Biblio

Security has always been one of the main concerns in the field of computer architecture and cloud computing. Cache-based side-channel attacks pose a threat to almost all existing architectures and cloud computing. Especially in the public cloud, the cache is shared among multiple tenants, and cache attacks can make good use of this to extract information. Cache side-channel attacks are a problem to be solved for security, in which how to accurately detect cache side-channel attacks has been a research hotspot. Because the cache side-channel attack does not require the attacker to physically contact the target device and does not need additional devices to obtain the side channel information, the cache-side channel attack is efficient and hidden, which poses a great threat to the security of cryptographic algorithms. Based on the AES algorithm, this paper uses hardware performance counters to obtain the features of different cache events under Flush + Reload, Prime + Probe, and Flush + Flush attacks. Firstly, the random forest algorithm is used to filter the cache features, and then the support vector machine algorithm is used to model the system. Finally, high detection accuracy is achieved under different system loads. The detection accuracy of the system is 99.92% when there is no load, the detection accuracy is 99.85% under the average load, and the detection accuracy under full load is 96.57%.

In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous mal ware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and linkable format when the training and test are done on the same platform. Meanwhile, it also achieves a 96% accuracy when training is carried out on a few popular IoT platforms but test is done on different platforms. Efficient malware prevention and mitigation solutions can be enabled based on the proposed method to prevent and mitigate IoT malware damages across different platforms.

Hardware security has long been an important issue in the current IC design. In this paper, a hardware Trojan detection method based on frequency domain characteristics of power consumption is proposed. For some HTs, it is difficult to detect based on the time domain characteristics, these types of hardware Trojan can be analyzed in the frequency domain, and Mahalanobis distance is used to classify designs with or without HTs. The experimental results demonstrate that taking 10% distance as the criterion, the hardware Trojan detection results in the frequency domain have almost no failure cases in all the tested designs.

Fuzzing has been widely adopted as an effective techniques to detect vulnerabilities in softwares. However, existing fuzzers suffer from the problems of generating excessive test inputs that either cannot pass input validation or are ineffective in exploring unvisited regions in the program under test (PUT). To tackle these problems, we propose a greybox fuzzer called MuFuzzer based on AFL, which incorporates two heuristics that optimize seed selection and automatically extract input formatting information from the PUT to increase the chance of generating valid test inputs, respectively. In particular, the first heuristic collects the branch coverage and execution information during a fuzz session, and utilizes such information to guide fuzzing tools in selecting seeds that are fast to execute, small in size, and more importantly, more likely to explore new behaviors of the PUT for subsequent fuzzing activities. The second heuristic automatically identifies string comparison operations that the PUT uses for input validation, and establishes a dictionary with string constants from these operations to help fuzzers generate test inputs that have higher chances to pass input validation. We have evaluated the performance of MuFuzzer, in terms of code coverage and bug detection, using a set of realistic programs and the LAVA-M test bench. Experiment results demonstrate that MuFuzzer is able to achieve higher code coverage and better or comparative bug detection performance than state-of-the-art fuzzers.

The characteristics of resource sharing and centralized deployment of network function virtualization (NFV) make the physical boundary under the traditional closed management mode disappear, bringing many new security threats to the network. To improve the security of the NFV network, this paper proposes a network function virtualization security architecture based on mimic defense. At the same time, to ensure the differences between heterogeneous entities, a genetic algorithm-based heterogeneous entities pool construction method is proposed. Simulation results show that this method can effectively guarantee the difference between heterogeneous entities and increase the difficulty of attackers.

In this paper, we proposed a path planning algorithm based on Q-learning model to simulate an environment model, which is suitable for the complex environment. A virtual simulation platform has been built to complete the experiments. The experimental results show that the algorithm proposed in this paper can be effectively applied to the solution of vehicle routing problems in the complex environment.

For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operation of the system. We model CPSs as a collection of input-output dynamical systems (the system operation modes). Depending on the mode the system is operating on, the output trajectory is generated by one of these systems in response to driving inputs. Output measurements and driving inputs are sent to the cloud for processing purposes. We capture this "processing" through some function (of the input-output trajectory) that we require the cloud to compute accurately - referred here as the trajectory utility. However, for privacy reasons, we would like to keep the mode private, i.e., we do not want the cloud to correctly identify what mode of the CPS produced a given trajectory. To this end, we distort trajectories before transmission and send the corrupted data to the cloud. We provide mathematical tools (based on output-regulation techniques) to properly design distorting mechanisms so that: 1) the original and distorted trajectories lead to the same utility; and the distorted data leads the cloud to misclassify the mode.

