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Register a new userDifferential Area Analysis for Ransomware Attack Detection within Mixed File Datasets
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William Buchanan Prof
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The threat from ransomware continues to grow both in the number of affected victims as well as the cost incurred by the people and organisations impacted in a successful attack. In the majority of cases, once a victim has been attacked there remain only two courses of action open to them; either pay the ransom or lose their data. One common behaviour shared between all crypto ransomware strains is that at some point during their execution they will attempt to encrypt the users files. Previous research Penrose et al. (2013); Zhao et al. (2011) has highlighted the difficulty in differentiating between compressed and encrypted files using Shannon entropy as both file types exhibit similar values. One of the experiments described in this paper shows a unique characteristic for the Shannon entropy of encrypted file header fragments. This characteristic was used to differentiate between encrypted files and other high entropy files such as archives. This discovery was leveraged in the development of a file classification model that used the differential area between the entropy curve of a file under analysis and one generated from random data. When comparing the entropy plot values of a file under analysis against one generated by a file containing purely random numbers, the greater the correlation of the plots is, the higher the confidence that the file under analysis contains encrypted data.
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Ransomware, a type of malicious software that encrypts a victims files and only releases the cryptographic key once a ransom is paid, has emerged as a potentially devastating class of cybercrimes in the past few years. In this paper, we present RAPTOR, a promising line of defense against ransomware attacks. RAPTOR fingerprints attackers operations to forecast ransomware activity. More specifically, our method learns features of malicious domains by looking at examples of domains involved in known ransomware attacks, and then monitors newly registered domains to identify potentially malicious ones. In addition, RAPTOR uses time series forecasting techniques to learn models of historical ransomware activity and then leverages malicious domain registrations as an external signal to forecast future ransomware activity. We illustrate RAPTORs effectiveness by forecasting all activity stages of Cerber, a popular ransomware family. By monitoring zone files of the top-level domain .top starting from August 30, 2016 through May 31, 2017, RAPTOR predicted 2,126 newly registered domains to be potential Cerber domains. Of these, 378 later actually appeared in blacklists. Our empirical evaluation results show that using predicted domain registrations helped improve forecasts of future Cerber activity. Most importantly, our approach demonstrates the value of fusing different signals in forecasting applications in the cyber domain.
Security operation centers (SOCs) typically use a variety of tools to collect large volumes of host logs for detection and forensic of intrusions. Our experience, supported by recent user studies on SOC operators, indicates that operators spend ample time (e.g., hundreds of man-hours) on investigations into logs seeking adversarial actions. Similarly, reconfiguration of tools to adapt detectors for future similar attacks is commonplace upon gaining novel insights (e.g., through internal investigation or shared indicators). This paper presents an automated malware pattern-extraction and early detection tool, testing three machine learning approaches: TF-IDF (term frequency-inverse document frequency), Fishers LDA (linear discriminant analysis) and ET (extra trees/extremely randomized trees) that can (1) analyze freshly discovered malware samples in sandboxes and generate dynamic analysis reports (host logs); (2) automatically extract the sequence of events induced by malware given a large volume of ambient (un-attacked) host logs, and the relatively few logs from hosts that are infected with potentially polymorphic malware; (3) rank the most discriminating features (unique patterns) of malware and from the learned behavior detect malicious activity; and (4) allows operators to visualize the discriminating features and their correlations to facilitate malware forensic efforts. To validate the accuracy and efficiency of our tool, we design three experiments and test seven ransomware attacks (i.e., WannaCry, DBGer, Cerber, Defray, GandCrab, Locky, and nRansom). The experimental results show that TF-IDF is the best of the three methods to identify discriminating features, and ET is the most time-efficient and robust approach.
Today, Internet communication security has become more complex as technology becomes faster and more efficient, especially for resource-limited devices such as embedded devices, wireless sensors, and radio frequency identification (RFID) tags, and Internet of Things (IoT). Lightweight encryption algorithms provide security for these devices to protect data against intruders. But the limitation of using energy in lightweight block ciphers (LBCs) is one of the major challenges for ever-expanding IoT technologies. Also, these LBC are subject to Side-channel attacks, which are among the most cited threats to these ciphers. In this paper, a differential power attack (DPA) to the Midori64 block cipher is designed. According to the proposed method, an attack on the S-boxes of the first round is done to obtain half of the master key bits. Then, the S-boxes of the second round were attacked to obtain remaining the master key bits. The results confirmed that the key is ultimately obtained. With the low volume of computational complexity, we obtained the Midori block cipher key, which was considered secure, just by using 300 samples of the plaintext. Following the running of Midori64 on the AVR microcontroller of the Atmega32 model, the master key of Midori block cipher is discovered with 300 known texts. Furthermore, we obtained the master key with a smaller number of samples than the electromagnetic analysis attack.
A Previously traditional methods were sufficient to protect the information, since it is simplicity in the past does not need complicated methods but with the progress of information technology, it become easy to attack systems, and detection of encryption methods became necessary to find ways parallel with the differing methods used by hackers, so the embedding methods could be under surveillance from system managers in an organization that requires the high level of security. This fact requires researches on new hiding methods and cover objects which hidden information is embedded in. It is the result from the researches to embed information in executable files, but when will use the executable file for cover they have many challenges must be taken into consideration which is any changes made to the file will be firstly detected by untie viruses, secondly the functionality of the file is not still functioning. In this paper, a new information hiding system is presented. The aim of the proposed system is to hide information (data file) within image page of execution file (EXEfile) to make sure changes made to the file will not be detected by universe and the functionality of the exe.file is still functioning after hiding process. Meanwhile, since the cover file might be used to identify hiding information, the proposed system considers overcoming this dilemma by using the execution file as a cover file.
Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There are two types of MI attacks in the literature, i.e., these with and without shadow models. The success of the former heavily depends on the quality of the shadow model, i.e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information. In this paper, we propose an MI attack, called BlindMI, which probes the target model and extracts membership semantics via a novel approach, called differential comparison. The high-level idea is that BlindMI first generates a dataset with nonmembers via transforming existing samples into new samples, and then differentially moves samples from a target dataset to the generated, non-member set in an iterative manner. If the differential move of a sample increases the set distance, BlindMI considers the sample as non-member and vice versa. BlindMI was evaluated by comparing it with state-of-the-art MI attack algorithms. Our evaluation shows that BlindMI improves F1-score by nearly 20% when compared to state-of-the-art on some datasets, such as Purchase-50 and Birds-200, in the blind setting where the adversary does not know the target models architecture and the target datasets ground truth labels. We also show that BlindMI can defeat state-of-the-art defenses.
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