Biblio

Code randomization is considered as the basis of mitigation against code reuse attacks, fundamentally supporting some recent proposals such as execute-only memory (XOM) that aims at dynamic return-oriented programming (ROP) attacks. However, existing code randomization methods are hard to achieve a good balance between high-randomization entropy and semantic consistency. In particular, they always ignore code semantic consistency, incurring performance loss and incompatibility with current security schemes, e.g., control flow integrity (CFI). In this paper, we present an enhanced code randomization method termed as HCRESC, which can improve the randomization entropy significantly, meanwhile ensure the semantic consistency between variants and the original code. HCRESC reschedules instructions within the range of functions rather than basic blocks, thus producing more variants of the original code and preserving the code's semantic. We implement HCRESC on Linux platform of x86-64 architecture and demonstrate that HCRESC can increase the randomization entropy of x86-64 code over than 120% compared with existing methods while ensuring control flow and size of the code unaltered.

Power communication network is an important infrastructure of power system. For a large number of widely distributed business terminals and communication terminals. The data protection is related to the safe and stable operation of the whole power grid. How to solve the problem that lots of nodes need a large number of keys and avoid the situation that these nodes cannot exchange information safely because of the lack of keys. In order to solve the problem, this paper proposed a segmentation and combination technology based on quantum key to extend the limited key. The basic idea was to obtain a division scheme according to different conditions, and divide a key into several different sub-keys, and then combine these key segments to generate new keys and distribute them to different terminals in the system. Sufficient keys were beneficial to key updating, and could effectively enhance the ability of communication system to resist damage and intrusion. Through the analysis and calculation, the validity of this method in the use of limited quantum keys to achieve the business data secure transmission of a large number of terminal was further verified.

Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial learning-based attacks, or adversarial attacks, where miscreants can avoid detection by the classification algorithm with very few perturbations of the input data. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defenses, the number of successfully crafted adversarial samples is reduced compared to an unprotected baseline system. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we show that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks.

This paper considers a pilot spoofing attack scenario in a massive MIMO system. A malicious user tries to disturb the channel estimation process by sending interference symbols to the base-station (BS) via the uplink. Another legitimate user counters by sending random symbols. The BS does not possess any partial channel state information (CSI) and distribution of symbols sent by malicious user a priori. For such scenario, this paper aims to separate the channel directions from the legitimate and malicious users to the BS, respectively. A blind channel separation algorithm based on estimating the characteristic function of the distribution of the signal space vector is proposed. Simulation results show that the proposed algorithm provides good channel separation performance in a typical massive MIMO system.

Software verification has been well applied in safety critical areas and has shown the ability to provide better quality assurance for modern software. However, as lines of code and complexity of software systems increase, the scalability of verification becomes a challenge. In this paper, we present an automatic software verification framework TSV to address the scalability issues: (i) the extended structural abstraction and property-guided program slicing to solve large-scale program verification problem, saving time and memory without losing accuracy; (ii) automatically select different verification methods according to the program and property context to improve the verification efficiency. For evaluation, we compare TSV's different configurations with existing C program verifiers based on open benchmarks. We found that TSV with auto-selection performs better than with bounded model checking only or with extended structural abstraction only. Compared to existing tools such as CMBC and CPAChecker, it acquires 10%-20% improvement of accuracy and 50%-90% improvement of memory consumption.

The communication security issue is of great importance and should not be ignored in backbone optical networks which is undergoing the evolution toward software defined networks (SDN). With the aim to solve this problem, this paper conducts deep analysis into the security challenge of software defined optical networks (SDON) and proposes a so-called security-enhanced signaling scheme of SDON. The proposed scheme makes full advantage of current OpenFIow protocol with some necessary extensions and security improvement, by combining digital signatures and message feedback with efficient PKI (Public Key Infrastructure) in signaling procedure of OpenFIow interaction. Thus, this security-enhanced signaling procedure is also designed in details to make sure the end-to-end trusted service connection. Simulation results show that this proposed approach can greatly improve the security level of large-scale optical network for Energy Internet services with better performance in term of connection success rate performance.

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). Poisoning attack, which is one of the most recognized security threats towards machine learning- based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

In order to evaluate the network security risks and implement effective defenses in industrial control system, a risk assessment method for industrial control systems based on attack graphs is proposed. Use the concept of network security elements to translate network attacks into network state migration problems and build an industrial control network attack graph model. In view of the current subjective evaluation of expert experience, the atomic attack probability assignment method and the CVSS evaluation system were introduced to evaluate the security status of the industrial control system. Finally, taking the centralized control system of the thermal power plant as the experimental background, the case analysis is performed. The experimental results show that the method can comprehensively analyze the potential safety hazards in the industrial control system and provide basis for the safety management personnel to take effective defense measures.