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AlShiab, Ismael, Leivadeas, Aris, Ibnkahla, Mohamed.  2021.  Virtual Sensing Networks and Dynamic RPL-Based Routing for IoT Sensing Services. ICC 2021 - IEEE International Conference on Communications. :1–6.
IoT applications are quickly evolving in scope and objectives while their focus is being shifted toward supporting dynamic users’ requirements. IoT users initiate applications and expect quick and reliable deployment without worrying about the underlying complexities of the required sensing and routing resources. On the other hand, IoT sensing nodes, sinks, and gateways are heterogeneous, have limited resources, and require significant cost and installation time. Sensing network-level virtualization through virtual Sensing Networks (VSNs) could play an important role in enabling the formation of virtual groups that link the needed IoT sensing and routing resources. These VSNs can be initiated on-demand with the goal to satisfy different IoT applications’ requirements. In this context, we present a joint algorithm for IoT Sensing Resource Allocation with Dynamic Resource-Based Routing (SRADRR). The SRADRR algorithm builds on the current distinguished empowerment of sensing networks using recent standards like RPL and 6LowPAN. The proposed algorithm suggests employing the RPL standard concepts to create DODAG routing trees that dynamically adapt according to the available sensing resources and the requirements of the running and arriving applications. Our results and implementation of the SRADRR reveal promising enhancements in the overall applications deployment rate.
Cai, Junhui, Li, Qianmu.  2020.  Machine Learning-Based Threat Identification of Industrial Internet. 2020 IEEE International Conference on Progress in Informatics and Computing (PIC). :335–340.
In order to improve production and management efficiency, traditional industrial control systems are gradually connected to the Internet, and more likely to use advanced modern information technologies, such as cloud computing, big data technology, and artificial intelligence. Industrial control system is widely used in national key infrastructure. Meanwhile, a variety of attack threats and risks follow, and once the industrial control network suffers maliciously attack, the loss caused is immeasurable. In order to improve the security and stability of the industrial Internet, this paper studies the industrial control network traffic threat identification technology based on machine learning methods, including GK-SVDD, RNN and KPCA reconstruction error algorithm, and proposes a heuristic method for selecting Gaussian kernel width parameter in GK-SVDD to accelerate real-time threat detection in industrial control environments. Experiments were conducted on two public industrial control network traffic datasets. Compared with the existing methods, these methods can obtain faster detection efficiency and better threat identification performance.
Dong, Sichen, Jiao, Jian, Li, Shuyu.  2020.  A Multiple-Replica Provable Data Possession Algorithm Based on Branch Authentication Tree. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :400–404.
The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; pattern classification; convolutional neural nets; computer network security; security of data; recurrent neural nets; data privacy; and cloud computing.
Yang, Ping, Shu, Hui, Kang, Fei, Bu, Wenjuan.  2020.  Automatically Generating Malware Summary Using Semantic Behavior Graphs (SBGs). 2020 Information Communication Technologies Conference (ICTC). :282–291.
In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.
Amjath, M.I.M., Senthooran, V..  2020.  Secure Communication Using Steganography in IoT Environment. 2020 2nd International Conference on Advancements in Computing (ICAC). 1:114—119.
IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.
Singh, Shivshakti, Inamdar, Aditi, Kore, Aishwarya, Pawar, Aprupa.  2020.  Analysis of Algorithms for User Authentication using Keystroke Dynamics. 2020 International Conference on Communication and Signal Processing (ICCSP). :0337—0341.
In the present scenario, security is the biggest concern in any domain of applications. The latest and widely used system for user authentication is a biometric system. This includes fingerprint recognition, retina recognition, and voice recognition. But these systems can be bypassed by masqueraders. To avoid this, a combination of these systems is used which becomes very costly. To overcome these two drawbacks keystroke dynamics were introduced in this field. Keystroke dynamics is a biometric authentication-based system on behavior, which is an automated method in which the identity of an individual is identified and confirmed based on the way and the rhythm of passwords typed on a keyboard by the individual. The work in this paper focuses on identifying the best algorithm for implementing an authentication system with the help of machine learning for user identification based on keystroke dynamics. Our proposed model which uses XGBoost gives a comparatively higher accuracy of 93.59% than the other algorithms for the dataset used.
Bhutta, Muhammad Nasir Mumtaz, Cruickshank, Haitham, Nadeem, Adnan.  2020.  A Framework for Key Management Architecture for DTN (KMAD): Requirements and Design. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT). :1–4.
Key Management in Delay Tolerant Networks (DTN) still remains an unsolved complex problem. Due to peculiar characteristics of DTN, important challenges that make it difficult to design key management architecture are: 1) no systematic requirement analysis is undertaken to define its components, their composition and prescribed functions; and 2) no framework is available for its seamless integration with Bundle Security Protocol (BSP). This paper proposes a Key Management Architecture for DTN (KMAD) to address challenges in DTN key management. The proposed architecture not only provides guidelines for key management in DTN but also caters for seamless integration with BSP. The framework utilizes public key cryptography to provide required security services to enable exchange of keying material, and information about security policy and cipher suites. The framework also supports secure exchange of control and data information in DTNs.
Sinha, Ayush, Chakrabarti, Sourin, Vyas, O.P..  2020.  Distributed Grid restoration based on graph theory. 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). :1–6.
With the emergence of smart grids as the primary means of distribution across wide areas, the importance of improving its resilience to faults and mishaps is increasing. The reliability of a distribution system depends upon its tolerance to attacks and the efficiency of restoration after an attack occurs. This paper proposes a unique approach to the restoration of smart grids under attack by impostors or due to natural calamities via optimal islanding of the grid with primary generators and distributed generators(DGs) into sub-grids minimizing the amount of load shed which needs to be incurred and at the same time minimizing the number of switching operations via graph theory. The minimum load which needs to be shed is computed in the first stage followed by selecting the nodes whose load needs to be shed to achieve such a configuration and then finally deriving the sequence of switching operations required to achieve the configuration. The proposed method is tested against standard IEEE 37-bus and a 1069-bus grid system and the minimum load shed along with the sequencing steps to optimal configuration and time to achieve such a configuration are presented which demonstrates the effectiveness of the method when compared to the existing methods in the field. Moreover, the proposed algorithm can be easily modified to incorporate any other constraints which might arise due to any operational configuration of the grid.
Flores, Hugo, Tran, Vincent, Tang, Bin.  2020.  PAM PAL: Policy-Aware Virtual Machine Migration and Placement in Dynamic Cloud Data Centers. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2549—2558.
We focus on policy-aware data centers (PADCs), wherein virtual machine (VM) traffic traverses a sequence of middleboxes (MBs) for security and performance purposes, and propose two new VM placement and migration problems. We first study PAL: policy-aware virtual machine placement. Given a PADC with a data center policy that communicating VM pairs must satisfy, the goal of PAL is to place the VMs into the PADC to minimize their total communication cost. Due to dynamic traffic loads in PADCs, however, above VM placement may no longer be optimal after some time. We thus study PAM: policy-aware virtual machine migration. Given an existing VM placement in the PADC and dynamic traffic rates among communicating VMs, PAM migrates VMs in order to minimize the total cost of migration and communication of the VM pairs. We design optimal, approximation, and heuristic policyaware VM placement and migration algorithms. Our experiments show that i) VM migration is an effective technique, reducing total communication cost of VM pairs by 25%, ii) our PAL algorithms outperform state-of-the-art VM placement algorithm that is oblivious to data center policies by 40-50%, and iii) our PAM algorithms outperform the only existing policy-aware VM migration scheme by 30%.
Mengli, Zhou, Fucai, Chen, Wenyan, Liu, Hao, Liang.  2020.  Negative Feedback Dynamic Scheduling Algorithm based on Mimic Defense in Cloud Environment. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :2265–2270.
The virtualization technology in cloud environment brings some data and privacy security issues to users. Aiming at the problems of virtual machines singleness, homogeneity and static state in cloud environment, a negative feedback dynamic scheduling algorithm is proposed. This algorithm is based on mimic defense and creates multiple virtual machines to complete user request services together through negative feedback control mechanism which can achieve real-time monitor of the running state of virtual machines. When virtual machines state is found to be inconsistent, this algorithm will dynamically change its execution environment, resulting in the attacker's information collection and vulnerability exploitation process being disrupting. Experiments show that the algorithm can better solve security threats caused by the singleness, homogeneity and static state of virtual machines in the cloud, and improve security and reliability of cloud users.
Xiong, Yi, Li, Zhongkui.  2020.  Privacy Preserving Average Consensus by Adding Edge-based Perturbation Signals. 2020 IEEE Conference on Control Technology and Applications (CCTA). :712—717.
In this paper, the privacy preserving average consensus problem of multi-agent systems with strongly connected and weight balanced graph is considered. In most existing consensus algorithms, the agents need to exchange their state information, which leads to the disclosure of their initial states. This might be undesirable because agents' initial states may contain some important and sensitive information. To solve the problem, we propose a novel distributed algorithm, which can guarantee average consensus and meanwhile preserve the agents' privacy. This algorithm assigns some additive perturbation signals on the communication edges and these perturbations signals will be added to original true states for information exchanging. This ensures that direct disclosure of initial states can be avoided. Then a rigid analysis of our algorithm's privacy preserving performance is provided. For any individual agent in the network, we present a necessary and sufficient condition under which its privacy is preserved. The effectiveness of our algorithm is demonstrated by a numerical simulation.
Gohari, Parham, Hale, Matthew, Topcu, Ufuk.  2020.  Privacy-Preserving Policy Synthesis in Markov Decision Processes. 2020 59th IEEE Conference on Decision and Control (CDC). :6266—6271.
In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation's investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections.
Jing, Si-Yuan, Yang, Jun.  2020.  Efficient attribute reduction based on rough sets and differential evolution algorithm. 2020 16th International Conference on Computational Intelligence and Security (CIS). :217–222.
Attribute reduction algorithms in rough set theory can be classified into two groups, i.e. heuristics algorithms and computational intelligence algorithms. The former has good search efficiency but it can not find the global optimal reduction. Conversely, the latter is possible to find global optimal reduction but usually suffers from premature convergence. To address this problem, this paper proposes a two-stage algorithm for finding high quality reduction. In first stage, a classical differential evolution algorithm is employed to rapidly approach the optimal solution. When the premature convergence is detected, a local search algorithm which is intuitively a forward-backward heuristics is launched to improve the quality of the reduction. Experiments were performed on six UCI data sets and the results show that the proposed algorithm can outperform the existing computational intelligence algorithms.
Ravikumar, Gelli, Hyder, Burhan, Govindarasu, Manimaran.  2020.  Efficient Modeling of IEC-61850 Logical Nodes in IEDs for Scalability in CPS Security Testbed. 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T D). :1–5.

