Biblio

with the development of SDN, ICN and 5G networks, the research of future network becomes a hot topic. Based on the design idea of SDN network, this paper analyzes the propagation model and detection method of malicious code in future network. We select characteristics of SDN and analyze the features use different feature selection methods and sort the features. After comparison the influence of running time by different classification algorithm of different feature selection, we analyze the choice of reduction dimension m, and find out the different types of malicious code corresponding to the optimal feature subset and matching classification method, designed for malware detection system. We analyze the node migration rate of malware in mobile network and its effect on the outbreak of the time. In this way, it can provide reference for the management strategy of the switch node or the host node by future network controller.

Aiming to the current lack of VoIP voice encryption, a dynamic encryption method on grouping voice encryption and parallel encrypted is proposed in this paper. Though dynamic selection of encryption algorithms and dynamic distribution of key to increase the complexity of the encryption, at the same time reduce the time complexity of asymmetric encryption algorithm by using parallel encryption to ensure the real-time of the voice and improve call security.

Cloud computing, cloud security and cloud storage have been gradually introduced into people's life and become hot topicsof research, for which relevant technologies have permeated through the computer industry and relevant industries. With the coming of the modern information society, secure storage of data has been becoming increasingly important. Proceeding from traditional policy storage, this paper includes comparison and improvement of policy storage for the purpose of meeting requirements of storage of cloud security policy. Policy storage technology refers to a technology used to realize storage of policies created by users and relevant policy information. Policy repository can conduct centralized management and processing of multiple policies and their relevant information. At present, popular policy repositories generally include policy storage for relational database or policy storage for directory server or a file in a fixed format, such as XML file format.

In view of the demand for the continuous guarantee capability of the information system in the diversified task and the complex cyber threat environment, a dual loop architecture of the resilient cloud environment for mission assurance is proposed. Firstly, general technical architecture of cloud environment is briefly introduced. Drawing on the idea of software definition, a resilient dual loop architecture based on "perception analysis planning adjustment" is constructed. Then, the core mission assurance system deployment mechanism is designed using the idea of distributed control. Finally, the core mission assurance system is designed in detail, which is consisted of six functional modules, including mission and environment awareness network, intelligent anomaly analysis and prediction, mission and resource situation generation, mission and resource planning, adaptive optimization and adjustment. The design of the dual loop architecture of the resilient cloud environment for mission assurance will further enhance the fast adaptability of the information system in the complex cyber physical environment.

