Biblio

In the field of airport passenger security, a new type of security inspection equipment called intelligent passenger security equipment is applied widely, which can significantly improve the efficiency of airport security screening and passenger satisfaction. This paper establishes a security check channel model based on intelligent passenger security check equipment, and studies the factors affecting the efficiency of airport security screening, such as the number of baggage unloading points, baggage loading points, secondary inspection points, etc. A simulation model of security check channel is established based on data from existing intelligent passenger security check equipment and data collected from Beijing Daxing Airport. Equipment utilization and queue length data is obtained by running the simulation model. According to the data, the bottleneck is that the manual inspection process takes too long, and the utilization rate of the baggage unloading point is too low. For the bottleneck link, an optimization scheme is proposed. With more manual check points and secondary inspection points and less baggage unloading points, the efficiency of airport security screening significantly increases by running simulation model. Based on the optimized model, the effect of baggage unloading point and baggage loading point on efficiency is further studied. The optimal parameter configuration scheme under the expected efficiency is obtained. This research can assist engineers to find appropriate equipment configuration quickly and instruct the airport to optimize the arrangement of security staff, which can effectively improve the efficiency of airport security screening and reduce the operating costs of airport.

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.

The growing number of cybersecurity incidents and the always increasing complexity of cybersecurity attacks is forcing the industry and the research community to develop robust and effective methods to detect and respond to network attacks. Many tools are either built upon a large number of rules and signatures which only large third-party vendors can afford to create and maintain, or are based on complex artificial intelligence engines which, in most cases, still require personalization and fine-tuning using costly service contracts offered by the vendors.This paper introduces an open-source network traffic monitoring system based on the concept of cyberscore, a numerical value that represents how a network activity is considered relevant for spotting cybersecurity-related events. We describe how this technique has been applied in real-life networks and present the result of this evaluation.

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.

Network attacks become more complicated with the improvement of technology. Traditional statistical methods may be insufficient in detecting constantly evolving network attack. For this reason, the usage of payload-based deep packet inspection methods is very significant in detecting attack flows before they damage the system. In the proposed method, features are extracted from the byte distributions in the payload and these features are provided to characterize the flows more deeply by using N-Gram analysis methods. The proposed procedure has been tested on IDS 2012 and 2017 datasets, which are widely used in the literature.

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.

Attack detection in enterprise networks is increasingly faced with large data volumes, in part high data bursts, and heavily fluctuating data flows that often cause arbitrary discarding of data packets in overload situations which can be used by attackers to hide attack activities. Attack detection systems usually configure a comprehensive set of signatures for known vulnerabilities in different operating systems, protocols, and applications. Many of these signatures, however, are not relevant in each context, since certain vulnerabilities have already been eliminated, or the vulnerable applications or operating system versions, respectively, are not installed on the involved systems. In this paper, we present an approach for clustering data flows to assign them to dedicated analysis units that contain only signature sets relevant for the analysis of these flows. We discuss the performance of this clustering and show how it can be used in practice to improve the efficiency of an analysis pipeline.

The Internet has evolved to the point that gigabytes and even terabytes of data are generated and processed on a daily basis. Such a stream of data is characterised by high volume, velocity and variety and is referred to as Big Data. Traditional data processing tools can no longer be used to process big data, because they were not designed to handle such a massive amount of data. This problem concerns also cyber security, where tools like intrusion detection systems employ classification algorithms to analyse the network traffic. Achieving a high accuracy attack detection becomes harder when the amount of data increases and the algorithms must be efficient enough to keep up with the throughput of a huge data stream. Due to the challenges posed by a big data environment, some monitoring systems have already shifted from deep packet inspection to flow-level inspection. The goal of this paper is to evaluate the applicability of an existing intrusion detection technique that performs deep packet inspection in a big data setting. We have conducted several experiments with Apache Spark to assess the performance of the technique when classifying anomalous packets, showing that it benefits from the use of Spark.

The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.

