APT Attack Inference and Multidimensional Visual Representation

Advanced Persistent Threat (APT) attack detection and monitoring has attracted a lot of attention recently when this type of cyber-attacks is growing in both number and dangerous levels. In this paper, a new APT attack model, which is the combination of three different neural network layers including: Multi-layer Perceptron (MLP), Inference (I), and Graph Convolutional Networks (GCN) is proposed. The new model is named MIG for short. In this model, the MLP layer is in charge of aggregating and extracting properties of the IPs based on flow network in Network traffic, while the Inference layer is responsible for building IP information profiles by grouping and concatenating flow networks generated from the same IP. Finally, the GCN layer is used for analyzing and reconstructing IP features based on the behavior extraction process from IP information records. The APT attacks detection method based on network traffic using this MIG model is new, and has yet been proposed and applied anywhere. The novelty and uniqueness of this method is the combination of many different data mining techniques in order to calculate, extract and represent the relationship and the correlation between APT attack behaviors based on Network traffic. In MIG model, many meaningful anomalous properties and behaviors of APT attacks are synthesized and extracted, which help improve the performance of APT attack detection. The experimental results showed that the proposed method is meaningful in both theory and practice since the MIG model not only improves the ability to correctly detect APT attacks in network traffic but also minimizes false alarms.

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

This paper analyzes the process of Google Aurora attack, summarizes the typical steps of APT attacks. By analyzing the defects on traditional enterprise security architecture, this paper proposes a new security solution with a central analysis and control module. The module adopts a detection mode based on the storage, and it is divided into storage layer, analysis layer and presentation layer.

Advanced Persistent Threat (APT) attacks are a form of malicious, intentionally and clearly targeted attack. This attack technique is growing in both the number of recorded attacks and the extent of its dangers to organizations, businesses and governments. Therefore, the task of detecting and warning APT attacks in the real system is very necessary today. One of the most effective approaches to APT attack detection is to apply machine learning or deep learning to analyze network traffic. There have been a number of studies and recommendations to analyze network traffic into network flows and then combine with some classification or clustering methods to look for signs of APT attacks. In particular, recent studies often apply machine learning algorithms to spot the present of APT attacks based on network flow. In this paper, a new method based on deep learning to detect APT attacks using network flow is proposed. Accordingly, in our research, network traffic is analyzed into IP-based network flows, then the IP information is reconstructed from flow, and finally deep learning models are used to extract features for detecting APT attack IPs from other IPs. Additionally, a combined deep learning model using Bidirectional Long Short-Term Memory (BiLSTM) and Graph Convolutional Networks (GCN) is introduced. The new detection model is evaluated and compared with some traditional machine learning models, i.e. Multi-layer perceptron (MLP) and single GCN models, in the experiments. Experimental results show that BiLSTM-GCN model has the best performance in all evaluation scores. This not only shows that deep learning application on flow network analysis to detect APT attacks is a good decision but also suggests a new direction for network intrusion detection techniques based on deep learning.

The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors’ profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue. It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence. With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors. The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques. Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks. The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art. The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

In recent years, extensive research has been conducted in Advanced Persistent Threat (APT) attack defence. However, most existing defence solutions can only identify and temporarily disrupt cyber attacks, seeking to deny the threat from the intranet, it’s difficult to defence against APT attacks. Attributing the APT organization is an excellent complement to the existing defence solutions, which not only can expose the attacker’s true identity, but also provide evidence to bring the attacker to justice. However, research on attributing APT Organization is still few, poses complex tasks because APT attacks are highly targeted, stealthy, persistent and organized. To answer thie question, we propose a Particle Swarm Optimization Multiclass Support Vector Machine (PSO-MSVM) approach to identify the organization behind complex APT attacks automatically. Firstly, we have collected a large amount of data on the traces of APT attack tools executed in the sandbox, and selected data closely related to APT organizations to construct the feature set. Secondly, based on the strategy of keeping the personal best (pbest) and global best (gbest) particles in the particle swarm algorithm away from the adaptation values generated by the misclassification information as they move, the particle positions are updated frequently to eventually obtain the optimal parameters (i.e., penalty parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> ) and sigma parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> )) for MSVM, thus enabling the MSVM technique to accurately identify APT organizations. The results obtained from the PSO-MSVM approach showed the superiority of this technique in three different measures of accuracy, precision and F1,compared with other six classical methods.

Advanced Persistent Threat (APT) has become one of the most complicated and intractable cyber attack over the last decade. As APT attacks are conducted through series of actions that comprise social engineering, phishing, command and control servers, and remote desktop control, conventional anti-virus mechanisms become insufficient because they were designed to cope with traditional stand-alone malware attacks. Furthermore, data transmission from the compromised network to the APT actors is usually well disguised and embedded in normal transmission, exacerbating the detection of APT attacks to the point that even major anti-virus firms are not sure about the ratio of discovered APT attacks against real attacks. To make things worse, APT actors tend to be well-organized and potentially government-funded groups of hackers and professionals who are capable of developing and maintaining malware specifically made for their own purposes and interpret the stolen data. While most efforts in defending against APT attacks focus on related technologies, this research argues the importance of constructing a holistic understanding by analyzing the behaviors and changes of ATP attacks and actors. This research aims to understand the evolution of technologies and malware on the one hand and the behavioral changes of attacking groups. By doing so, this research is expected to contribute to constructing a clearer roadmap of APT attacks and actors that cyber security providers can use as reference.