Containers have transformed the cluster management into an application oriented endeavor, thus being widely used as the deployment units (i.e., micro-services) of large scale cloud services. As opposed to VMs, containers allow for resource provisioning with fine granularity and their resource usage directly reflects the micro-service behaviors. Container management systems like Kubernetes and Mesos provision resources to containers according to the capacity requested by the developers. Resource usages estimated by the developers are grossly inaccurate. They tend to be risk-averse and over provision resources, as under-provisioning would cause poor runtime performance or failures. Without actually running the workloads, resource provisioning is challenging. However, benchmarking production workloads at scale requires huge manual efforts. In this work, we leverage IBM Monitoring service to profile the resource usage of production IBM Watson services in rolling windows by focusing on both evaluating how developers request resources and characterizing the actual resource usage. Our resource profiling study reveals two important characteristics of the cognitive workloads. 1. Stationarity. According to Augmented Dickey-Fuller test with 95% confidence, more than 95% of the container instances have stationary CPU usage while more than 85% have stationary memory usage, indicating that resource usage statistics do not change over time. We find for the majority of containers that the stationarity can be detected at the early stage of container execution and can hold throughout their lifespans. In addition, containers with non-stationary CPU or memory usage are also observed to implement predictable usage trends and patterns (e.g., trend stationarity or seasonality). 2. Predictability by container image. By clustering the containers based on their images, container resource usages within the same cluster are observed to exhibit strong statistical similarity. This suggests that the history of resource usage for one instance can be used to predict usage for future instances that run the same container image. Based on profiling results of running containers in rolling windows, we propose a resource usage advisory system to refine the requested resource values of the running and arriving containers as illustrated in Fig. 1. Our system continuously retrieves the resource usage metrics of running containers from IBM monitoring service and predicts the resource usage profiles in a container resource usage prediction agent. Upon the arrival of a new pod1, the resource profile advisor, proposed as a module in the web-hooked admission controller in Kubernetes, checks whether the resource profile of each container in the pod has been predicted with confidence. If a container's profile has been predicted and cached in the container resource profile database, the default requested values of containers are refined by the predicted ones; otherwise, containers are forwarded to the scheduler without any change. Similarly, a resource profile auto-scaler is proposed to update the requested resource values of containers for running pods2 as soon as the database is updated. Our study shows that developers request at least 1 core-per-second (cps) CPU and 1 GB memory for ≥ 70% of the containers, while ≥ 80% of the containers actually use less than 1 cps and 1GB. Additionally, \textbackslashtextasciitilde 20% of the containers are significantly under provisioned. We use resource usage data in one day to generate container resource profiles and evaluate our approach based on the actual usage on the following day. Without our system, average CPU (memory) usage for \textbackslashtextgreater90% of containers lies outside of 50% - 100% (70% - 100%) of the requested values. Our evaluation shows that our system can advise request values appropriately so that average and 95th percentile CPU (memory) usage for \textbackslashtextgreater90% of the containers are within 50% - 100% (70% - 100%) of the requested values. Furthermore, average CPU (memory) utilization across all pods is raised from 10% (26%) to 54% (88%).

This paper presents a methodology for IP core protection of CE devices from both buyer's and seller's perspective. In the presented methodology, buyer fingerprint is embedded along seller watermark during architectural synthesis phase of IP core design. The buyer fingerprint is inserted during scheduling phase while seller watermark is implanted during register allocation phase of architectural synthesis process. The presented approach provides a robust mechanisms of IP core protection for both buyer and seller at zero area overhead, 1.1 % latency overhead and 0.95 % design cost overhead compared to a similar approach (that provides only protection to IP seller).

Introduction of SDN (Software-Defined Networking) into the network management turns the formerly quite rigid networks to programmatically reconfigurable, dynamic and high-performing entities, which are managed remotely. At the same time, introduction of the new interfaces evidently widens the attack surface, and new kind of attack vectors are introduced threatening the QoS even critically. Thus, there is need for a security architecture, drawing from the SDN management and monitoring capabilities, and eventually covering the threats posed by the SDN evolution. For efficient security-architecture implementation, we analyze the security risks of SDN and based on that propose heuristic security objectives. Further, we decompose the objectives for effective security control implementation and security metrics definition to support informed security decision-making and continuous security improvement.

Applications in C/C++ are notoriously prone to memory corruptions. With significant research efforts devoted to this area of study, the security threats posed by previously popular vulnerabilities, such as stack and heap overflows, are not as serious as before. Instead, we have seen the meteoric rise of attacks exploiting use-after-free (UaF) vulnerabilities in recent years, which root in pointers pointing to freed memory (i.e., dangling pointers). Although various approaches have been proposed to harden software against UaF, none of them can achieve robustness and efficiency at the same time. In this paper, we present a novel defense called pSweeper to robustly protect against UaF exploits with low overhead, and pinpoint the root-causes of UaF vulnerabilities with one safe crash. The success of pSweeper lies in its two unique and innovative design ideas, concurrent pointer sweeping (CPW) and object origin tracking (OOT). CPW exploits the increasingly available multi-cores on modern PCs and outsources the heavyweight security checks and enforcement to dedicated threads that can run on spare cores. Specifically, CPW iteratively sweeps all live pointers in a concurrent thread to find dangling pointers. This design is quite different from previous work that requires to track every pointer propagation to maintain accurate point-to relationship between pointers and objects. OOT can help to pinpoint the root-causes of UaF by informing developers of how a dangling pointer is created, i.e., how the problematic object is allocated and freed. We implement a prototype of pSweeper and validate its efficacy in real scenarios. Our experimental results show that pSweeper is effective in defeating real-world UaF exploits and efficient when deployed in production runs.

