Biblio

Attacks on cloud-computing services are becoming more prevalent with recent victims including Tesla, Aviva Insurance and SIM-card manufacturer Gemalto[1]. The risk posed to organisations from malicious insiders is becoming more widely known about and consequently many are now investing in hardware, software and new processes to try to detect these attacks. As for all types of attack vector, there will always be those which are not known about and those which are known about but remain exceptionally difficult to detect - particularly in a timely manner. We believe that insider attacks are of particular concern in a cloud-computing environment, and that cloud-service providers should enhance their ability to detect them by means of indirect detection. We propose a combined attack-tree and kill-chain based method for identifying multiple indirect detection measures. Specifically, the use of attack trees enables us to encapsulate all detection opportunities for insider attacks in cloud-service environments. Overlaying the attack tree on top of a kill chain in turn facilitates indirect detection opportunities higher-up the tree as well as allowing the provider to determine how far an attack has progressed once suspicious activity is detected. We demonstrate the method through consideration of a specific type of insider attack - that of attempting to capture virtual machines in transit within a cloud cluster via use of a network tap, however, the process discussed here applies equally to all cloud paradigms.

The quantity of Internet of Things (IoT) devices in the marketplace and lack of security is staggering. The interconnectedness of IoT devices has increased the attack surface for hackers. "White Worm" technology has the potential to combat infiltrating malware. Before white worm technology becomes viable, its capabilities must be constrained to specific devices and limited to non-harmful actions. This paper addresses the current problem, international research, and the conflicting interest of individuals, businesses, and governments regarding white worm technology. Proposed is a new perspective on utilizing white worm technology to protect the vulnerability of IoT devices, while overcoming its challenges.

While the IT industry is embracing the cloud-native technologies, migrating from monolithic architecture to service-oriented architecture is not a trivial process. It involves a lot of dissection and abstraction. The layer of abstraction designed for simplifying the development quickly becomes the barrier of visibility and the source of misconfigurations. The complexity may give microservices a larger attack surface compared to monolithic applications. This talk presents a microservices threat modeling that uncovers the attack vectors hidden in each abstraction layer. Scenarios of security breaches in microservices platforms are discussed, followed by the countermeasures to close these attack vectors. Finally, a decision-making process for architecting secure microservices is presented.

Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.

Starting from our previous research where we in- troduced a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings, in this paper we further and significantly extend our past research contributions, and provide several novel contributions that complement our previous work in the investigated research field. Our proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the “pedigree” of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so- called Data-dRIven aggregate-PROvenance privacy-preserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest. Extensions and discussion on main motivations and principles of our proposed research, two relevant case studies that clearly state the need-for and covered (related) properties of supporting privacy- preserving management and analytics of big data in modern distributed systems, and an experimental assessment and analysis of our proposed DRIPROM framework are the major results of this paper.

To improve production efficiency, more industrial control systems are connected to IT networks, and more IT technologies are applied to industrial control networks, network security has become an important problem. Industrial control network security analysis and decision-making is a effective method to solve the problem, which can predict risks and support to make decisions before the actual fault of the industrial control network system has not occurred. This paper proposes a security analysis and decision-making method with forward reasoning based on strong relevant logic for industrial control networks. The paper presents a case study in security analysis and decision-making for industrial control networks. The result of the case study shows that the proposed method is effective.

Cyber-attacks against cloud storage infrastructure e.g. Amazon S3 and Google Cloud Storage, have increased in recent years. One reason for this development is the rising adoption of cloud storage for various purposes. Robust counter-measures are therefore required to tackle these attacks especially as traditional techniques are not appropriate for the evolving attacks. We propose a two-pronged approach to address these challenges in this paper. The first approach involves dynamic snapshotting and recovery strategies to detect and partially neutralize security events. The second approach builds on the initial step by automatically correlating the generated alerts with cloud event log, to extract actionable intelligence for incident response. Thus, malicious activities are investigated, identified and eliminated. This approach is implemented in SlingShot, a cloud threat detection and incident response system which extends our earlier work - CSBAuditor, which implements the first step. The proposed techniques work together in near real time to mitigate the aforementioned security issues on Amazon Web Services (AWS) and Google Cloud Platform (GCP). We evaluated our techniques using real cloud attacks implemented with static and dynamic methods. The average Mean Time to Detect is 30 seconds for both providers, while the Mean Time to Respond is 25 minutes and 90 minutes for AWS and GCP respectively. Thus, our proposal effectively tackles contemporary cloud attacks.