Static analysis tools generate a large number of alarms that require manual inspection. In prior work, repositioning of alarms is proposed to (1) merge multiple similar alarms together and replace them by a fewer alarms, and (2) report alarms as close as possible to the causes for their generation. The premise is that the proposed merging and repositioning of alarms will reduce the manual inspection effort. To evaluate the premise, this paper presents an empirical study with 249 developers on the proposed merging and repositioning of static alarms. The study is conducted using static analysis alarms generated on \$C\$ programs, where the alarms are representative of the merging vs. non-merging and repositioning vs. non-repositioning situations in real-life code. Developers were asked to manually inspect and determine whether assertions added corresponding to alarms in \$C\$ code hold. Additionally, two spatial cognitive tests are also done to determine relationship in performance. The empirical evaluation results indicate that, in contrast to expectations, there was no evidence that merging and repositioning of alarms reduces manual inspection effort or improves the inspection accuracy (at times a negative impact was found). Results on cognitive abilities correlated with comprehension and alarm inspection accuracy.

Software based scan diagnosis is the de facto method for debugging logic scan failures. Physical analysis success rate is high on dies diagnosed with maximum score, one symptom, one suspect and shorter net. This poses a limitation on maximum utilization of scan diagnosis data for PFA. There have been several attempts to combine dynamic fault isolation techniques with scan diagnosis results to enhance the utilization and success rate. However, it is not a feasible approach for foundry due to limited product design and test knowledge and hardware requirements such as probe card and tester. Suitable for a foundry, an enhanced diagnosis-driven analysis scheme was proposed in [1] that classifies the failures as frontend-of-line (FEOL) and backend-of-line (BEOL) improving the die selection process for PFA. In this paper, static NIR PEM and defect prediction approach are applied on dies that are already classified as FEOL and BEOL failures yet considered unsuitable for PFA due to low score, multiple symptoms, and suspects. Successful case studies are highlighted to showcase the effectiveness of using static NIR PEM as the next level screening process to further maximize the scan diagnosis data utilization.

Design of smart risk assessment system for the agricultural products and the food safety inspection based on multivariate data analysis is studied in this paper. The designed quality traceability system also requires the collaboration and cooperation of various companies in the supply chain, and a unified database, including agricultural product identification system, code system and security status system, is required to record in detail the trajectory and status of agricultural products in the logistics chain. For the improvement, the multivariate data analysis is combined. Hadoop cannot be used on hardware with high price and high reliability. Even for groups with high probability of the problems, HDFS will continue to use when facing problems, and at the same time. Hence, the core model of HDFS is applied into the system. In the verification part, the analytic performance is simulated.

In order to ensure the security of information assets and standardize the risk assessment and inspection workflow of information assets. This paper has designed and developed a risk assessment system for information and communication equipment with simple operation, offline assessment, and diversified external interfaces. The process of risk assessment can be realized, which effectively improves the efficiency of risk assessment.

Fault injection is a major threat to embedded system security since it can lead to modified control flows and leakage of critical security parameters, such as secret keys. However, injecting physical faults into devices is cumbersome and difficult since it requires a lot of preparation and manual inspection of the assembly instructions. Furthermore, a single fault injection method cannot cover all possible fault types. Simulating fault injection in comparison, is, in general, less costly, more time-efficient, and can cover a large amount of possible fault combinations. Hence, many different fault injection tools have been developed for this purpose. However, previous tools have several drawbacks since they target only individual architectures or cover merely a limited amount of the possible fault types for only specific memory types. In this paper, we present ARCHIE, a QEMU-based architecture-independent fault evaluation tool, that is able to simulate transient and permanent instruction and data faults in RAM, flash, and processor registers. ARCHIE supports dynamic code analysis and parallelized execution. It makes use of the Tiny Code Generator (TCG) plugin, which we extended with our fault plugin to enable read and write operations from and to guest memory. We demonstrate ARCHIE’s capabilities through automatic binary analysis of two exemplary applications, TinyAES and a secure bootloader, and validate our tool’s results in a laser fault injection experiment. We show that ARCHIE can be run both on a server with extensive resources and on a common laptop. ARCHIE can be applied to a wide range of use cases for analyzing and enhancing open source and proprietary firmware in white, grey, or black box tests.

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.

