Advanced Persistent Threat Attacks Research Articles

Many phish in the : A coexisting-choice-criteria model of security behavior.

Normative decision theory proves inadequate for modeling human responses to the social-engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social-engineering attack vectors which operate through emotions and peripheral-route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by . Thus, the proposed model reduces to conventional Expected Utility theory when , while Dual-Process (thinking fast vs. thinking slow) decision making corresponds to a model with . We consider a more general case with , which necessitates careful consideration of how, for a particular choice-task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral-route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security-breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the "stepping-stone" penetration pattern of APT attacks, which has confounded modeling approaches based on normativerationality.

Multi-receiver signcryption scheme with multiple key generation centers through public channel in edge computing

The emerging edge computing technology for the Internet of Things has been playing an important role in our daily life. It is promising to utilize a multi-receiver signcryption scheme to protect the transmission data when an edge device broadcasts its sensing data to many different end devices at a time. There are several things to consider when we design a signcryption scheme. First existing schemes need to maintain a secure channel to generate the user private key, which may increase economic costs. Second the system private key of those schemes is kept secret by a single key generation center (KGC), and the single point of failure of KGC may compromise the whole system. For this, we propose a multi-receiver multi-message signcryption scheme without the secure channel. Firstly the scheme allows KGC to send secrets through the public channel, which reduces maintenance costs. Secondly, to eliminate the single point of failure, the scheme utilizes multiple KGCs to manage the system private key, and updates the secret of each KGC periodically to resist advanced persistent threat attacks. We demonstrate that the proposed scheme can achieve expected security properties. Performance analysis shows that it is with shorter ciphertext length and higher efficiency.

Comparison of the Host Based Intrusion Detection Systems and Network Based Intrusion Detection Systems

Recently, Advanced Persistent Threat (APT) has emerged as sophisticated and tailor-made attacks. APTs pose threats mainly targeting institutions such as military, defense and security infrastructure, high profile companies and governments etc. Particularly as a counter measure for APT attacks done by hactivists and cyber warriors and cyber terrorists over government institutions and e-government applications intrusion detection mechanisms are of crucial importance in effective defense. In this study, Intrusion detection and prevention systems have been studied in detail after being referred to the tasks and abilities of the intrusion detection systems that are at the core of the computer security technology presented today in order to meet the increasing need for information and network security. This paper’s aim is the specifying the differences between Host Based Intrusion Detection Systems (HIDS) and Network Based Intrusion Detection Systems (NIDS) and compares the tools which are using HIDS and NIDS. It is asserted that in order to have a better assurance for APT attacks there should be set up a Hybrid IDS approach covering both networks and hosts using both signature and behavioral detection mechanism based on machine learning.

Optimization of APT attack detection based on a model combining ATTENTION and deep learning

Nowadays, early detecting and warning Advanced Persistent Threat (APT) attacks is a major challenge for intrusion monitoring and prevention systems. Current studies and proposals for APT attack detection often focus on combining machine-learning techniques and APT malware behavior analysis techniques based on network traffic. To improve the efficiency of APT attack detection, this paper proposes a new approach based on a combination of deep learning networks and ATTENTION networks. The proposed process for APT attack detection in this study is as follows: Firstly, all data of network traffic is pre-processed, and analyzed by the CNN-LSTM deep learning network, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Then, instead of being used directly for classification, this data is analyzed and evaluated by the ATTENTION network. Finally, the output data of the ATTENTION network is classified to identify APT attacks. The optimization proposal for detecting APT attacks in this study is a novel proposal. It hasn’t been proposed and applied by any research. Some scenarios for comparing and evaluating the method proposed in this study with other approaches (implemented in section 4.4) show the superior effectiveness of our proposed approach. The results prove that the proposed method not only has scientific significance but also has practical significance because the model combining deep learning with ATTENTION network has helped improve the efficiency of analyzing and detecting APT malware based on network traffic.

Zero Trust and Advanced Persistent Threats: Who Will Win the War?