Though the deep penetration of cyber systems across the smart grid sub-domains enrich the operation of the wide-area protection, control, and smart grid applications, the stochastic nature of cyber-attacks by adversaries inflict their performance and the system operation. Various hardware-in-the-loop (HIL) cyber-physical system (CPS) testbeds have attempted to evaluate the cyberattack dynamics and power system perturbations for robust wide-area protection algorithms. However, physical resource constraints and modular integration designs have been significant barriers while modeling large-scale grid models (scalability) and have limited many of the CPS testbeds to either small-scale HIL environment or complete simulation environments. This paper proposes a meticulous design and efficient modeling of IEC-61850 logical nodes in physical relays to simulate large-scale grid models in a HIL real-time digital simulator environment integrated with industry-grade hardware and software systems for wide-area power system applications. The proposed meticulous design includes multi-breaker emulation in the physical relays, which extends the capacity of a physical relay to accommodate more number of CPS interfaces in the HIL CPS security testbed environment. We have used our existing HIL CPS security testbed to demonstrate scalability by the real-time performance of ten simultaneous IEEE-39 CPS grid models. The experiments demonstrated significant results by 100% real-time performance with zero overruns, and low latency while receiving and executing control signals from physical SEL relays via IEC-61850 and DNP-3 protocols to real-time digital simulator, substation remote terminal unit (RTU) software and supervisory control and data acquisition (SCADA) software at control center.