Insider threats have shown their great destructive power in information security and financial stability and have received widespread attention from governments and organizations. Traditional intrusion detection systems fail to be effective in insider attacks due to the lack of extensive knowledge for insider behavior patterns. Instead, a more sophisticated method is required to have a deeper understanding for activities that insiders communicate with the information system. In this paper, we design a classifier, a neural network model utilizing Long Short Term Memory (LSTM) to model user log as a natural language sequence and achieve role-based classification. LSTM Model can learn behavior patterns of different users by automatically extracting feature and detect anomalies when log patterns deviate from the trained model. To illustrate the effective of classification model, we design two experiments based on cmu dataset. Experimental evaluations have shown that our model can successfully distinguish different behavior pattern and detect malicious behavior.

Byte addressable persistent memory eliminates the need for serialization and deserialization of data, to and from persistent storage, allowing applications to interact with it through common store and load instructions. In the event of a process or system failure, applications rely on persistent techniques to provide consistent storage of data in non-volatile memory (NVM). For most of these techniques, consistency is ensured through logging of updates, with consequent intensive cache line flushing and persistent fences necessary to guarantee correctness. Undo log based approaches require store interposition and persistence fences before each in-place modification. Redo log based techniques can execute transactions using just two persistence fences, although they require store and load interposition which may incur a performance penalty for large transactions. So far, these techniques have been difficult to integrate with known memory allocators, requiring allocators or garbage collectors specifically designed for NVM. We present Romulus, a user-level library persistent transactional memory (PTM) which provides durable transactions through the usage of twin copies of the data. A transaction in Romulus requires at most four persistence fences, regardless of the transaction size. Romulus uses only store interposition. Any sequential implementation of a memory allocator can be adapted to work with Romulus. Thanks to its lightweight design and low synchronization overhead, Romulus achieves twice the throughput of current state of the art PTMs in update-only workloads, and more than one order of magnitude in read-mostly scenarios.

CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.

Recent worldwide cybersecurity attacks caused by Cryptographic Ransomware infected systems across countries and organizations with millions of dollars lost in paying extortion amounts. This form of malicious software takes user files hostage by encrypting them and demands a large ransom payment for providing the decryption key. Signature-based methods employed by Antivirus Software are insufficient to evade Ransomware attacks due to code obfuscation techniques and creation of new polymorphic variants everyday. Generic Malware Attack vectors are also not robust enough for detection as they do not completely track the specific behavioral patterns shown by Cryptographic Ransomware families. This work based on analysis of an extensive dataset of Ran-somware families presents RansomWall, a layered defense system for protection against Cryptographic Ransomware. It follows a Hybrid approach of combined Static and Dynamic analysis to generate a novel compact set of features that characterizes the Ransomware behavior. Presence of a Strong Trap Layer helps in early detection. It uses Machine Learning for unearthing zero-day intrusions. When initial layers of RansomWall tag a process for suspicious Ransomware behavior, files modified by the process are backed up for preserving user data until it is classified as Ransomware or Benign. We implemented RansomWall for Microsoft Windows operating system (the most attacked OS by Cryptographic Ransomware) and evaluated it against 574 samples from 12 Cryptographic Ransomware families in real-world user environments. The testing of RansomWall with various Machine Learning algorithms evaluated to 98.25% detection rate and near-zero false positives with Gradient Tree Boosting Algorithm. It also successfully detected 30 zero-day intrusion samples (having less than 10% detection rate with 60 Security Engines linked to VirusTotal).

