Biblio

Passwords are still the most widespread means for authenticating users, even though they have been shown to create huge security problems. This motivated the use of additional authentication mechanisms used in so-called multi-factor authentication protocols. In this paper we define a detailed threat model for this kind of protocols: while in classical protocol analysis attackers control the communication network, we take into account that many communications are performed over TLS channels, that computers may be infected by different kinds of malwares, that attackers could perform phishing, and that humans may omit some actions. We formalize this model in the applied pi calculus and perform an extensive analysis and comparison of several widely used protocols - variants of Google 2-step and FIDO's U2F. The analysis is completely automated, generating systematically all combinations of threat scenarios for each of the protocols and using the P ROVERIF tool for automated protocol analysis. Our analysis highlights weaknesses and strengths of the different protocols, and allows us to suggest several small modifications of the existing protocols which are easy to implement, yet improve their security in several threat scenarios.

With the rapid development of Internet of Things (IoT), distributed denial of service (DDoS) attacks become the important security threat of the IoT. Characteristics of IoT, such as large quantities and simple function, which have easily caused the IoT devices or servers to be attacked and be turned into botnets for launching DDoS attacks. In this paper, we use software-defined networking (SDN) to develop moving target defense (MTD) architecture that increases uncertainty because of ever changing attack surface. In addition, we deploy SDN-based honeypots to mimic IoT devices, luring attackers and malwares. Finally, experimental results show that combination of MTD and SDN-based honeypots can effectively hide network asset from scanner and defend against DDoS attacks in IoT.

With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.

As recently studied, network-on-chip (NoC) suffers growing threats from hardware trojans (HTs), leading to performance degradation or information leakage when it provides communication service in many/multi-core systems. Therefore, defense techniques against NoC HTs experience rapid development in recent years. However, to the best of our knowledge, there are few standard benchmarks developed for the defense techniques evaluation. To address this issue, in this paper, we design a suite of benchmarks which involves multiple NoCs with different HTs, so that researchers can compare various HT defense methods fairly by making use of them. We first briefly introduce the features of target NoC and its infected modules in our benchmarks, and then, detail the design of our NoC HTs in a one-by-one manner. Finally, we evaluate our benchmarks through extensive simulations and report the circuit cost of NoC HTs in terms of area and power consumption, as well as their effects on NoC performance. Besides, comprehensive experiments, including functional testing and side channel analysis are performed to assess the stealthiness of our HTs.

NoC (Network-on-Chip) is widely considered and researched by academic communities as a new inter-core interconnection method that replaces the bus. Nowadays, the complexity of on-chip systems is increasing, requiring better communication performance and scalability. Therefore, the optimization of communication performance has become one of the research hotspots. While the NoC is rapidly developing, it is threatened by hardware Trojans inserted during the design or manufacturing processes. This leads to that the attackers can exploit NoC's vulnerability to attack the on-chip systems. To solve the problem, we design and implement a replay-type hardware Trojan inserted into the NoC, aiming to provide a benchmark test set to promote the defense strategies for NoC hardware security. The experiment proves that the power consumption of the designed Trojan accounts for less than one thousandth of the entire NoC power consumption and area. Besides, simulation experiments reveal that this replaytype hardware Trojan can reduce the network throughput.

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

The prevalence of Cloud Computing has appealed many users to put their business into low-cost and flexible cloud servers instead of bare-metal machines. Most virtual machines in the cloud run commodity operating system(e.g., linux), and the complexity of such operating systems makes them more bug-prone and easier to be compromised. To mitigate the security threats, previous works attempt to mediate and filter system calls, transform all unpopular paths into popular paths, or implement a nested kernel along with the untrusted outter kernel to enforce certain security policies. However, such solutions only enforce read-only protection or assume that popular paths in the kernel to contain almost no bug, which is not always the case in the real world. To overcome their shortcomings and combine their advantages as much as possible, we propose a hardware-assisted isolation mechanism that isolates untrusted part of the kernel. To achieve isolation, we prepare multiple restricted Extended Page Table (EPT) during boot time, each of which has certain critical data unmapped from it so that the code executing in the isolated environment could not access sensitive data. We leverage the VMFUNC instruction already available in recent Intel processors to directly switch to another pre-defined EPT inside guest virtual machine without trapping into the underlying hypervisor, which is faster than the traditional trap-and-emulate procedure. The semantic gap is minimized and real-time check is achieved by allowing EPT violations to be converted to Virtualization Exception (VE), which could be handled inside guest kernel in non-root mode. Our preliminary evaluation shows that with hardware virtualization feature, we are able to run the untrusted code in an isolated environment with negligible overhead.

