Biblio

The article deals with the mathematical model for information security controls optimization and evaluation of the information security systems effectiveness. Distinctive features of APT attacks are given. The generalized efficiency criterion in which both the requirements of the return of security investment maximization and the return on attack minimization are simultaneously met. The generalized reduced gradient method for solving the optimization of the objective function based on formulated efficiency criterion is proposed.

In the context of Advanced Persistent Threat (APT) attacks, this paper introduces a model, called Nuke, which tries to provide a more operational reading of the attackers' lifecycle in a compromised network. It allows to consider the notions of regression; and repetitiveness of final objectives achievement. By confronting this model with examples of recent attacks (Equifax data breach and TV5Monde sabotage), we emphasize the importance of the attack chronology in the Cyber Threat Intelligence (CTI) reports, as well as the Tactics, Techniques and Procedures (TTP) used by the attacker during his progression.

Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

In order to deal with APT(Advanced Persistent Threat) attacks, this paper proposes a quantifiable Attack-Defense Tree model. First, the model gives both attack and defense leaf node a variety of security attributes. And then quantifies the nodes through the analytic hierarchy process. Finally, it analyzes the impact of the defense measures on the attack behavior. Through the application of the model, we can see that the quantifiable Attack-Defense Tree model can well describe the impact of defense measures on attack behavior.

This research aims to identify some vulnerabilities of advanced persistent threat (APT) attacks using multiple simulated attacks in a virtualized environment. Our experimental study shows that while updating the antivirus software and the operating system with the latest patches may help in mitigating APTs, APT threat vectors could still infiltrate the strongest defenses. Accordingly, we highlight some critical areas of security concern that need to be addressed.

Over a decade, intelligent and persistent forms of cyber threats have been damaging to the organizations' cyber assets and missions. In this paper, we analyze current cyber kill chain models that explain the adversarial behavior to perform advanced persistent threat (APT) attacks, and propose a cyber kill chain model that can be used in view of cyber situation awareness. Based on the proposed cyber kill chain model, we propose a threat taxonomy that classifies attack tactics and techniques for each attack phase using CAPEC, ATT&CK that classify the attack tactics, techniques, and procedures (TTPs) proposed by MITRE. We also implement a cyber common operational picture (CyCOP) to recognize the situation of cyberspace. The threat situation can be represented on the CyCOP by applying cyber kill chain based threat taxonomy.

There are continuous hacking and social issues regarding APT (Advanced Persistent Threat - APT) attacks and a number of antivirus businesses and researchers are making efforts to analyze such APT attacks in order to prevent or cope with APT attacks, some host PC security technologies such as firewalls and intrusion detection systems are used. Therefore, in this study, malignant behavior patterns were extracted by using an API of PE files. Moreover, the FP-Growth Algorithm to extract behavior information generated in the host PC in order to overcome the limitation of the previous signature-based intrusion detection systems. We will utilize this study as fundamental research about a system that extracts malignant behavior patterns within networks and APIs in the future.

This paper proposes a method to detect two primary means of using the Domain Name System (DNS) for malicious purposes. We develop machine learning models to detect information exfiltration from compromised machines and the establishment of command & control (C&C) servers via tunneling. We validate our approach by experiments where we successfully detect a malware used in several recent Advanced Persistent Threat (APT) attacks [1]. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

Advanced Persistent Threats are increasingly becoming one of the major concerns to many industries and organizations. Currently, there exists numerous articles and industrial reports describing various case studies of recent notable Advanced Persistent Threat attacks. However, these documents are expressed in natural language. This limits the efficient reusability of the threat intelligence information due to ambiguous nature of the natural language. In this article, we propose a model to formally represent Advanced Persistent Threats as multi-agent systems. Our model is inspired by the concepts of agent-oriented social modelling approaches, generally used for software security requirement analysis.

Advanced Persistent Threat (APT) attacks became a major network threat in recent years. Among APT attack techniques, sending a phishing email with malicious documents attached is considered one of the most effective ones. Although many users have the impression that documents are harmless, a malicious document may in fact contain shellcode to attack victims. To cope with the problem, we design and implement a malicious document detector called Forensor to differentiate malicious documents. Forensor integrates several open-source tools and methods. It first introspects file format to retrieve objects inside the documents, and then automatically decrypts simple encryption methods, e.g., XOR, rot and shift, commonly used in malware to discover potential shellcode. The emulator is used to verify the presence of shellcode. If shellcode is discovered, the file is considered malicious. The experiment used 9,000 benign files and more than 10,000 malware samples from a well-known sample sharing website. The result shows no false negative and only 2 false positives.

Advanced Persistent Threat (APT) attacks, which have become prevalent in recent years, are classified into four phases. These are initial compromise phase, attacking infrastructure building phase, penetration and exploration phase, and mission execution phase. The malware on infected terminals attempts various communications on and after the attacking infrastructure building phase. In this research, using OpenFlow technology for virtual networks, we developed a system of identifying infected terminals by detecting communication events of malware communications in APT attacks. In addition, we prevent information fraud by using OpenFlow, which works as real-time path control. To evaluate our system, we executed malware infection experiments with a simulation tool for APT attacks and malware samples. In these experiments, an existing network using only entry control measures was prepared. As a result, we confirm the developed system is effective.