Ransomware emerged in recent years as one of the most significant cyber threats facing both individuals and organizations, inflicting global damage costs that are estimated upwards of $1 billion in 2016 alone [23]. The increase in the scale and impact of recent ransomware attacks highlights the need of finding effective countermeasures. We present AntiBotics - a novel system for application authentication-based file access control. AntiBotics enforces a file access-control policy by presenting periodic identification/authorization challenges.

We implemented AntiBotics for Windows. Our experimental evaluation shows that contemporary ransomware programs are unable to encrypt any of the files protected by AntiBotics and that the daily rate of challenges it presents to users is very low. We discuss possible ways in which future ransomware may attempt to attack AntiBotics and explain how these attacks can be thwarted.

In context-aware applications, user's access privileges rely on both user's identity and context. Access control rules are usually statically defined while contexts and the system state can change dynamically. Changes in contexts can result in service disruptions. To address this issue, this poster proposes a reactive access control system that associates contingency plans with access control rules. Risk scores are also associated with actions part of the contingency plans. Such risks are estimated by using fuzzy inference. Our approach is cast into the XACML reference architecture.

This paper proposes a new adaptively distributed packet filtering mechanism to mitigate the DDoS attacks targeted at the victim's bandwidth. The mechanism employs IP traceback as a means of distinguishing attacks from legitimate traffic, and continuous action reinforcement learning automata, with an improved learning function, to compute effective filtering probabilities at filtering routers. The solution is evaluated through a number of experiments based on actual Internet data. The results show that the proposed solution achieves a high throughput of surviving legitimate traffic as a result of its high convergence speed, and can save the victim's bandwidth even in case of varying and intense attacks.

The improvement of the implementation of the RSA cryptographic algorithm for encrypting / decoding information flows based on the use of the vector-modular method of modular exponential is presented in this paper. This makes it possible to replace the complex operation of modular multiplication with the addition operation, which increases the speed of the RSA cryptosystem. The scheme of algorithms of modular multiplication and modular exponentiation is presented. The analytical and graphical comparison of the time complexities of the proposed and known approaches shows that the use of the vector-modular method reduces the temporal complexity of the modular exponential compared to the classical one.

With the development of large scale integrated circuits, the functions of the IoT chips have been increasingly perfect. The verification work has become one of the most important aspects. On the one hand, an efficient verification platform can ensure the correctness of the design. On the other hand, it can shorten the chip design cycle and reduce the design cost. In this paper, based on a transmission protocol of the IoT node, we propose a verification method which combines simulation verification and FPGA-based prototype verification. We also constructed a system verification platform for the IoT smart node chip combining two kinds of verification above. We have simulated and verificatied the related functions of the node chip using this platform successfully. It has a great reference value.

Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.

Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.

Including electronic identities (eIDs), such as passports or driving licenses in smartphones transforms them into a single point of failure: loss, theft, or malfunction would prevent their users even from identifying themselves e.g. during travel. Therefore, a secure backup of such identity data is paramount, and an obvious solution is to store encrypted backups on cloud servers. However, the critical challenge is how a user decrypts the encrypted data backup if the user's device gets lost or stolen and there is no longer a secure storage (e.g. smartphone) to keep the secret key. To address this issue, Password-Protected Secret Sharing (PPSS) schemes have been proposed which allow a user to store a secret key among n servers such that the user can later reconstruct the secret key. Unfortunately, PPSS schemes are not appropriate for some applications. For example, users will be highly unlikely to remember a cryptographically strong password when the smartphone is lost. Also, they still suffer from inefficiency. In this paper, we propose a new secret key reconstruction protocol based recently popular PPSS schemes with a Fuzzy Extractor which allows a client to recover secret keys from an only partially trusted server and an auxiliary device using multiple key shares and a biometric identifier. We prove the security of our proposed protocol in the random oracle model where the parties can be corrupted separately at any time. An initial performance analysis shows that it is efficient for this use case. 