Zhang, ZhiShuo, Zhang, Wei, Qin, Zhiguang.  2020.  Multi-Authority CP-ABE with Dynamical Revocation in Space-Air-Ground Integrated Network. 2020 International Conference on Space-Air-Ground Computing (SAGC). :76–81.
Space-air-ground integrated network (SAGIN) is emerged as a versatile computing and traffic architecture in recent years. Though SAGIN brings many significant benefits for modern communication and computing services, there are many unprecedented challenges in SAGIN. The one critical challenge in SAGIN is the data security. In SAGIN, because the data will be stored in cleartext on cloud, the sensitive data may suffer from the illegal access by the unauthorized users even the untrusted cloud servers (CSs). Ciphertext-policy attribute-based encryption (CP-ABE), which is a type of attribute-based encryption (ABE), has been regarded as a promising solution to the critical challenge of the data security on cloud. But there are two main blemishes in traditional CP-ABE. The first one is that there is only one attribute authority (AA) in CP-ABE. If the single AA crashs down, the whole system will be shut down. The second one is that the AA cannot effectively manage the life cycle of the users’ private keys. If a user on longer has one attribute, the AA cannot revoke the user’s private key of this attribute. This means the user can still decrypt some ciphertexts using this invalid attribute. In this paper, to solve the two flaws mentioned above, we propose a multi-authority CP-ABE (MA-CP-ABE) scheme with the dynamical key revocation (DKR). Our key revocation supports both user revocation and attribute revocation. And the our revocation is time friendly. What’s more, by using our dynamically tag-based revocation algorithm, AAs can dynamically and directly re-enable or revoke the invalid attributes to users. Finally, by evaluating and implementing our scheme, we can observe that our scheme is more comprehensive and practical for cloud applications in SAGIN.
Nie, Guanglai, Zhang, Zheng, Zhao, Yufeng.  2020.  The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface. 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). :281–287.
In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm.
Fischer, A., Janneck, J., Kussmaul, J., Krätzschmar, N., Kerschbaum, F., Bodden, E..  2020.  PASAPTO: Policy-aware Security and Performance Trade-off Analysis–Computation on Encrypted Data with Restricted Leakage. 2020 IEEE 33rd Computer Security Foundations Symposium (CSF). :230—245.