With the development of positioning technology and the popularity of mobile devices, location-based services have been widely deployed. To use the services, users must provide the server accurate location information, during which the attacker tends to infer sensitive information from intercepting queries. In this paper, we model the road network as an edge cluster graph with its location semantics considered. Then, we propose the Circle First Structure Optimization (CFSO) algorithm which generates an anonymous set by adding optimal adjacent locations. Furthermore, we introduce controllable randomness and propose the Attack-Resilient (AR) algorithm to enhance the anti-attack ability. Meanwhile, to reduce the system overhead, our algorithms build the anonymous set quickly and take the structure of the anonymous set into account. Finally, we conduct experiments on a real map and the results demonstrate a higher anonymity success rate and a stronger anti-attack capability with less system overhead.

With the development of smart grid technologies and the fast adoption of household IoT devices in recent years, new threats, attacks, and security challenges arise. While a large number of vulnerabilities, threats, attacks and controls have been discussed in the literature, there lacks an abstract and generalizable framework that can be used to model the cyber-physical interactions of attacks and guide the design of defense mechanisms. In this paper, we propose a new modeling approach for security attacks in smart grids and IoT devices using a Cyber-Physical Systems (CPS) perspective. The model considers both the cyber and physical aspects of the core components of the smart grid system and the household IoT devices, as well as the interactions between the components. In particular, our model recognizes the two parallel attack channels via the cyber world and the physical world, and identifies the potential crossing routes between these two attack channels. We further discuss all possible attack surfaces, attack objectives, and attack paths in this newly proposed model. As case studies, we examine from the perspective of this new model three representative attacks proposed in the literature. The analysis demonstrates the applicability of the model, for instance, to assist the design of detection and defense mechanisms against smart grid cyber-attacks.

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.

The key feature of NDN is in-network caching that every router has its cache to store data for future use, thus improve the usage of the network bandwidth and reduce the network latency. However, in-network caching increases the security risks - cache pollution attacks (CPA), which includes locality disruption (ruining the cache locality by sending random requests for unpopular contents to make them popular) and False Locality (introducing unpopular contents in the router's cache by sending requests for a set of unpopular contents). In this paper, we propose a machine learning method, named Triangle Area Based Multivariate Correlation Analysis (TAB-MCA) that detects the cache pollution attacks in NDN. This detection system has two parts, the triangle-area-based MCA technique, and the threshold-based anomaly detection technique. The TAB-MCA technique is used to extract hidden geometrical correlations between two distinct features for all possible permutations and the threshold-based anomaly detection technique. This technique helps our model to be able to distinguish attacks from legitimate traffic records without requiring prior knowledge. Our technique detects locality disruption, false locality, and combination of the two with high accuracy. Implementation of XC-topology, the proposed method shows high efficiency in mitigating these attacks. In comparison to other ML-methods, our proposed method has a low overhead cost in mitigating CPA as it doesn't require attackers' prior knowledge. Additionally, our method can also detect non-uniform attack distributions.

The following topics are dealt with: authorisation; data privacy; mobile computing; security of data; cryptography; Internet of Things; message authentication; invasive software; Android (operating system); vectors.

Traditional security solutions that rely on public key infrastructure present scalability and transparency challenges when deployed in Internet of Things (IoT). In this paper, we develop a blockchain based authentication mechanism for IoT that can be integrated into the traditional transport layer security protocols such as Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). Our proposed mechanism is an alternative to the traditional Certificate Authority (CA)-based Public Key Infrastructure (PKI) that relies on x.509 certificates. Specifically, the proposed solution enables the modified TLS/DTLS a viable option for resource constrained IoT devices where minimizing memory utilization is critical. Experiments show that blockchain based authentication can reduce dynamic memory usage by up to 20%, while only minimally increasing application image size and time of execution of the TLS/DTLS handshake.

IoT devices are open to traditional control flow integrity (CFI) attacks resulting from buffer overflow and return-oriented programming like techniques. They often have limited computational capacity ruling out many of the traditional heavy-duty software countermeasures. In this work, we deploy hardware/software solutions to detect CFI attacks. Some of the medium capability IoT devices, for example based on Raspberry Pi, contain ARM Cortex A-53 (Pi 3) or Cortex A-73 (Pi 4) processors. These processors include hardware counters to count microarchitecture level events affecting performance. Lighter weight IoT devices, say based on ARM Cortex M4 or M7, include DWT (Debug, Watch & Trace) module. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. The proposed anomaly detection mechanism is evaluated on ArduPilot [1] - a popular autopilot software on a Raspberry Pi 3 with PMU and DWT. A self-navigation program is evaluated on an iCreate Roomba platform with an ARM Cortex M4 processor with DWT only. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per control flow anomaly check.