Advanced Persistent Threats (APTs) are state-sponsored actors who break into computer networks for political or industrial espionage. Because of the nature of cyberspace and ever-changing sophisticated attack techniques, it is challenging to prevent and detect APT attacks. 2020 United States Federal Government data breach once again showed how difficult to protect networks from targeted attacks. Among many other solutions and techniques, zero trust is a promising security architecture that might effectively prevent the intrusion attempts of APT actors. In the zero trust model, no process insider or outside the network is trusted by default. Zero trust is also called perimeterless security to indicate that it changes the focus from network devices to assets. All processes are required to verify themselves to access the resources. In this paper, we focused on APT prevention. We sought an answer to the question: "could the 2020 United States Federal Government data breach have been prevented if the attacked networks used zero trust architecture?" To answer this question, we used MITRE's ATT&CK® framework to extract how the APT29 threat group techniques could be mitigated to prevent initial access to federal networks. Secondly, we listed basic constructs of the zero trust model using NIST Special Publication 800-207 and several other academic and industry resources. Finally, we analyzed how zero trust can prevent malicious APT activities. We found that zero trust has a strong potential of preventing APT attacks or mitigating them significantly. We also suggested that vulnerability scanning, application developer guidance, and training should not be neglected in zero trust implementations as they are not explicitly or strongly mentioned in NIST SP 800-207 and are among the mostly referred controls in academic and industry publications.

Foureye: Defensive Deception Against Advanced Persistent Threats via Hypergame Theory

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.

An Intelligent System to Detect Advanced Persistent Threats in Industrial Internet of Things (I-IoT)

The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain.

Orchestration of APT malware evasive manoeuvers employed for eluding anti-virus and sandbox defense

The modern day cyber attacks are highly targeted and incorporate advanced tactics, techniques and procedures for greater stealth, impact and success. These attacks are also known as Advanced Persistent Threats(APT) because of their evasive and stealth nature along with longer foothold on the victim’s digital infrastructure. The malware involved in APT attacks are sophisticated and developed with the intention of sabotaging the victim’s digital infrastructure or performing espionage. They are capable of targeting multiple operating environments starting from desktop and server operating systems (Windows, Linux and MacOS), Mobile platforms (Android, iOS), Embedded platforms (IoT Devices), to Industrial control systems (ICS/SCADA Devices). The evolution of evasive tactics and techniques employed in such advanced malware leads to extensive research efforts to develop mechanisms that can counter these evasion techniques. The research primarily aims to demonstrate that evasive manoeuvers are currently over-weighing the security countermeasures deployed by the prevalent security solutions. This paper will first explain the evasion mechanism in a systematic manner employed in modern APT malware and aims to implement a novel Evasive Manoeuvers Re-Engineering Framework(EMRF).EMRF aims to establish and demonstrate combinations of evasive manoeuvers with much known APT malware samples to elude security solutions. The payload variants, i.e., executable, dynamic link library, and shell-code, were experimented through a research-based framework EMRF to demonstrate 36% to 96% of evasive behavior countering the majority of defender engines. The EMRF system with its dynamic user defined evasion manoeuvers is able to transform non-zero-day payloads more potent by evading majority of the modern security solutions. This research clearly demonstrates the attacker’s ability to deliver non-zero-day payloads easily rather than investing resources and time in discovering zero-day exploits and developing zero-day payloads. This important observation can potentially disrupt the Advanced Persistent Threat Defenses incorporated in modern day security solution where focus is mainly on to detect zero-day payloads and exploits. Exhibiting the threat landscape poised due to APT, the paper utilizes a dataset of 4403 APT malware samples to extract and orchestrate the prevalence of evasive manoeuvers like stealth, covert communication, and anti-analysis mechanisms. This paper will contribute towards advanced malware analysis as an avenue to analyzing intrusion, evasion, and deception to prevent detection and verification, an association of responsibility, and determination of intent.