Device attestation is a procedure to verify whether an embedded device is running the intended application code. This way, protection against both physical attacks and remote attacks on the embedded software is aimed for. With the wide adoption of Field-Programmable Gate Arrays or FPGAs, hardware also became configurable, and hence susceptible to attacks (just like software). In addition, an upcoming trend for hardware-based attestation is the use of configurable FPGA hardware. Therefore, in order to attest a whole system that makes use of FPGAs, the status of both the software and the hardware needs to be verified, without the availability of a tamper-resistant hardware module.In this paper, we propose a solution in which a prover core on the FPGA performs an attestation of the entire FPGA, including a self-attestation. This way, the FPGA can be used as a tamper-resistant hardware module to perform hardware-based attestation of a processor, resulting in a protection of the entire hardware/software system against malicious code updates.

Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware- level Intellectual Property (IP) of the device providers. However, these intelligent devices might be abused and such vulnerability has not been identified. The unregulated usage of intelligent platforms and the lack of hardware-bounded IP protection impair the commercial advantage of the device provider and prohibit reliable technology transfer. Our goal is to design a systematic methodology that provides hardware-level IP protection and usage control for DNN applications on various platforms. To address the IP concern, we present DeepAttest, the first on-device DNN attestation method that certifies the legitimacy of the DNN program mapped to the device. DeepAttest works by designing a device-specific fingerprint which is encoded in the weights of the DNN deployed on the target platform. The embedded fingerprint (FP) is later extracted with the support of the Trusted Execution Environment (TEE). The existence of the pre-defined FP is used as the attestation criterion to determine whether the queried DNN is authenticated. Our attestation framework ensures that only authorized DNN programs yield the matching FP and are allowed for inference on the target device. DeepAttest provisions the device provider with a practical solution to limit the application usage of her manufactured hardware and prevents unauthorized or tampered DNNs from execution. We take an Algorithm/Software/Hardware co-design approach to optimize DeepAttest's overhead in terms of latency and energy consumption. To facilitate the deployment, we provide a high-level API of DeepAttest that can be seamlessly integrated into existing deep learning frameworks and TEEs for hardware-level IP protection and usage control. Extensive experiments corroborate the fidelity, reliability, security, and efficiency of DeepAttest on various DNN benchmarks and TEE-supported platforms.

Remote attestation is a security technique through which a remote trusted party (i.e., Verifier) checks the trust-worthiness of a potentially untrusted device (i.e., Prover). In the Internet of Things (IoT) systems, the existing remote attestation protocols propose various approaches to detect the modified software and physical tampering attacks. However, in an inter-operable IoT system, in which IoT devices interact autonomously among themselves, an additional problem arises: a compromised IoT service can influence the genuine operation of other invoked service, without changing the software of the latter. In this paper, we propose a protocol for Remote Attestation of Distributed IoT Services (RADIS), which verifies the trust-worthiness of distributed IoT services. Instead of attesting the complete memory content of the entire interoperable IoT devices, RADIS attests only the services involved in performing a certain functionality. RADIS relies on a control-flow attestation technique to detect IoT services that perform an unexpected operation due to their interactions with a malicious remote service. Our experiments show the effectiveness of our protocol in validating the integrity status of a distributed IoT service.

Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In particular, corruptions of the graph topology can degrade the performance of graph based learning algorithms severely. This is due to the fact that the prediction capability of these algorithms relies mostly on the similarity structure imposed by the graph connectivity. Therefore, detecting the location of the corruption and correcting the induced errors becomes crucial. There has been some recent work which tackles the detection problem, however these methods do not address the effect of the attack on the downstream learning task. In this work, we propose an algorithm that uses node copying to mitigate the degradation in classification that is caused by adversarial attacks. The proposed methodology is applied only after the model for the downstream task is trained and the added computation cost scales well for large graphs. Experimental results show the effectiveness of our approach for several real world datasets.

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.