Bitcoin and other digital cryptocurrencies have de-veloped rapidly in recent years. To reduce hardware and power costs, many criminals use the botnet to infect other hosts to mine cryptocurrency for themselves, which has led to the proliferation of mining botnets and is referred to as cryptojacking. At present, the mechanisms specific to cryptojacking detection include host-based, Deep Packet Inspection (DPI) based, and dynamic network characteristics based. Host-based detection requires detection installation and running at each host, and the other two are heavyweight. Besides, DPI-based detection is a breach of privacy and loses efficacy if encountering encrypted traffic. This paper de-signs a lightweight cryptojacking traffic detection method based on network behavior features for an ISP, without referring to the payload of network traffic. We set up an environment to collect cryptojacking traffic and conduct a cryptojacking traffic study to obtain its discriminative network traffic features extracted from only the first four packets in a flow. Our experimental study suggests that the machine learning classifier, random forest, based on the extracted discriminative network traffic features can accurately and efficiently detect cryptojacking traffic.

Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline – grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

X-ray baggage security screening is a demanding task for aviation and rail transit security; automatic prohibited item detection in X-ray baggage images can help reduce the work of inspectors. However, as many items are placed too close to each other in the baggages, it is difficult to fully trust the detection results of intelligent prohibited item detection algorithms. In this paper, a human-in-the-loop baggage inspection framework is proposed. The proposed framework utilizes the deep-learning-based algorithm for prohibited item detection to find suspicious items in X-ray baggage images, and select manual examination when the detection algorithm cannot determine whether the baggage is dangerous or safe. The advantages of proposed inspection process include: online to capture new sample images for training incrementally prohibited item detection model, and augmented prohibited item detection intelligence with human-computer collaboration. The preliminary experimental results show, human-in-the-loop process by combining cognitive capabilities of human inspector with the intelligent algorithms capabilities, can greatly improve the efficiency of in-baggage security screening.

The npm ecosystem has the largest number of third-party packages for making node.js-based web apps. Due to its free and open nature, it can raise diversity of security concerns. Adversaries can take advantage of existing software APIs included in node.js web apps for achieving their own malicious targets. More specifically, attackers may inject malicious data into its client requests and then submit them to a victim node.js server. It then may manipulate program states to reuse sensitive APIs as gadgets required in the node.js web app executed on the victim server. Once such sensitive APIs can be successfully accessed, it may indirectly raise security threats such as code injection attacks, software-layer DoS attacks, private data leaks, etc. For example, when the sensitive APIs are implemented as pattern matching operations and are called with hard-to-match input string submitted by clients, it may launch application-level DoS attacks.In this paper, we would like to introduce software reuse exploits through reusing packages available in node.js web apps for posing security threats to servers. In addition, we propose an approach based on data flow analysis to detect vulnerable npm packages that can be exposed to such exploits. To evaluate its effectiveness, we collected a dataset of 15,000 modules from the ecosystem to conduct the experiments. As a result, it discovered out 192 vulnerable packages. By manual analysis, we identified 156 true positives of 192 that can be exposed to code reuse exploits for remotely causing software-layer DoS attacks with 128 modules of 156, for code injection with 18 modules, and for private data leaks including 10 vulnerable ones.

Everyday millions of people use Internet for various purposes including information access, communication, business, education, entertainment and more. As a result, huge amount of information is exchanged between billions of connected devices. This information can be encapsulated in different types of data packets. This information is also referred to as network traffic. The traffic analysis is a challenging task when the traffic is encrypted and the contents are not readable. So complex algorithms required to deduce the information and form patterns for traffic analysis. Many of currently available techniques rely on application specific attribute analysis, deep packet inspection (DPI) or content-based analysis that become ineffective on encrypted traffic. The article will focused on analysis techniques for encrypted traffic that are adaptive to address the evolving nature and increasing volume of network traffic. The proposed solution solution is less dependent on application and protocol specific parameters so that it can adapt to new types of applications and protocols. Our results shows that processing required for traffic analysis need to be in acceptable limits to ensure applicability in real-time applications without compromising performance.