While because the range of web users have increased exponentially, thus has the quantity of attacks that decide to use it for malicious functions. The vulnerability that has become usually exploited is thought as cross-site scripting (XSS). Cross-site Scripting (XSS) refers to client-side code injection attack whereby a malicious user will execute malicious scripts (also usually stated as a malicious payload) into a legitimate web site or web based application. XSS is amongst the foremost rampant of web based application vulnerabilities and happens once an internet based application makes use of un-validated or un-encoded user input at intervals the output it generates. In such instances, the victim is unaware that their data is being transferred from a website that he/she trusts to a different site controlled by the malicious user. In this paper we shall focus on type 1 or "non-persistent cross-site scripting". With non-persistent cross-site scripting, malicious code or script is embedded in a Web request, and then partially or entirely echoed (or "reflected") by the Web server without encoding or validation in the Web response. The malicious code or script is then executed in the client's Web browser which could lead to several negative outcomes, such as the theft of session data and accessing sensitive data within cookies. In order for this type of cross-site scripting to be successful, a malicious user must coerce a user into clicking a link that triggers the non-persistent cross-site scripting attack. This is usually done through an email that encourages the user to click on a provided malicious link, or to visit a web site that is fraught with malicious links. In this paper it will be discussed and elaborated as to how attack surfaces related to type 1 or "non-persistent cross-site scripting" attack shall be reduced using secure development life cycle practices and techniques.

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cybersecurity threats and attacks by utilizing data mining techniques in the field of Artificial Intelligence. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection utilizing machine learning algorithms for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.

Cyber-Physical Systems (CPS), such as Water Distribution Networks (WDNs), deploy digital devices to monitor and control the behavior of physical processes. These digital devices, however, are susceptible to cyber and physical attacks, that may alter their functionality, and therefore the integrity of their measurements/actions. In practice, industrial control systems utilize simple control laws, which rely on various sensor measurements and algorithms which are expected to operate normally. To reduce the impact of a potential failure, operators may deploy redundant components; this however may not be useful, e.g., when a cyber attack at a PLC component occurs. In this work, we address the problem of reducing vulnerability to cyber-physical attacks in water distribution networks. This is achieved by augmenting the graph which describes the information flow from sensors to actuators, by adding new connections and algorithms, to increase the number of redundant cyber components. These, in turn, increase the \textitcyber-physical security level, which is defined in the present paper as the number of malicious attacks a CPS may sustain before becoming unable to satisfy the control requirements. A proof-of-concept of the approach is demonstrated over a simple WDN, with intuition on how this can be used to increase the cyber-physical security level of the system.

We propose using memristor-based TCAMs (Ternary Content Addressable Memory) to accelerate Regular Expression (RegEx) matching. RegEx matching is a key function in network security, where deep packet inspection finds and filters out malicious actors. However, RegEx matching latency and power can be incredibly high and current proposals are challenged to perform wire-speed matching for large scale rulesets. Our approach dramatically decreases RegEx matching operating power, provides high throughput, and the use of mTCAMs enables novel compression techniques to expand ruleset sizes and allows future exploitation of the multi-state (analog) capabilities of memristors. We fabricated and demonstrated nanoscale memristor TCAM cells. SPICE simulations investigate mTCAM performance at scale and a mTCAM power model at 22nm demonstrates 0.2 fJ/bit/search energy for a 36x400 mTCAM. We further propose a tiled architecture which implements a Snort ruleset and assess the application performance. Compared to a state-of-the-art FPGA approach (2 Gbps,\textbackslashtextasciitilde1W), we show x4 throughput (8 Gbps) at 60% the power (0.62W) before applying standard TCAM power-saving techniques. Our performance comparison improves further when striding (searching multiple characters) is considered, resulting in 47.2 Gbps at 1.3W for our approach compared to 3.9 Gbps at 630mW for the strided FPGA NFA, demonstrating a promising path to wire-speed RegEx matching on large scale rulesets.