K-anonymity is a popular model used in microdata publishing to protect individual privacy. This paper introduces the idea of ball tree and projection area density partition into k-anonymity algorithm.The traditional kd-tree implements the division by forming a super-rectangular, but the super-rectangular has the area angle, so it cannot guarantee that the records on the corner are most similar to the records in this area. In this paper, the super-sphere formed by the ball-tree is used to address this problem. We adopt projection area density partition to increase the density of the resulting recorded points. We implement our algorithm with the Gotrack dataset and the Adult dataset in UCI. The experimentation shows that the k-anonymity algorithm based on ball-tree and projection area density partition, obtains more anonymous groups, and the generalization rate is lower. The smaller the K is, the more obvious the result advantage is. The result indicates that our algorithm can make data usability even higher.

In this paper, we propose a conditional privacy-preserving authentication scheme based on pseudonyms and (t,n) threshold secret sharing, named CPPT, for vehicular communications. To achieve conditional privacy preservation, our scheme implements anonymous communications based on pseudonyms generated by hash chains. To prevent bad vehicles from conducting framed attacks on honest ones, CPPT introduces Shamir (t,n) threshold secret sharing technique. In addition, through two one-way hash chains, forward security and backward security are guaranteed, and it also optimize the revocation overhead. The size of certificate revocation list (CRL) is only proportional to the number of revoked vehicles and irrelated to how many pseudonymous certificates are held by the revoked vehicles. Extensive simulations demonstrate that CPPT outperforms ECPP, DCS, Hybrid and EMAP schemes in terms of revocation overhead, certificate updating overhead and authentication overhead.

In 2008, 3GPP proposed the Long Term Evolution (LTE) in version 8. The standard is used in high-speed wireless communication standard for mobile terminal in telecommunication. It supports subscribers to access internet via specific base station after authentication. These authentication processes were defined in standard TS33.401 and TS33.102 by 3GPP. Authenticated processing standard inherits the authentication and key agreement protocol in RFC3310 and has been changed into authenticated scheme suitable for LTE. In the origin LTE authenticated scheme, subscribers need to transfer its International Mobile Subscriber Identity (IMSI) with plaintext. The IMSI might be intercepted and traced by fake stations. In this work, we propose a new scheme with a pseudo IMSI so that fake stations cannot get the real IMSI and trace the subscriber. The subscriber can keep anonymous and be confirmed by the base station for the legality. The pseudo identity is unlinkable to the subscriber. Not only does the proposed scheme enhance the security but also it just has some extra costs for signature generation and verification as compared to the original scheme.

Threat intelligence, information about potential or current attacks to an organization, is an important component in cyber security territory. As new threats consecutively occurring, cyber security professionals always keep an eye on the latest threat intelligence in order to continuously lower the security risks for their organizations. Cyber threat intelligence is usually conveyed by structured data like CVE entities and unstructured data like articles and reports. Structured data are always under certain patterns that can be easily analyzed, while unstructured data have more difficulties to find fixed patterns to analyze. There exists plenty of methods and algorithms on information extraction from structured data, but no current work is complete or suitable for semantics extraction upon unstructured cyber threat intelligence data. In this paper, we introduce an idea of automatic tagging applying JAPE feature within GATE framework to perform semantics extraction upon cyber threat intelligence unstructured data such as articles and reports. We extract token entities from each cyber threat intelligence article or report and evaluate the usefulness of them. A threat intelligence ontology then can be constructed with the useful entities extracted from related resources and provide convenience for professionals to find latest useful threat intelligence they need.

By studying the problems of network information security in power system, this paper proposes a self-defense research and solution for power information network based on artificial intelligence. At the same time, it proposes active defense new technologies such as vulnerability scanning, baseline scanning, network security attack and defense drills in power information network security, aiming at improving the security level of network information and ensuring the security of the information network in the power system.