As the dynamics of cyber warfare continue to change, it is very important to be aware of the issues currently confronting cyberspace. One threat which continues to grow in the danger it poses to cyber security are botnets. Botnets can launch massive Distributed Denial of Service (DDoS) attacks against internet connected hosts anonymously, undertake intricate spam campaigns, launch mass financial fraud campaigns, and even manipulate public opinion via social media bots. The network topology and technology undergirding each botnet varies greatly, as do the motivations commonly behind such networks. Furthermore, as botnets have continued to evolve, many newer ones demonstrate increased levels of anonymity and sophistication, making it more difficult to effectively counter them. Increases in the production of vulnerable Internet of Things (IoT) devices has made it easier for malicious actors to quickly assemble sizable botnets. Because of this, the steps necessary to stop botnets also vary, and in some cases, it may be extremely difficult to effectively defeat a fully functional and sophisticated botnet. While in some cases, the infrastructure supporting the botnet can be targeted and remotely disabled, other cases require the physical assistance of law enforcement to shut down the botnet. In the latter case, it is often a significant challenge to cheaply end a botnet. On the other hand, there are many steps and mitigations that can be taken by end-users to prevent their own devices from becoming part of a botnet. Many of these solutions involve implementing basic cybersecurity practices like installing firewalls and changing default passwords. More sophisticated botnets may require similarly sophisticated intrusion detection systems, to detect and remove malicious infections. Much research has gone into such systems and in recent years many researchers have begun to implement machine learning techniques to defeat botnets. This paper is intended present a review on botnet evolution, trends and mitigations, and offer related examples and research to provide the reader with quick access to a broad understanding of the issues at hand.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

The botnet has been one of the most common threats to the network security since it exploits multiple malicious codes like worm, Trojans, Rootkit, etc. These botnets are used to perform the attacks, send phishing links, and/or provide malicious services. It is difficult to detect Peer-to-peer (P2P) botnets as compare to IRC (Internet Relay Chat), HTTP (HyperText Transfer Protocol) and other types of botnets because of having typical features of the centralization and distribution. To solve these problems, we propose an effective two-stage traffic classification method to detect P2P botnet traffic based on both non-P2P traffic filtering mechanism and machine learning techniques on conversation features. At the first stage, we filter non-P2P packages to reduce the amount of network traffic through well-known ports, DNS query, and flow counting. At the second stage, we extract conversation features based on data flow features and flow similarity. We detected P2P botnets successfully, by using Machine Learning Classifiers. Experimental evaluations show that our two-stage detection method has a higher accuracy than traditional P2P botnet detection methods.

Botnet has been evolving over time since its birth. Nowadays, P2P (Peer-to-Peer) botnet has become a main threat to cyberspace security, owing to its strong concealment and easy expansibility. In order to effectively detect P2P botnet, researchers often focus on the analysis of network traffic. For the sake of enriching P2P botnet detection methods, the author puts forward a new sight of applying distributed threat intelligence sharing system to P2P botnet detection. This system aims to fight against distributed botnet by using distributed methods itself, and then to detect botnet in real time. To fulfill the goal of botnet detection, there are 3 important parts: the threat intelligence sharing and evaluating system, the BAV quantitative TI model, and the AHP and HMM based analysis algorithm. Theoretically, this method should work on different types of distributed cyber threat besides P2P botnet.

with the development of SDN, ICN and 5G networks, the research of future network becomes a hot topic. Based on the design idea of SDN network, this paper analyzes the propagation model and detection method of malicious code in future network. We select characteristics of SDN and analyze the features use different feature selection methods and sort the features. After comparison the influence of running time by different classification algorithm of different feature selection, we analyze the choice of reduction dimension m, and find out the different types of malicious code corresponding to the optimal feature subset and matching classification method, designed for malware detection system. We analyze the node migration rate of malware in mobile network and its effect on the outbreak of the time. In this way, it can provide reference for the management strategy of the switch node or the host node by future network controller.