This work considers the trade-off between security and performance when revealing partial information about encrypted data computed on. The focus of our work is on information revealed through control flow side-channels when executing programs on encrypted data. We use quantitative information flow to measure security, running time to measure performance and program transformation techniques to alter the trade-off between the two. Combined with information flow policies, we perform a policy-aware security and performance trade-off (PASAPTO) analysis. We formalize the problem of PASAPTO analysis as an optimization problem, prove the NP-hardness of the corresponding decision problem and present two algorithms solving it heuristically. We implemented our algorithms and combined them with the Dataflow Authentication (DFAuth) approach for outsourcing sensitive computations. Our DFAuth Trade-off Analyzer (DFATA) takes Java Bytecode operating on plaintext data and an associated information flow policy as input. It outputs semantically equivalent program variants operating on encrypted data which are policy-compliant and approximately Pareto-optimal with respect to leakage and performance. We evaluated DFATA in a commercial cloud environment using Java programs, e.g., a decision tree program performing machine learning on medical data. The decision tree variant with the worst performance is 357% slower than the fastest variant. Leakage varies between 0% and 17% of the input.

Harada, T., Tanaka, K., Ogasawara, R., Mikawa, K..  2020.  A Rule Reordering Method via Pairing Dependent Rules. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.
Packet classification is used to determine the behavior of incoming packets to network devices. Because it is achieved using a linear search on a classification rule list, a larger number of rules leads to a longer communication latency. To decrease this latency, the problem is generalized as Optimal Rule Ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Because ORO is known to be NP-complete by Hamed and Al-Shaer [Dynamic rule-ordering optimization for high-speed firewall filtering, ASIACCS (2006) 332-342], various heuristics for ORO have been proposed. Sub-graph merging (SGM) by Tapdiya and Fulp [Towards optimal firewall rule ordering utilizing directed acyclical graphs, ICCCN (2009) 1-6] is the state of the art heuristic algorithm for ORO. In this paper, we propose a novel heuristic method for ORO. Although most heuristics try to recursively determine the maximum-weight rule and move it as far as possible to an upper position, our algorithm pairs rules that cause policy violations until there are no such rules to simply sort the rules by these weights. Our algorithm markedly decreases the classification latency and reordering time compared with SGM in experiments. The sets consisting of thousands of rules that require one or more hours for reordering by SGM can be reordered by the proposed method within one minute.
Lyshevski, S. E., Aved, A., Morrone, P..  2020.  Information-Centric Cyberattack Analysis and Spatiotemporal Networks Applied to Cyber-Physical Systems. 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW). 1:172—177.