We propose a construction of an attribute-based authentication scheme (ABAuth). Our ABAuth is a challenge-and-response protocol which uses an attribute-based key-encapsulation mechanisum (ABKEM). The ABKEM is basically the one proposed by Ostrovsky-Sahai-Waters (ACM-CCS 2007), but in contrast to the original ABKEM our ABKEM is based on an asymmetric bilinear map for better computational efficiency. We also give a proof of one-way-CCA security of ABKEM in the asymmetric case, which leads to concurrent man-in-the-middle security of ABAuth. We note that the selective security is often enough for the case of authentication in contrast to the case of encryption. Then we evaluate our ABAuth by implementation as well as by discussion. We use the TEPLA library TEPLA for the asymmetric bilinear map that is Type-3 pairing on the BN curve.

Cloud computing is a new promising technology paradigm that can provide clients from the whole network with scalable storage resources and on-demand high-quality services. However, security concerns are raised when sensitive data are outsourced. Searchable encryption is a kind of cryptographic primitive that enables clients to selectively retrieve encrypted data, the existing schemes that support for sub-linear boolean queries are only considered in symmetric key setting, which makes a limitation for being widely deployed in many cloud applications. In order to address this issue, we propose a novel searchable asymmetric encryption scheme to support for sub-linear boolean query over encrypted data in a multi-client model that is extracted from an important observation that the outsourced database in cloud is continuously contributed and searched by multiple clients. For the purpose of introducing the scheme, we combine both the ideas of symmetric searchable encryption and public key searchable encryption and then design a novel secure inverted index. Furthermore, a detailed security analysis for our scheme is given under the simulation-based security definition. Finally, we conduct experiments for our construction on a real dataset (Enron) along with a performance analysis to show its practicality.

Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this paper, which is based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is first adopted to generate the initial values of a chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted by EC-ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. The experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against the chosen-plaintext attack which make it have potential applications for the image secure communications.

Detecting repackaged apps is one of the important issues in the Android ecosystem. Many attackers usually reverse engineer a legitimate app, modify or embed malicious codes into the app, repackage and distribute it in the online markets. They also employ code obfuscation techniques to hide app cloning or repackaging. In this paper, we propose a new technique for detecting repackaged Android apps, which is robust to code obfuscation. The technique analyzes the similarity of Android apps based on the method call information of component classes that receive implicit intents. We developed a tool Calldroid that implemented the proposed technique, and evaluated it on apps transformed using well-known obfuscators. The evaluation results showed that the proposed technique can effectively detect repackaged apps.

Recently, damage caused by ransomware has been increasing in PC and Android environments. There are many studies into real-time ransomware detection because the most important time to prevent encryption is before ransomware is able to execute its malicious process. Traditional analyses determine an application is ransomware or not by static/dynamic methods. Those analyses can serve as components of a method to detect ransomware in real time. However, problems can occur such as the inability to detect new/variant/unknown ransomware. These types require signed patches from a trusted party that can only be created after attacks occur. In a previous study into realtime new/variant/unknown ransomware detection in a PC environment, important files are monitored and only programs that have been previously analyzed and evaluated as nonmalicious are allowed. As such, programs that have not been analyzed are restricted from accessing important files. In an Android environment, this method can be applied using Android applications to prevent emerging threats and verify consistency with user intent. Thus, this paper proposes a method of detecting new/variant/unknown ransomware in real time in an Android environment.

Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeynets for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce an undiscounted, infinite-horizon Markov decision process on a continuous state space in order to model the defender's problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Finally, we simulate the policy for a conceptual network. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.

Advanced Persistent Threat (APT) attack has the characteristics of complex attack means, long duration and great harmfulness. Based on the idea of dynamic deception, the paper proposed an APT defense system framework, and analyzed the deception defense process. The paper proposed a hybrid encryption communication mechanism based on socket, a dynamic IP address generation method based on SM4, a dynamic timing selection method based on Viterbi algorithm and a dynamic policy allocation mechanism based on DHCPv6. Tests show that the defense system can dynamically change and effectively defense APT attacks.

In the context of Advanced Persistent Threat (APT) attacks, this paper introduces a model, called Nuke, which tries to provide a more operational reading of the attackers' lifecycle in a compromised network. It allows to consider the notions of regression; and repetitiveness of final objectives achievement. By confronting this model with examples of recent attacks (Equifax data breach and TV5Monde sabotage), we emphasize the importance of the attack chronology in the Cyber Threat Intelligence (CTI) reports, as well as the Tactics, Techniques and Procedures (TTP) used by the attacker during his progression.