Biblio

In this paper, we present a flexible Variable Precision Computation Array (VPCA) component for different accelerators, which leverages a sparsification scheme for activations and a low bits serial-parallel combination computation unit for improving the efficiency and resiliency of accelerators. The VPCA can dynamically decompose the width of activation/weights (from 32bit to 3bit in different accelerators) into 2-bits serial computation units while the 2bits computing units can be combined in parallel computing for high throughput. We propose an on-the-fly compressing and calculating strategy SLE-CLC (single lane encoding, cross lane calculation), which could further improve performance of 2-bit parallel computing. The experiments results on image classification datasets show VPCA can outperforms DaDianNao, Stripes, Loom-2bit by 4.67×, 2.42×, 1.52× without other overhead on convolution layers.

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.

The massive proliferation of state of the art interfaces into the automotive sector has triggered a revolution in terms of the technological ecosystem that is found in today's modern car. Accordingly, on the one hand, we find dozens of Electronic Control Units (ECUs) running several hundred MB of code, and more and more sophisticated dashboards with integrated wireless communications. On the other hand, in the same vehicle we find the underlying communication infrastructure struggling to keep up with the pace of these radical changes. This paper presents MixCAN (MIXed data authentication for Control Area Networks), an approach for mixing different message signatures (i.e., authentication tags) in order to reduce the overhead of Controller Area Network (CAN) communications. MixCAN leverages the attributes of Bloom Filters in order to ensure that an ECU can sign messages with different CAN identifiers (i.e., mix different message signatures), and that other ECUs can verify the signature for a subset of monitored CAN identifiers. Extensive experimental results based on Vectors Informatik's CANoe/CANalyzer simulation environment and the data set provided by Hacking and Countermeasure Research Lab (HCRL) confirm the validity and applicability of the developed approach. Subsequent experiments including a test bed consisting of Raspberry Pi 3 Model B+ systems equipped with CAN communication modules demonstrate the practical integration of MixCAN in real automotive systems.

Modern cryptographic ransomware pose a severe threat to the security of individuals and organizations. Targeted ransomware attacks exhibit refinement in attack vectors owing to the manual reconnaissance performed by the perpetrators for infiltration. The result is an impenetrable lock on multiple hosts within the organization which allows the cybercriminals to demand hefty ransoms. Reliance on prevention strategies is not sufficient and a firm comprehension of implementation details is necessary to develop effective solutions that can thwart ransomware after preventative strategies have failed. Ransomware depend heavily on the abstraction offered by Windows APIs. This paper provides a detailed review of the common API calls in ransomware. We propose four classes of API calls that can be used for profiling and generating effective API call relationships useful in automated detection. Finally, we present counts and visualizations pertaining to API call extraction from real-world ransomware that demonstrate that even advanced variants from different families carry similarities in implementation.

Information security is everyone's concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.

The following topics are dealt with: cloud computing; software defined networking; cryptography; telecommunication traffic; Internet of Things; authorisation; software radio; cryptocurrencies; data privacy; learning (artificial intelligence).

In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.

In the paper, an intrusion detection system to safeguard computer software is proposed. The detection is based on negative selection algorithm, inspired by the human immunity mechanism. It is composed of two stages, generation of receptors and anomaly detection. Experimental results of the proposed system are presented, analyzed, and concluded.

Software defined network (SDN) is an emerging network architecture. Its control logic and forwarding logic are separated. SDN has the characteristics of centralized management, which makes it easier for malicious attackers to use the security vulnerabilities of SDN networks to implement distributed denial Service (DDoS) attack. Information entropy is a kind of lightweight DDoS early detection method. This paper proposes a DDoS attack detection method in SDN networks based on φ-entropy. φ-entropy can adjust related parameters according to network conditions and enlarge feature differences between normal and abnormal traffic, which can make it easier to detect attacks in the early stages of DDoS traffic formation. Firstly, this article demonstrates the basic properties of φ-entropy, mathematically illustrates the feasibility of φ-entropy in DDoS detection, and then we use Mini-net to conduct simulation experiments to compare the detection effects of DDoS with Shannon entropy.