An Attack Simulation and Evidence Chains Generation Model for Critical Information Infrastructures

Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they operate and further explore the technical aspects of cyber-attacks, e.g., attack’s course, vulnerabilities exploitability, attacker’s behavior, and location. Several research papers address vulnerability chain discovery techniques. Nevertheless, most of them do not focus on developing attack graphs based on incident analysis. This paper proposes an attack simulation and evidence chains generation model which computes all possible attack paths associated with specific, confirmed security events. The model considers various attack patterns through simulation experiments to estimate how an attacker has moved inside an organization to perform an intrusion. It analyzes artifacts, e.g., Indicators of Compomise (IoCs), and any other incident-related information from various sources, e.g., log files, which are evidence of cyber-attacks on a system or network.

Advanced Persistent Threat Attack Detection using Clustering Algorithms

Advanced Persistent Threat (APT) attack has become one of the most complex attacks. It targets sensitive information. Many cybersecurity systems have been developed to detect the APT attack from network data traffic and request. However, they still need to be improved to identify this attack effectively due to its complexity and slow move. It gets access to the organizations either from an active directory or by gaining remote access, or even by targeting the Domain Name Server (DNS). Nowadays, many machine learning (ML) techniques have been implemented to detect APT attack by using the tools in the market. However, still, there are some limitations in terms of accuracy, efficiency, and effectiveness, especially the lack of labeled data to train ML methods. This paper proposes a framework to detect APT attacks using the most applicable clustering algorithms, such as the APRIORI, K-means, and Hunt’s algorithm. To evaluate and compare the performance of the proposed framework, several experiments are conducted on a public dataset. The experimental results showed that the Support Vector Machine with Radial Basis Function (SVM-RBF) achieves the highest accuracy rate, reaching about 99.2%. This accurate result confirms the effectiveness of the developed framework for detecting attacks from network data traffic.

CSKG4APT: A Cybersecurity Knowledge Graph for Advanced Persistent Threat Organization Attribution

Open-source cyber threat intelligence (OSCTI) is becoming more influential in obtaining current network security information. Most studies on cyber threat intelligence (CTI) focus on automating the extraction of threat entities from public sources that describe attack events. The cybersecurity knowledge graph aims to change the expression of threat knowledge so that security researchers can accurately and efficiently obtain various types of threat information for preliminary intelligent decisions. The attribution technology can not only assist security analysts in detecting advanced persistent threats, but can also identify the same threat from different attack events. Therefore, it is important to trace the attack threat actor. In this study, we used the knowledge graph technology, considered the latest research on cyber threat attack attribution, and thoroughly examined key related technologies and theories in the process of constructing and applying the advanced persistent threat (APT) knowledge graph from OSCTI. We designed a cybersecurity platform named CSKG4APT based on a knowledge graph. Inspired by the theory of ontology, we constructed CSKG4APT as an APT knowledge graph model based on real APT attack scenarios. We then designed an APT threat knowledge extraction algorithm for completing and updating the knowledge graph using deep learning and expert knowledge. Finally, we proposed a practical APT attack attribution method with attribution and countermeasures. CSKG4APT is not a passive defense method in traditional network confrontation but one that integrates a large amount of fragmented intelligence and can actively adjust its defense strategy. It lays the foundation for further dominance in network attack and defense.

Blockchain-Enabled Intrusion Detection and Prevention System of APTs Within Zero Trust Architecture

In a world where organisations are embracing new IT working models such as Bring Your Own Device (BYOD) and remote working, the traditional mindset of defending the network perimeter is no longer sufficient. Zero Trust Architecture (ZTA) has recently emerged as a new security model in which the breach mindset dominates the threat model. By default, the ZTA considers any endpoint (i.e., device), user, or application to be untrusted until proven otherwise. Nonetheless, once proven by the endpoint, using Advanced Persistent Threats (APT), attackers can still take over an authenticated and authorised session via that endpoint. Therefore, they can perform several user/device centric malicious activities in addition to lateral movement rendering the endpoint as the Achilles heel of ZTA. To effectively deter APT attack capabilities on the endpoints, this work proposes a Blockchain-enabled Intrusion Detection and Prevention System (BIDPS) that augments ZTA onto endpoints. The BIDPS aims to achieve two core outcomes: first, detect and prevent attackers’ techniques and tactics as per MITRE’s ATT&CK enterprise matrix earlier than the lateral movement stage, and secondly, strip trust out of the endpoint itself and place it on-chain, thus creating an immutable system of explicit trust. To evaluate the effectiveness of the BIDPS, a testbed was built where techniques of over ten APTs attacks were launched against the endpoint. BIDPS has proven a high rate of success defending against the launched attacks owing to its Blockchain’s immutability, fortifying the detection/prevention processes.

