malware samples. Unlike conventional byte signatures that can be evaded by simply modifying one or multiple bytes in malware samples, these signatures are more difficult to evade. For example, CFG-based signatures are shown to be resilient against instruction modifications and shuffling, as a single signature is sufficient for detecting mildly different versions of the same malware. Last but not least, the process for extracting CFG-based signatures is also made automatic.

Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.

Botnet malware, which infects Internet-connected devices and seizes control for a remote botmaster, is a long-standing threat to Internet-connected users and systems. Botnets are used to conduct DDoS attacks, distributed computing (e.g., mining bitcoins), spread electronic spam and malware, conduct cyberwarfare, conduct click-fraud scams, and steal personal user information. Current approaches to the detection and classification of botnet malware include syntactic, or signature-based, and semantic, or context-based, detection techniques. Both methods have shortcomings and botnets remain a persistent threat. In this paper, we propose a method of botnet detection using Nonparametric Bayesian Methods.

Monitoring network behaviors of mobile applications, controlling their resource access and detecting potentially harmful apps are becoming increasingly important for the security protection within today's organizational, ISP and carriers. For this purpose, apps need to be identified from their communication, based upon their individual traffic signatures (called imprints in our research). Creating imprints for a large number of apps is nontrivial, due to the challenges in comprehensively analyzing their network activities at a large scale, for millions of apps on today's rapidly-growing app marketplaces. Prior research relies on automatic exploration of an app's user interfaces (UIs) to trigger its network activities, which is less likely to scale given the cost of the operation (at least 5 minutes per app) and its effectiveness (limited coverage of an app's behaviors). In this paper, we present Tiger (Traffic Imprint Generator), a novel technique that makes comprehensive app imprint generation possible in a massive scale. At the center of Tiger is a unique instantiated slicing technique, which aggressively prunes the program slice extracted from the app's network-related code by evaluating each variable's impact on possible network invariants, and removing those unlikely to contribute through assigning them concrete values. In this way, Tiger avoids exploring a large number of program paths unrelated to the app's identifiable traffic, thereby reducing the cost of the code analysis by more than one order of magnitude, in comparison with the conventional slicing and execution approach. Our experiments show that Tiger is capable of recovering an app's full network activities within 18 seconds, achieving over 98% coverage of its identifiable packets and 0.742% false detection rate on app identification. Further running the technique on over 200,000 real-world Android apps (including 78.23% potentially harmful apps) leads to the discovery of surprising new types of traffic invariants, including fake device information, hardcoded time values, session IDs and credentials, as well as complicated trigger conditions for an app's network activities, such as human involvement, Intent trigger and server-side instructions. Our findings demonstrate that many network activities cannot easily be invoked through automatic UI exploration and code-analysis based approaches present a promising alternative.

Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.

Transport Layer Security (TLS), has become the de-facto standard for secure Internet communication. When used correctly, it provides secure data transfer, but used incorrectly, it can leave users vulnerable to attacks while giving them a false sense of security. Numerous efforts have studied the adoption of TLS (and its predecessor, SSL) and its use in the desktop ecosystem, attacks, and vulnerabilities in both desktop clients and servers. However, there is a dearth of knowledge of how TLS is used in mobile platforms. In this paper we use data collected by Lumen, a mobile measurement platform, to analyze how 7,258 Android apps use TLS in the wild. We analyze and fingerprint handshake messages to characterize the TLS APIs and libraries that apps use, and also evaluate weaknesses. We see that about 84% of apps use default OS APIs for TLS. Many apps use third-party TLS libraries; in some cases they are forced to do so because of restricted Android capabilities. Our analysis shows that both approaches have limitations, and that improving TLS security in mobile is not straightforward. Apps that use their own TLS configurations may have vulnerabilities due to developer inexperience, but apps that use OS defaults are vulnerable to certain attacks if the OS is out of date, even if the apps themselves are up to date. We also study certificate verification, and see low prevalence of security measures such as certificate pinning, even among high-risk apps such as those providing financial services, though we did observe major third-party tracking and advertisement services deploying certificate pinning.

There are currently few methods that can be applied to malware classification problems which don't require domain knowledge to apply. In this work, we develop our new SHWeL feature vector representation, by extending the recently proposed Lempel-Ziv Jaccard Distance. These SHWeL vectors improve upon LZJD's accuracy, outperform byte n-grams, and allow us to build efficient algorithms for both training (a weakness of byte n-grams) and inference (a weakness of LZJD). Furthermore, our new SHWeL method also allows us to directly tackle the class imbalance problem, which is common for malware-related tasks. Compared to existing methods like SMOTE, SHWeL provides significantly improved accuracy while reducing algorithmic complexity to O(N). Because our approach is developed without the use of domain knowledge, it can be easily re-applied to any new domain where there is a need to classify byte sequences.