Real-time discovery of all different types of unique attributes within unstructured data is a challenging problem to solve when dealing with multiple petabytes of unstructured data volume everyday. Popular discovery solutions such as the creation of offline jobs to uniquely identify attributes or running aggregation queries on raw data sets limits real time discovery use-cases and often results into poor resource utilization. The discovery information must be treated as a parallel problem to just storing raw data sets efficiently onto back-end big data systems. Solving the discovery problem by creating a parallel discovery data store infrastructure has multiple benefits as it allows such to channel the actual search queries against the raw data set in much more funneled manner instead of being widespread across the entire data sets. Such focused search queries and data separation are far more performant and requires less compute and memory footprint.

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

In this paper, we propose a robust Nash strategy for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) via static output feedback (SOF). After establishing the extended bounded real lemma for UMJDSS, the conditions for the existence of a robust Nash strategy set are determined by means of cross coupled stochastic matrix inequalities (CCSMIs). In order to solve the SOF problem, an heuristic algorithm is developed based on the algebraic equations and the linear matrix inequalities (LMIs). In particular, it is shown that robust convergence is guaranteed under a new convergence condition. Finally, a practical numerical example based on the congestion control for active queue management is provided to demonstrate the reliability and usefulness of the proposed design scheme.

The exponential growth rate of malware causes significant security concern in this digital era to computer users, private and government organizations. Traditional malware detection methods employ static and dynamic analysis, which are ineffective in identifying unknown malware. Malware authors develop new malware by using polymorphic and evasion techniques on existing malware and escape detection. Newly arriving malware are variants of existing malware and their patterns can be analyzed using the vision-based method. Malware patterns are visualized as images and their features are characterized. The alternative generation of class vectors and feature vectors using ensemble forests in multiple sequential layers is performed for classifying malware. This paper proposes a hybrid stacked multilayered ensembling approach which is robust and efficient than deep learning models. The proposed model outperforms the machine learning and deep learning models with an accuracy of 98.91%. The proposed system works well for small-scale and large-scale data since its adaptive nature of setting parameters (number of sequential levels) automatically. It is computationally efficient in terms of resources and time. The method uses very fewer hyper-parameters compared to deep neural networks.

The most common defenses against malware threats involves the use of signatures derived from instances of known malware. However, the constant evolution of the malware threat landscape necessitates defense against unknown malware, making a signature catalog of known threats insufficient to prevent zero-day vulnerabilities from being exploited. Recent research has applied machine learning approaches to identify malware through artifacts of malicious activity as observed through dynamic behavioral analysis. We have seen that these approaches mimic common malware defenses by simply offering a method of detecting known malware. We contribute a new method of identifying software as malicious or benign through analysis of the frequency of Windows API system function calls. We show that this is a powerful technique for malware detection because it generates learning models which understand the difference between malicious and benign software, rather than producing a malware signature classifier. We contribute a method of systematically comparing machine learning models against different datasets to determine their efficacy in accurately distinguishing the difference between malicious and benign software.

Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.

Live Linux distribution devices can hold Linux operating system for portability. Using such devices and distributions, one can access system or critical files, which otherwise cannot be accessed by guest or any unauthorized user. Events like file leakage before the official announcement. These announcements can vary from mobile companies to software industries. Damages caused by such vulnerabilities can be data theft, data tampering, or permanent deletion of certain records. This study uncovers the security flaws of operating system against live device attacks. For this study, we used live devices with different Linux distributions. Target operating systems are exposed to live device attacks and their behavior is recorded against different Linux distribution. This study also compares the robustness level of different operating system against such attacks.