Threat led advanced persistent threat penetration test

Cyber security attacks have been on the rise in recent years. One of the most destructive attacks are known as advanced persistent threat (APT) attacks which can inflict massive damages to a network. A common approach of testing the security of an IT infrastructure is a penetration test (PT), which identifies security flaws in a system. However, this approach covers only the present vulnerabilities in a system without minding any new potential ones that could harm such systems. The goal in this research paper is to implement an enhanced PT approach called threat led APT PT to test the security of a target network against present vulnerabilities. The enhanced PT approach presented in this research work resulted in significantly enhancing the security of a network up to 28.5%. The proposed PT approach is compared to other custom PT approaches, which failed to achieve the same results as the proposed approach.

Honeypot Detection Strategy Against Advanced Persistent Threats in Industrial Internet of Things: A Prospect Theoretic Game

Software-defined networking (SDN) has become a promising trend for managing the Industrial Internet of Things (IIoT) devices. As the core of sensitive data storage and business interaction, the SDN is vulnerable to advanced persistent threats (APTs) attacks, while honeypots have shown great promise against APT attacks. In this article, we propose a new SDN-based dynamic bounded rational honeypot-APT game model in IIoT. Specifically, the defender maximizes the utility by chossing the period strategy of honeypot collecting and analyzing the data, while the attacker maximizes utility by choosing the period strategy of its latency and attack. To describe the bounded rationality, we model the simultaneous dynamic attack and defense process through the prospect theory, in which the Prelec function and the value function are both introduced. Experiment results show that bounded rationality affects strategy selection and reduces defender and attacker’s utilities. Furthermore, our strategy outperforms the existing work in defensive performance.

An Effective Reconstruction Method of the APT Attack Based on Hidden Markov Model

Advanced Persistent Threat (APT) is a multi-stage and multi-step attack process. The reconstruction of the APT attack scene can start with discrete stage attack detection. However, due to the strong characteristic of concealment of APT attacks, some discrete events in the attack scenarios may not be detected. Therefore, to reconstruct the APT attack scene, we need to mine the hidden attack events according to the APT attack target and the detected discrete attack events, describe the action sequence according to the time sequence or the conditions reached by the attack, and finally reconstruct the attack path. In this paper, we depend on the EP-IKC attack cooperation model, we take the total target of APT attack as the pyramid vertex, and the alerted network entities and potential attacked entities related to the vertex as the facet nodes, this paper introduces the hidden Markov model (HMM), and uses the methods of data association and advanced probability theory to mine the hidden APT attack stages, Finally, the detection of APT attack process and the reconstruction of attack scene are realized.

Toward Identifying APT Malware through API System Calls

Self-developed malware was usually used by advanced persistent threat (APT) attackers to launch APT attacks. Therefore, we can enhance the understanding and cognition of APT attacks by comprehending the behavior of APT malware. Unfortunately, the current research cannot effectively explain the relationship between the recognition, detection, and defense of APT. The model of similar studies also lacks an explanation about it. To defend against APT attacks and inquire about the similarity of different APT attacks, this study proposes an APT malware classification method based on a combination of multiple deep learning algorithms and transfer learning by collecting malware used in several famous APT groups in public. By extracting the application programming interface (API) system calls, with the vector representation of features by combining dynamic LSTM and attention algorithm, we can obtain API at different APT families classification contributions trained dynamic. Thus, we used transfer learning to perform multiple classifications of the APT family. This study aims to reduce the burden of network security staff from reviewing a large number of suspicious files when defending against APT attacks. Additionally, it can effectively intercept them in the initial invasion stage of APT to perform targeted defense against specific APT attacks by combining threat intelligence in public. The experimental result shows that the proposed method can achieve 99.2% in distinguishing common malware from APT malware and assign APT malware to different APT families with an accuracy of 95.5%.