Advanced Persistent Threat (APT) attacks have caused serious security threats and financial losses worldwide. Various real-time detection mechanisms that combine context information and provenance graphs have been proposed to defend against APT attacks. However, existing real-time APT detection mechanisms suffer from accuracy and efficiency issues due to inaccurate detection models and the growing size of provenance graphs. To address the accuracy issue, we propose a novel and accurate APT detection model that removes unnecessary phases and focuses on the remaining ones with improved definitions. To address the efficiency issue, we propose a state-based framework in which events are consumed as streams and each entity is represented in an FSA-like structure without storing historic data. Additionally, we reconstruct attack scenarios by storing just one in a thousand events in a database. Finally, we implement our design, called <small>Conan</small>, on Windows and conduct comprehensive experiments under real-world scenarios to show that <small>Conan</small> can accurately and efficiently detect all attacks within our evaluation. The memory usage and CPU efficiency of <small>Conan</small> remain constant over time (1-10 MB of memory and hundreds of times faster than data generation), making <small>Conan</small> a practical design for detecting both known and unknown APT attacks in real-world scenarios.

This article proposes an Advanced Persistent Threat (APT) attack detection method based on traceability graphs, aimed at addressing the complexity and concealment of APT attacks. This method describes system behavior by constructing a traceability graph, optimizing it to reduce redundant information, converting the traceability graph sequence into a feature vector sequence, and using an encoder decoder model to train the GRU (Gate Recurrent Unit) model to extract long-term features of the sequence. Finally, a normal behavior model is established through clustering to detect APT attacks.

In recent years, advanced persistent threat (APT) attacks have become a significant network security threat due to their concealment and persistence. Correlation analysis of APT groups is vital for understanding the global network security landscape and accurately attributing threats. Current studies on threat attribution rely on experts or advanced technology to identify evidence linking attack incidents to known APT groups. However, there is a lack of research focused on automatically discovering potential correlations between APT groups. This paper proposes a method using attack behavior patterns and rough set theory to quantify APT group relevance. It extracts two types of features from threat intelligence: APT attack objects and behavior features. To address the issues of inconsistency and limitations in threat intelligence, this method uses rough set theory to model APT group behavior and designs a link prediction method to infer correlations among APT groups. Experimental results on publicly available APT analysis reports show a correlation precision of 90.90%. The similarity coefficient accurately reflects the correlation strength, validating the method’s efficacy and accuracy.

Smartphones are ubiquitous today, and they contain a large amount of personal and sensitive information. It is, therefore, essential to secure the underlying operating system. Android is the dominant operating system among the smartphone market; therefore, it is critical to uphold the security standards of Android. Android smartphone manufacturers and third-party custom ROM developers modify the operating system heavily to differentiate themselves among the competitors. The modifications done by the Smartphone manufacturers and third-party custom ROM developers posses a threat to the smartphone user’s privacy and make the Android OS vulnerable to advanced persistent threat (APT) attacks. This paper demonstrates that Smartphone manufacturers and third-party custom ROM developers can bypass Android’s security mechanisms and breach the user’s privacy without getting detected by the user by modifying parts of Android OS except for the kernel. In particular, this paper shows methods by which APT attacks can be performed on the Android 10’s Camera subsystem to capture pictures from the camera and upload them to a remote server without the user’s knowledge.

For years governments, organizations and companies have made great efforts to keep hackers, malware, cyber attacks at bay with different degrees of success. On the other hand, cyber criminals and miscreants produced more advanced techniques to compromise Internet infrastructure. Targeted attack or advanced persistent threat (APT) attack is a new challenge and aims to accomplish a specific goal, most often espionage. APTs are presently the biggest threat to governments and organizations. This paper states research questions and propose a novel approach to intrusion detection system processes network traffic and able to detect potential APT attack. This detection of APT attack is based on the correlation between the events which we get as outputs of our detection methods. Each detection method aims to detect one technique used in one of APT attack steps.

Malware homology analysis aims at detecting whether different malicious code originates from the same set of malicious code or is written by the same author or team, and whether it has intrinsic relevance and similarity. At the same time, the homology analysis of malicious code is also an important part of studying the groups behind different APT (Advanced Persistent Threat) attacks. At present, homology identification still relies on manual analysis and security experts' experience in the anti-malware industry. In addition, research on large-scale malicious code automated homology analysis is still insufficient. The method proposed in this paper is to solve the problem of large-scale malicious code homology automatic analysis, and hope to provide auxiliary information for discovering the group behind the APT attack. In this paper, we collected samples of different APT groups from public threat intelligence and proposed a novel approach to classify these samples into different APT groups to further analyze the homology of malware. We combined the CFG (Control Flow Graph) of the malicious code function and the disassembled code of the stripped malware to generate the embedding, i.e., a numeric vector, which formed a function feature database of the APT group, and presented a neural network model used for APT group classification. We have implemented our approach in a prototype system called MCrab. Our extensive evaluation showed that MCrab could produce high accuracy results, with few to no false positives. Our research also showed that deep learning can be successfully applied to malware homology analysis.

The essence of Network attack and Defense confrontation is the Game process between attacker and Defender. In the research of the existing network attack strategy, there is a priori hypothesis defect of equal status between the subjects of the attack and defense game. Threat intelligence cannot be shared between defenders. Game theory is introduced into the process of constructing network defense strategy generation model, and prediction method of attack behavior based on Stackelberg Security Game is proposed. By establishing the network model, the Stackelberg security game strong equilibrium strategy algorithm is used to generate the optimal attack strategy, which not only fully considers the influence of the relationship between the two sides in the attack and defense behavior and shares Information, but also ensures the accuracy of the attack strategy generation. The experimental results show that the proposed model and method are feasible and effective, and can improv the attack effect.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision-making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain (CKC) where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, we demonstrated how effectively the defender can leverage defensive deception techniques while dealing with multi-staged APT attacks in a hypergame in which the imperfect information is reflected based on perceived uncertainty, cost, and expected utilities of both the attacker and defender, the system lifetime (i.e., mean time to security failure), and improved false-positive rates of intrusion detection.