Energy efficiency evaluation (EEE) is a research difficulty in the field of cloud computing. The current research mainly considers the relevant energy efficiency indicators of cloud systems and weights the interrelationship between energy consumption, system performance and QoS requirements. However, it lacks a combination of subjective and objective, qualitative and quantitative evaluation method to accurately evaluate the energy efficiency of cloud systems. We propose a novel EEE method based on the QoS parameter specification for cloud systems (EEE-QoS). Firstly, it reduces the metric values that affect QoS requirements to the same dimension range and then establishes a belief rule base (BRB). The best-worst method is utilized to determine the initial weights of the premise attributes in the BRB model. Then, the BRB model parameters are optimized by the mean-square error, the activation weight is calculated, and the activation rules of the evidence reasoning algorithm are integrated to evaluate the belief of the conclusion. The quantitative and qualitative evaluation of the energy efficiency of cloud systems is realized. The experiments show that the proposed method can accurately and objectively evaluate the energy efficiency of cloud systems.

This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For such a system, eavesdroppers having access to system output trajectories may infer the system initial states, leading to initial-value privacy risks. When a finite number of output trajectories are eavesdropped, we consider a requirement that any guess about the initial values can be plausibly denied. When an infinite number of output trajectories are eavesdropped, we consider a requirement that the initial values should not be uniquely recoverable. In view of these two privacy requirements, we define differential initial-value privacy and intrinsic initial-value privacy, respectively, for the system as metrics of privacy risks. First of all, we prove that the intrinsic initial-value privacy is equivalent to unobservability, while the differential initial-value privacy can be achieved for a privacy budget depending on an extended observability matrix of the system and the covariance of the noises. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network system perspective, we allow the initial states at some nodes to be public, and investigate the resulting intrinsic initial- value privacy of each individual node. We establish necessary and sufficient conditions for such individual node initial-value privacy, and also prove that the intrinsic initial-value privacy of individual nodes is generically determined by the network structure.

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.

With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis.

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.

The following topics are dealt with: formal verification; formal specification; cyber-physical systems; program verification; mobile robots; control engineering computing; temporal logic; security of data; Internet of Things; traffic engineering computing.

{With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.

Modern Internet TCP uses Secure Sockets Layers (SSL)/Transport Layer Security (TLS) for secure communication, which relies on Public Key Infrastructure (PKIs) to authenticate public keys. Conventional PKI is done by Certification Authorities (CAs), issuing and storing Digital Certificates, which are public keys of users with the users identity. This leads to centralization of authority with the CAs and the storage of CAs being vulnerable and imposes a security concern. There have been instances in the past where CAs have issued rogue certificates or the CAs have been hacked to issue malicious certificates. Motivated from these facts, in this paper, we propose a method (named as Trustful), which aims to build a decentralized PKI using blockchain. Blockchains provide immutable storage in a decentralized manner and allows us to write smart contracts. Ethereum blockchain can be used to build a web of trust model where users can publish attributes, validate attributes about other users by signing them and creating a trust store of users that they trust. Trustful works on the Web-of-Trust (WoT) model and allows for any entity on the network to verify attributes about any other entity through a trusted network. This provides an alternative to the conventional CA-based identity verification model. The proposed model has been implemented and tested for efficacy and known major security attacks.

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging.

Nowadays the provision of online services by government or business organizations has become a standard and necessary operation. Transferring data including the confidential or sensitive information via Internet or insecure network and exchange of them is also increased day by day. As a result, confidential information leakage and cyber threats are also heightened. Confidential information trading became one of the most profitable businesses. Encrypting the data is a solution to secure the data from being exposed. In this paper, we would like to propose a solution for the secure transfer of data using symmetric encryption, asymmetric encryption technologies and Key Generation Server as a mixed hybrid solution. A Symmetric encryption, modified AES algorithm, is used to encrypt data. Digital certificate is used both for data encryption and digital signing to assure data integrity. Key generation server is used to generate the second secret key from the publicly recognized information of a person and this key is used as a second secret key in the modified AES. The proposed hybrid solution can be utilized in any applications that require high confidentiality, integrity of data and non-repudiation.