Having a clear insight on the protocols carrying traffic is crucial for network applications. Deep Packet Inspection (DPI) has been a key technique to provide visibility into traffic. DPI has proven effective in various scenarios, and indeed several open source DPI solutions are maintained by the community. Yet, these solutions provide different classifications, and it is hard to establish a common ground truth. Independent works approaching the question of the quality of DPI are already aged and rely on limited datasets. Here, we test if open source DPI solutions can provide useful information in practical scenarios, e.g., supporting security applications. We provide an evaluation of the performance of four open-source DPI solutions, namely nDPI, Libprotoident, Tstat and Zeek. We use datasets covering various traffic scenarios, including operational networks, IoT scenarios and malware. As no ground truth is available, we study the consistency of classification across the solutions, investigating rootcauses of conflicts. Important for on-line security applications, we check whether DPI solutions provide reliable classification with a limited number of packets per flow. All in all, we confirm that DPI solutions still perform satisfactorily for well-known protocols. They however struggle with some P2P traffic and security scenarios (e.g., with malware traffic). All tested solutions reach a final classification after observing few packets with payload, showing adequacy for on-line applications.

Advanced persistent threats (APT’s) are stealthy threat actors with the skills to gain covert control of the computer network for an extended period of time. They are the highest cyber attack risk factor for large companies and states. A successful attack via an APT can cost millions of dollars, can disrupt civil life and has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. Attacks of APT’s are executed in several stages as pointed out in the Lockheed Martin cyber kill chain (CKC). Each of these APT stages can potentially be identified as patterns in network traffic. Using the "APT-2020" dataset, that compiles the characteristics and stages of an APT, we carried out experiments on the detection of anomalous traffic for all APT stages. We compare several artificial intelligence models, like a stacked auto encoder, a recurrent neural network and a one class state vector machine and show significant improvements on detection in the data exfiltration stage. This dataset is the first to have a data exfiltration stage included to experiment on. According to APT-2020’s authors current models have the biggest challenge specific to this stage. We introduce a method to successfully detect data exfiltration by analyzing the payload of the network traffic flow. This flow based deep packet inspection approach improves detection compared to other state of the art methods.

Traffic classification is fundamental to network operators to manage the network better. L7 classification and Deep Packet Inspection (DPI) using regular expressions are vital components to provide application-aware traffic classification. Nevertheless, there are open challenges yet, such as programmability and performance combined with security. In this paper, we introduce eBPFlow, a fast application layer packet classifier in hardware. eBPFlow allows packet classification with DPI on packet headers and payloads in runtime. It enables programming of regular expressions (RegEx) and security protocols using eBPF (extended Berkeley Packet Filter). We built eBPFlow on NetFPGA SUME 40 Gbps and created several application classifiers. The tests were performed in a physical testbed. Our results show that eBPFlow supports packet classification on the application layer with line rate. It only consumes 22 W.

In recent years we have observed an escalation of cybersecurity attacks, which are becoming more sophisticated and harder to detect as they use more advanced evasion techniques and encrypted communications. The research community has often proposed the use of machine learning techniques to overcome the limitations of traditional cybersecurity approaches based on rules and signatures, which are hard to maintain, require constant updates, and do not solve the problems of zero-day attacks. Unfortunately, machine learning is not the holy grail of cybersecurity: machine learning-based techniques are hard to develop due to the lack of annotated data, are often computationally intensive, they can be target of hard to detect adversarial attacks, and more importantly are often not able to provide explanations for the predicted outcomes. In this paper, we describe a novel approach to cybersecurity detection leveraging on the concept of security score. Our approach demonstrates that extracting signals via deep packet inspections paves the way for efficient detection using traffic analysis. This work has been validated against various traffic datasets containing network attacks, showing that it can effectively detect network threats without the complexity of machine learning-based solutions.

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a mission team, has been widely used for emergent scenarios such as criminal tracking and victim assistance. These scenarios are related to human safety and require a robot team to quickly transit from the current undergoing task into the new emergent task. This sudden mission change brings difficulty in robot motion adjustment and increases the risk of performance degradation of the swarm. Trust in human-human collaboration reflects a general expectation of the collaboration; based on the trust humans mutually adjust their behaviors for better teamwork. Inspired by this, in this research, a trust-aware reflective control (Trust-R), was developed for a robot swarm to understand the collaborative mission and calibrate its motions accordingly for better emergency response. Typical emergent tasks “transit between area inspection tasks”, “response to emergent target - car accident” in social security with eight fault-related situations were designed to simulate robot deployments. A human user study with 50 volunteers was conducted to model trust and assess swarm performance. Trust-R's effectiveness in supporting a robot team for emergency response was validated by improved task performance and increased trust scores.