A spatial-temporal correlation based method for advanced persistent threat detection

Advanced Persistent Threats (APT) have caused severe damage to the core information infrastructure of many governments and organizations. APT attacks usually remain low and slow which makes them difficult to be detected. In this case, the way of correlatively analyzing massive logs generated by various security devices for effectively detecting the new type of cyber threat turns out to be more and more significant. In this paper, on the basis of analyzing the principles and characteristics of APT, we propose an intelligent threat detection method based on the expanded Cyber Attack Chain (CAC) model and the long short-term memory network (LSTM) autoencoder to extensively correlate malicious behaviors from spatial and temporal dimensions, which provides a brain new idea for the application and practice of complex network attack detection.

A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base

Advanced persistent threat (APT) is a special attack method, which is usually initiated by hacker groups to steal data or destroy systems for large enterprises and even countries. APT has a long-term and multi-stage characteristic, which makes it difficult for traditional detection methods to effectively identify. To detect APT attacks requires solving some problems: how to deal with various uncertain information during APT attack detection, how to fully train the APT detection model with small attack samples, and how to obtain the interpretable detection results for subsequent APT attack forensics. Traditional detection methods cannot effectively utilize multiple uncertain information with small samples. Meanwhile, most detection models are black box and lack a transparent calculation process, which makes it impossible for managers to analyze the reliability and evidence of the results. To solve these problems, a novel detection method based on belief rule base (BRB) is proposed in this paper, where expert knowledge and small samples are both utilized to obtain interpretable detection results. A case study with numerical simulation is established to prove the effectiveness and practicality of the proposed method.

Attribution Classification Method of APT Malware in IoT Using Machine Learning Techniques

In recent years, the popularity of IoT (Internet of Things) applications and services has brought great convenience to people's lives, but ubiquitous IoT has also brought many security problems. Among them, advanced persistent threat (APT) is one of the most representative attacks, and its continuous outbreak has brought unprecedented security challenges for the large-scale deployment of the IoT. However, important research on analyzing the attribution of APT malware samples is still relatively few. Therefore, we propose a classification method for attribution organizations with APT malware in IoT using machine learning. It aims to mark the real attacking organization entities to better identify APT attack activity and protect the security of IoT. This method performs feature representation and feature selection based on APT behavior data obtained from devices in the Internet of Things and selects the features with a high degree of differentiation among organizations. Then, it trains a multiclass model named SMOTE-RF that can better deal with imbalance and multiclassification problems. Our experiments on real dynamic behavior data are combined to verify the effectiveness of the method proposed in this paper for attribution analysis of APT malware samples and achieve good performance. Our method could identify the organization behind complex APT attacks in IoT devices and services.

A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures

In recent years, Smart Farming (SF) and Precision Agriculture (PA) have attracted attention from both the agriculture industry as well as the research community. Altogether, SF and PA aim to help farmers use inputs (such as fertilizers and pesticides) more efficiently through using Internet of Things (IoT) devices, but in doing so, they create new security threats that can defeat this purpose in the absence of adequate awareness and proper countermeasures. A survey on different security-related challenges is required to raise awareness and pave they way for further research in this area. In this paper, we first itemize the security aspects of SF and PA. Next, we review the types of cyber attacks that can violate each of these aspects. Accordingly, we present a taxonomy on cyber-threats to SF and PA on the basis of their relations to different stages of Cyber-Kill Chain (CKC). Among cyber-threats, we choose Advanced Persistent Threats (APTs) for further study. Finally, we studied related risk mitigation strategies and countermeasure, and developed a future road map for further study in this area. This paper’s main contribution is a categorization of security threats within the SF/PA areas and provide a taxonomy of security threats for SF environments so that we may detect the behavior of APT attacks and any other security threat in SF and PA environments.