The globalization of the integrated circuit supply chain has given rise to major security concerns ranging from intellectual property piracy to hardware Trojans. Logic encryption is a promising solution to tackle these threats. Recently, a Boolean satisfiability attack capable of unlocking existing logic encryption techniques was introduced. This attack initiated a paradigm shift in the design of logic encryption algorithms. However, recent approaches have been strongly focusing on low-cost countermeasures that unfortunately lead to low functional and structural corruption. In this paper, we show that a simple approach can offer provable security and more than 99% corruption if a higher area overhead is accepted. Our results strongly suggest that future proposals should consider higher overheads or more realistic circuit sizes for the evaluation of modern logic encryption algorithms.

Nowadays mobile devices have become the majority terminals used by people for social activities so that carrying business data and private information in them have become normal. Accordingly, the risk of data related cyber attacks has become one of the most critical security concerns. The main purpose of this work is to mitigate the risk of data breaches and damages caused by malware and the lost of mobile devices. In this paper, we propose an encrypted QR code based optical challenge-response authentication by mobile devices for mounting concealed file systems. The concealed file system is basically invisible to the users unless being successfully mounted. The proposed authentication scheme practically applies cryptography and QR code technologies to challenge-response scheme in order to secure the concealed file system. The key contribution of this work is to clarify a possibility of a mounting authentication scheme involving two mobile devices using a special optical communication way (QR code exchanges) which can be realizable without involving any network accesses. We implemented a prototype system and based on the preliminary feature evaluations results we confirmed that encrypted QR code based optical challenge-response is possible between a laptop and a smart phone and it can be applied to authentication for mounting concealed file systems.

Hard-coded credentials such as clear text log-in id and password provided by the IoT manufacturers and unsecured ways of remotely accessing IoT devices are the major security concerns of industry and academia. Limited memory, power, and processing capabilities of IoT devices further worsen the situations in improving the security of IoT devices. In such scenarios, a lightweight security algorithm up to some extent can minimize the risk. This paper proposes one such approach using Quick Response (QR) code to mitigate hard-coded credentials related attacks such as Mirai malware, wreak havoc, etc. The QR code based approach provides non-clear text unpredictable login id and password. Further, this paper also proposes a secured way of remotely accessing IoT devices through modified https. The proposed algorithms are implemented and verified using Raspberry Pi 3 model B.

Malware threat classification involves understanding the behavior of the malicious software and how it affects a victim host system. Classifying threats allows for measured response appropriate to the risk involved. Malware incident response depends on many automated tools for the classification of threat to help identify the appropriate reaction to a threat alert. Cuckoo Sandbox is one such tool which can be used for automated analysis of malware and one method of threat classification provided is a threat score. A security analyst might submit a suspicious file to Cuckoo for analysis to determine whether or not the file contains malware or performs potentially malicious behavior on a system. Cuckoo is capable of producing a report of this behavior and ranks the severity of the observed actions as a score from one to ten, with ten being the most severe. As such, a malware sample classified as an 8 would likely take priority over a sample classified as a 3. Unfortunately, this scoring classification can be misleading due to the underlying methodology of severity classification. In this paper we demonstrate why the current methodology of threat scoring is flawed and therefore we believe it can be improved with greater emphasis on analyzing the behavior of the malware. This allows for a threat classification rating which scales with the risk involved in the malware behavior.

With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.

Network-on-Chip (NoC) is the communication platform of the data among the processing cores in Multiprocessors System-on-Chip (MPSoC). NoC has become a target to security attacks and by outsourcing design, it can be infected with a malicious Hardware Trojan (HT) to degrades the system performance or leaves a back door for sensitive information leaking. In this paper, we proposed a HT model that applies a denial of service attack by deliberately discarding the data packets that are passing through the infected node creating a black hole in the NoC. It is known as Black Hole Router (BHR) attack. We studied the effect of the BHR attack on the NoC. The power and area overhead of the BHR are analyzed. We studied the effect of the locations of BHRs and their distribution in the network as well. The malicious nodes has very small area and power overhead, 1.98% and 0.74% respectively, with a very strong violent attack.

