PAYLOAD EXTRACTOR DESIGN PATTERN CODE

Lecture Notes in Computer Science (LNCS), vol. In: Breaking the Steganographic Utilities EzStego Jsteg Steganos and S- Tools-and Some Lessons Learned. Westfeld, A., Pfitzmann, A.: Attacks on steganographic systems. Tian, J.: Reversible data embedding using a difference expansion. In: IEEE 3rd International Conference on Cyber Security and Cloud Computing (2016) Thakur, K., Kopecky, S., Nuseir, M., Ali, M.L., Qiu, M.: Advanced Persistent Threats: Behind the Scenes. In: IET Conference on Wireless, Mobile and Multimedia Networks, pp. Sutaone, M.S., Khandare, M.V.: Image based steganography using LSB insertion technique. In: International Joint Conference On Science And Technology (IJCST), May–June 2014, vol. Saini, L.K., Shrivastava, V.: A survey of digital watermarking techniques and its applications. National Institute of Standards and Technology (NIST) Report Number 800-39, March 2011 Ross, R.S.: Managing information security risk: organization, mission, and information system view. Mao, Q., Li, F., Chang, C.-C.: Reversible data hiding with oriented and minimized distortions using cascading trellis coding. Li, X., Li, J., Li, B., Yang, B.: High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error- expansion. In: International Conference on Information Technology (ICIT) (2017) Kittawi, N., Al-Haj, A.: Reversible data hiding in encrypted images. In: International Conference on Signal Processing and Integrated Networks (SPIN) (2015) Kamila, S., Roy, R., Changder, S.: A DWT based steganography scheme with image block partitioning. Kayarkar, H., Sanyal, S.: A survey on various data hiding techniques and their comparative analysis. Hutchins, E.M., Cloppert, M.J., Amin, R.M.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Gandotra, E., Bansal, D., Sofat, S.: Malware analysis and classification: a survey. 581–590, April 2006įridrich, J., Goljan, M., Du, R.: Detecting LSB steganography in color, and gray-scale images. In: CHI ’06 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. In: International Federation for Information Processing (IFIP) International Conference on Communications and Multimedia Security, pp. ISBN-13 978-1530508907Ĭhen, P., Desmet, L., Huygens, C.: A study on advanced persistent threats. 54–60, October 2010īazzell, M.: Open Source Intelligence Techniques: Resources for Searching and Analyzing Online Information, 5th edn. In: AISec ’10 Proceedings of the 3rd ACM Workshop on Artificial Intelligence and Security, pp. 37(12), 7913–7921 (2010)īlum, A., Wardman, B., Solorio, T., Warner, G.: Lexical feature based phishing URL detection using online learning. KeywordsĪburrousa, M., Hossaina, M.A., Dahala, K., Thabtahb, F.: Intelligent phishing detection system for e-banking using fuzzy data mining. We present the more efficient detection methods to countermeasure advanced persistent threat in this paper. In our experiments, both stego-image analysis and extractor program are tested. The steganography is always used to deliver secret message by building a covert channel, but this technique can also be used to hide malware.

The behavior and signature of the extractor program are very different with the other malwares. In contrast, schemes using our steganography-based approach are less likely to be detected since there is no detecting system/software that analyzes pixel values of an image.

PAYLOAD EXTRACTOR DESIGN PATTERN CODE

The most common methods of delivering malicious code to target include phishing, watering hole attack, removable drive, compromised control system, and provided system service these can all be detected.

The stego-image can be triggered by an extractor program. This paper proposes an APT attack scheme that exploits image steganography, in which a malicious code is embedded into a digital image. These threats usually aim at stealing secure information and include six phases: (i) Reconnaissance, (ii) Delivery, (iii) Initial intrusion, (iv) Command and control, (v) Lateral movement, and (vi) Data exfiltration. The main characteristic of Advanced Persistent Threats is the stealthy and long period of attack.

Payload extractor design pattern