With rapid growth of network size and complexity, network defenders are facing more challenges in protecting networked computers and other devices from acute attacks. Traffic visualization is an essential element in an anomaly detection system for visual observations and detection of distributed DoS attacks. This paper presents an interactive visualization system called TVis, proposed to detect both low-rate and highrate DDoS attacks using Heron's triangle-area mapping. TVis allows network defenders to identify and investigate anomalies in internal and external network traffic at both online and offline modes. We model the network traffic as an undirected graph and compute triangle-area map based on incidences at each vertex for each 5 seconds time window. The system triggers an alarm iff the system finds an area of the mapped triangle beyond the dynamic threshold. TVis performs well for both low-rate and high-rate DDoS detection in comparison to its competitors.

Nowadays, computers are used everywhere to carry out daily routine tasks. The input devices i.e. keyboard or mouse are used to feed input to computers. The surveillance of input devices is much important as monitoring the users logging activity. A keylogger also referred as a keystroke logger, is a software or hardware device which monitors every keystroke typed by a user. Keylogger runs in the background that user cannot identify its presence. It can be used as monitoring software for parents to keep an eye on children activity on computers and for the owner to monitor their employees. A keylogger (which can be either spyware or software) is a kind of surveillance software that has the ability to store every keystroke in a log file. It is very dangerous for those systems which use their system for daily transaction purpose i.e. Online Banking Systems. A keylogger is a tool, made to save all the keystroke generated through the machine which sanctions hackers to steal sensitive information without user's intention. Privileged also relies on the access for both implementation and placement by Kernel keylogger, the entire message transmitted from the keyboard drivers, while the programmer simply relies on kernel level facilities that interrupt. This certainly needs a large power and expertise for real and error-free execution. However, it has been observed that 90% of the current keyloggers are running in userspace so they do not need any permission for execution. Our aim is focused on detecting userspace keylogger. Our intention is to forbid userspace keylogger from stealing confidential data and information. For this purpose, we use a strategy which is clearly based on detection manner techniques for userspace keyloggers, an essential category of malware packages. We intend to achieve this goal by matching I/O of all processes with some simulated activity of the user, and we assert detection in case the two are highly correlated. The rationale behind this is that the more powerful stream of keystrokes, the more I/O operations are required by the keylogger to log the keystrokes into the file.

Researchers are trying to find new ways of finding and pointing out Cybersecurity vulnerabilities by using innovative metrics. New theoretical proposals need to be tested in a real environment, using Cybersecurity tools applications that can validate the applicability of those in real life. This paper presents an experimental flexible environment, which can be used for the validation of several theoretical claims based on an “easy to use” architecture implemented in a cloud environment. The framework provides a much shorter time setup in the real world as well as a much better understanding based on log analysis provided by MS Azure. As a proof of concept, we have tested three different claims and provided proves of results such as screenshots and log samples.

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as × 6) can be achieved by parallelizing off-the-shelf stream learning approaches.

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.

Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, interpolation is needed and the upper and lower envelopes will be drawn. However, these interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite interpolation reconstruction method is used to replace the surface interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.

The era of information technology has, unfortunately, contributed to the tremendous rise in the number of criminal activities. However, digital artifacts can be utilized in convicting cybercriminal and exposing their activities. The digital forensics science concerns about all aspects related to cybercrimes. It seeks digital evidence by following standard methodologies to be admitted in court rooms. This paper concerns about memory forensics for the unique artifacts it holds. Memory contains information about the current state of systems and applications. Moreover, an application's data explains how a criminal has been interacting the application just before the memory is acquired. Memory forensics at the application level is currently random and cumbersome. Targeting specific applications is what forensic researchers and practitioner are currently striving to provide. This paper suggests a general solution to investigate any application. Our solution aims to utilize an application's data structures and variables' information in the investigation process. This is because an application's data has to be stored and retrieved in the means of variables. Data structures and variables' information can be generated by compilers for debugging purposes. We show that an application's information is a valuable resource to the investigator.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.