Cyber-physical systems (CPS) depend on cybersecurity to ensure functionality, data quality, cyberattack resilience, etc. There are known and unknown cyber threats and attacks that pose significant risks. Information assurance and information security are critical. Many systems are vulnerable to intelligence exploitation and cyberattacks. By investigating cybersecurity risks and formal representation of CPS using spatiotemporal dynamic graphs and networks, this paper investigates topics and solutions aimed to examine and empower: (1) Cybersecurity capabilities; (2) Information assurance and system vulnerabilities; (3) Detection of cyber threat and attacks; (4) Situational awareness; etc. We introduce statistically-characterized dynamic graphs, novel entropy-centric algorithms and calculi which promise to ensure near-real-time capabilities.

Zhou, J., Zhang, X., Liu, Y., Lan, X..  2020.  Facial Expression Recognition Using Spatial-Temporal Semantic Graph Network. 2020 IEEE International Conference on Image Processing (ICIP). :1961—1965.

Motions of facial components convey significant information of facial expressions. Although remarkable advancement has been made, the dynamic of facial topology has not been fully exploited. In this paper, a novel facial expression recognition (FER) algorithm called Spatial Temporal Semantic Graph Network (STSGN) is proposed to automatically learn spatial and temporal patterns through end-to-end feature learning from facial topology structure. The proposed algorithm not only has greater discriminative power to capture the dynamic patterns of facial expression and stronger generalization capability to handle different variations but also higher interpretability. Experimental evaluation on two popular datasets, CK+ and Oulu-CASIA, shows that our algorithm achieves more competitive results than other state-of-the-art methods.

Feng, G., Zhang, C., Si, Y., Lang, L..  2020.  An Encryption and Decryption Algorithm Based on Random Dynamic Hash and Bits Scrambling. 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). :317–320.
This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..
Li, Y., Zhou, W., Wang, H..  2020.  F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm. IEEE Access. 8:165963–165972.
Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.
Huo, T., Wang, W., Zhao, P., Li, Y., Wang, T., Li, M..  2020.  TEADS: A Defense-Aware Framework for Synthesizing Transient Execution Attacks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :320—327.

Since 2018, a broad class of microarchitectural attacks called transient execution attacks (e.g., Spectre and Meltdown) have been disclosed. By abusing speculative execution mechanisms in modern CPUs, these attacks enable adversaries to leak secrets across security boundaries. A transient execution attack typically evolves through multiple stages, termed the attack chain. We find that current transient execution attacks usually rely on static attack chains, resulting in that any blockage in an attack chain may cause the failure of the entire attack. In this paper, we propose a novel defense-aware framework, called TEADS, for synthesizing transient execution attacks dynamically. The main idea of TEADS is that: each attacking stage in a transient execution attack chain can be implemented in several ways, and the implementations used in different attacking stages can be combined together under certain constraints. By constructing an attacking graph representing combination relationships between the implementations and testing available paths in the attacking graph dynamically, we can finally synthesize transient execution attacks which can bypass the imposed defense techniques. Our contributions include: (1) proposing an automated defense-aware framework for synthesizing transient execution attacks, even though possible combinations of defense strategies are enabled; (2) presenting an attacking graph extension algorithm to detect potential attack chains dynamically; (3) implementing TEADS and testing it on several modern CPUs with different protection settings. Experimental results show that TEADS can bypass the defenses equipped, improving the adaptability and durability of transient execution attacks.

Stępień, K., Poniszewska-Marańda, A..  2020.  Security methods against Black Hole attacks in Vehicular Ad-Hoc Network. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1–4.
Vehicular Ad-Hoc Networks (VANET) are liable to the Black, Worm and Gray Hole attacks because of the broadcast nature of the wireless medium and a lack of authority standards. Black Hole attack covers the situation when a malicious node uses its routing protocol in order to publicize itself for having the shortest route to the destination node. This aggressive node publicizes its availability of fresh routes regardless of checking its routing table. The consequences of these attacks could lead not only to the broken infrastructure, but could cause hammering people's lives. This paper aims to investigate and compare methods for preventing such types of attacks in a VANET.
Xiao, R., Li, X., Pan, M., Zhao, N., Jiang, F., Wang, X..  2020.  Traffic Off-Loading over Uncertain Shared Spectrums with End-to-End Session Guarantee. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1–5.
As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.