Today, a significant threat to organisational information systems is ransomware that can completely occlude the information system by denying access to its data. To reduce this exposure and damage from ransomware attacks, organisations are obliged to concentrate explicitly on the threat of ransomware, alongside their malware prevention strategy. In attempting to prevent the escalation of ransomware attacks, it is important to account for their polymorphic behaviour and dispersion of inexhaustible versions. However, a number of ransomware samples possess similarity as they are created by similar groups of threat actors. A particular threat actor or group often adopts similar practices or codebase to create unlimited versions of their ransomware. As a result of these common traits and codebase, it is probable that new or unknown ransomware variants can be detected based on a comparison with their originating or existing samples. Therefore, this paper presents a detection method for ransomware by employing a similarity preserving hashing method called fuzzy hashing. This detection method is applied on the collected WannaCry or WannaCryptor ransomware corpus utilising three fuzzy hashing methods SSDEEP, SDHASH and mvHASH-B to evaluate the similarity detection success rate by each method. Moreover, their fuzzy similarity scores are utilised to cluster the collected ransomware corpus and its results are compared to determine the relative accuracy of the selected fuzzy hashing methods.

With the rapid increase in the number of Internet of Things (IoT) devices, mobile devices, cloud services, and cyber-physical systems, the large-scale cyber attacks on enterprises and public sectors have increased. In particular, ransomware attacks damaged UK's National Health Service and many enterprises around the world in 2017. Therefore, researchers have proposed ransomware detection and prevention systems. However, manual inspection in static and dynamic ransomware analysis is time-consuming and it cannot cope with the rapid increase in variants of ransomware family. Recently, machine learning has been used to automate ransomware analysis by creating a behavioral model of same ransomware family. To create effective behavioral models of ransomware, we first obtained storage access patterns of live ransomware samples and of a benign application by using a live-forensic hypervisor called WaybackVisor. To distinguish ransomware from a benign application that has similar behavior to ransomware, we carefully selected five dimensional features that were extracted both from actual ransomware's Input and Output (I/O) logs and from a benign program's I/O logs. We created and evaluated machine learning models by using Random Forest, Support Vector Machine, and K-Nearest Neighbors. Our experiments using the proposed five features of storage access patterns achieved F-measure rate of 98%.

In parallel with the increasing growth of the Internet and computer networks, the number of malwares has been increasing every day. Today, one of the newest attacks and the biggest threats in cybersecurity is ransomware. The effectiveness of applying machine learning techniques for malware detection has been explored in much scientific research, however, there is few studies focused on machine learning-based ransomware detection. In this paper, the effectiveness of ransomware detection using machine learning methods applied to CICAndMal2017 dataset is examined in two experiments. First, the classifiers are trained on a single dataset containing different types of ransomware. Second, different classifiers are trained on datasets of 10 ransomware families distinctly. Our findings imply that in both experiments random forest outperforms other tested classifiers and the performance of the classifiers are not changed significantly when they are trained on each family distinctly. Therefore, the random forest classification method is very effective in ransomware detection.

With the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.

Software defined networks (SDNs) represent new centralized network architecture that facilitates the deployment of services, applications and policies from the upper layers, relatively the management and control planes to the lower layers the data plane and the end user layer. SDNs give several advantages in terms of agility and flexibility, especially for mobile operators and for internet service providers. However, the implementation of these types of networks faces several technical challenges and security issues. In this paper we will focus on SDN's security issues and we will propose the implementation of a centralized security layer named AM-SecP. The proposed layer is linked vertically to all SDN layers which ease packets inspections and detecting intrusions. The purpose of this architecture is to stop and to detect malware infections, we do this by denying services and tunneling attacks without encumbering the networks by expensive operations and high calculation cost. The implementation of the proposed framework will be also made to demonstrate his feasibility and robustness.