It has recently become apparent that both accidental and maliciously caused randomness failures pose a real and serious threat to the security of cryptographic primitives, and in response, researchers have begone the development of primitives that provide robustness against these. In this paper, however, we focus on standardized, widely available primitives. Specifically, we analyze the RSA-OAEP encryption scheme and RSA-PSS signature schemes, specified in PKCS \#1, using the related randomness security notion introduced by Paterson et al. (PKC 2014) and its extension to signature schemes. We show that, under the RSA and $\Phi$-hiding assumptions, RSA-OAEP encryption is related randomness secure for a large class of related randomness functions in the random oracle model, as long as the recipient is honest, and remains secure even when additionally considering malicious recipients, as long as the related randomness functions does not allow the malicious recipients to efficiently compute the randomness used for the honest recipient. We furthermore show that, under the RSA assumption, the RSA-PSS signature scheme is secure for any class of related randomness functions, although with a non-tight security reduction. However, under additional, albeit somewhat restrictive assumptions on the related randomness functions and the adversary, a tight reduction can be recovered. Our results provides some reassurance regarding the use of RSA-OAEP and RSA-PSS in environments where randomness failures might be a concern. Lastly, we note that, unlike RSA-OAEP and RSA-PSS, several other schemes, including RSA-KEM, part of ISO 18033-2, and DHIES, part of IEEE P1363a, are not secure under simple repeated randomness attacks.

In this paper we conduct an empirical study with the purpose of identifying common software weaknesses of embedded devices used as part of industrial control systems in power grids. The data is gathered about the devices and software of 6 companies, ABB, General Electric, Schneider Electric, Schweitzer Engineering Laboratories, Siemens and Wind River. The study uses data from the manufacturersfi online databases, NVD, CWE and ICS CERT. We identified that the most common problems that were reported are related to the improper input validation, cryptographic issues, and programming errors.

We use model-based testing techniques to detect logical vulnerabilities in implementations of the Wi-Fi handshake. This reveals new fingerprinting techniques, multiple downgrade attacks, and Denial of Service (DoS) vulnerabilities. Stations use the Wi-Fi handshake to securely connect with wireless networks. In this handshake, mutually supported capabilities are determined, and fresh pairwise keys are negotiated. As a result, a proper implementation of the Wi-Fi handshake is essential in protecting all subsequent traffic. To detect the presence of erroneous behaviour, we propose a model-based technique that generates a set of representative test cases. These tests cover all states of the Wi-Fi handshake, and explore various edge cases in each state. We then treat the implementation under test as a black box, and execute all generated tests. Determining whether a failed test introduces a security weakness is done manually. We tested 12 implementations using this approach, and discovered irregularities in all of them. Our findings include fingerprinting mechanisms, DoS attacks, and downgrade attacks where an adversary can force usage of the insecure WPA-TKIP cipher. Finally, we explain how one of our downgrade attacks highlights incorrect claims made in the 802.11 standard.

Today, the proportion of software in society as a whole is steadily increasing. In addition to size of software increasing, the number of cases dealing with personal information is also increasing. This shows the importance of weekly software security verification. However, software security is very difficult in cases where libraries do not have source code. To solve this problem, it is necessary to develop a technique for checking existing binary security weaknesses. To this end, techniques for analyzing security weaknesses using intermediate languages are actively being discussed. In this paper, we propose a system that translate binary code to intermediate language to effectively analyze existing security weaknesses within binary code.

Due to flexibility, low cost and rapid deployment, wireless sensor networks (WSNs)have been drawing more and more interest from governments, researchers, application developers, and manufacturers in recent years. Nowadays, we are in the age of industry 4.0, in which the traditional industrial control systems will be connected with each other and provide intelligent manufacturing. Therefore, WSNs can play an extremely crucial role to monitor the environment and condition parameters for smart factories. Nevertheless, the introduction of the WSNs reveals the weakness, especially for industrial applications. Through the vulnerability of IWSNs, the latent attackers were likely to invade the information system. Risk evaluation is an overwhelmingly efficient method to reduce the risk of information system in order to an acceptable level. This paper aim to study the security issues about IWSNs as well as put forward a practical solution to evaluate the risk of IWSNs, which can guide us to make risk evaluation process and improve the security of IWSNs through appropriate countermeasures.

In this paper, an industrial testbed is proposed utilizing commercial-off-the-shelf equipment, and it is used to study the weakness of industrial Ethernet, i.e., PROFINET. The investigation is based on observation of the principles of operation of PROFINET and the functionality of industrial control systems.

Recently, Jung et al. [1] proposed a data access privilege scheme and claimed that their scheme addresses data and identity privacy as well as multi-authority, and provides data access privilege for attribute-based encryption. In this paper, we show that this scheme, and also its former and latest versions (i.e. [2] and [3] respectively) suffer from a number of weaknesses in terms of finegrained access control, users and authorities collusion attack, user authorization, and user anonymity protection. We then propose our new scheme that overcomes these shortcomings. We also prove the security of our scheme against user collusion attacks, authority collusion attacks and chosen plaintext attacks. Lastly, we show that the efficiency of our scheme is comparable with existing related schemes.

The factors that threaten electric power information network are analyzed. Aiming at the weakness of being unable to provide numerical value of risk, this paper presents the evaluation index system, the evaluation model and method of network security based on multilevel fuzzy comprehensive judgment. The steps and method of security evaluation by the synthesis evaluation model are provided. The results show that this method is effective to evaluate the risk of electric power information network.