Biblio

SCADA systems are one of the critical infrastructures and face many security threats. Attackers can control SCADA systems through network attacks, destroying the normal operation of the power system. It is important to conduct a risk assessment of security threats on SCADA systems. However, existing models for risk assessment using attack trees mainly focus on describing possible intrusions rather than the interaction between threats and defenses. In this paper, we comprehensively consider intrusion likelihood and defense capability and propose a quantitative risk assessment model of security threats based on attack countermeasure tree (ACT). Each leaf node in ACT contains two attributes: exploitable vulnerabilities and defense countermeasures. An attack scenario can be constructed by means of traversing the leaf nodes. We set up six indicators to evaluate the impact of security threats in attack scenarios according to NISTIR 7628 standard. Experimental results show the attack probability of security threats and high-risk attack scenarios in SCADA systems. We can improve defense countermeasures to protect against security threats corresponding to high-risk scenarios. In addition, the model can continually update risk assessments based on the implementation of the system’s defensive countermeasures.

The Internet of Things devices is rapidly becoming widespread, as are IoT services. Their achievement has not gone unnoticed, as threats as well as attacks towards IoT devices as well as services continue to grow. Cyber attacks are not unique to IoT, however as IoT becomes more ingrained in our lives as well as communities, it is imperative to step up as well as take cyber defense seriously. As a result, there is a genuine need to protect IoT, which necessitates a thorough understanding of the dangers and attacks against IoT infrastructure. The purpose of this study is to define threat types, as well as to assess and characterize intrusions and assaults against IoT devices as well as services

Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.

Nowadays, Windows is an operating system that is very popular among people, especially users who have limited knowledge of computers. But unconsciously, the security threat to the windows operating system is very high. Security threats can be in the form of illegal exploitation of the system. The most common attack is using malware. To determine the characteristics of malware using dynamic analysis techniques and static analysis is very dependent on the availability of malware samples. Honeypot is the most effective malware collection technique. But honeypot cannot determine the type of file format contained in malware. File format information is needed for the purpose of handling malware analysis that is focused on windows-based malware. For this reason, we propose a framework that can collect malware information as well as identify malware PE file type formats. In this study, we collected malware samples using a modern honey network. Next, we performed a feature extraction to determine the PE file format. Then, we classify types of malware using VirusTotal scanning. As the results of this study, we managed to get 1.222 malware samples. Out of 1.222 malware samples, we successfully extracted 945 PE malware. This study can help researchers in other research fields, such as machine learning and deep learning, for malware detection.

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.

A local area network (LAN) computer security control circuit is designed for the practical problem of LAN computer users "one machine crosses two networks" on this paper, which provides a protection barrier for the information security of LAN computers on the hardware. This paper briefly analyzes the risks and challenges faced by LAN security. The overall design idea, circuit design and working principle of LAN computer security control circuit are described in detail. The characteristics of the system are summarized. Finally, the design circuit is verified by practical application in the unit. The application results show that the circuit is stable in operation, simple in operation, safe and reliable, and convenient in installation and maintain, etc., which has achieved the design effect and played a good role in ensuring the security of the network information of the local area network.

To guarantee the quality of software, specifying security requirements (SRs) is essential for developing systems, especially for security-critical software systems. However, using security threat to determine detailed SR is quite difficult according to Common Criteria (CC), which is too confusing and technical for non-security specialists. In this paper, we propose a Co-occurrence Recommend Model (CoRM) to automatically recommend software SRs. In this model, the security threats of product are extracted from security target documents of software, in which the related security requirements are tagged. In order to establish relationships between software security threat and security requirement, semantic similarities between different security threat is calculated by Skip-thoughts Model. To evaluate our CoRM model, over 1000 security target documents of 9 types software products are exploited. The results suggest that building a CoRM model via semantic similarity is feasible and reliable.

Recent studies have shown that co-resident attacks have aroused great security threat in cloud. Since hardware is shared among different tenants, malicious tenants can launch various co-resident attacks, such as side channel attacks, covert channel attacks and resource interference attacks. Existing countermeasures have their limitations and can not provide comprehensive defense against co-resident attacks. This paper combines the advantages of various countermeasures and proposes a complete co-resident threat defense solution which consists of co-resident-resistant VM allocation (CRRVA), analytic hierarchy process-based threat score mechanism (AHPTSM) and attack-aware VM reallocation (AAVR). CRRVA securely allocates VMs and also takes load balance and power consumption into consideration to make the allocation policy more practical. According to the intrinsic characteristics of co-resident attacks, AHPTSM evaluates VM's threat score which denotes the probability that a VM is suffering or conducting co-resident attacks based on analytic hierarchy process. And AAVR further migrates VMs with extremely high threat scores and separates VM pairs which are likely to be malicious to each other. Extensive experiments in CloudSim have shown that CRRVA can greatly reduce the allocation co-resident threat as well as balancing the load for both CSPs and tenants with little impact on power consumption. In addition, guided by threat score distribution, AAVR can effectively guarantee runtime co-resident security by migrating high threat score VMs with less migration cost.

The pervasive use of databases for the storage of critical and sensitive information in many organizations has led to an increase in the rate at which databases are exploited in computer crimes. While there are several techniques and tools available for database forensic analysis, such tools usually assume an apriori database preparation, such as relying on tamper-detection software to already be in place and the use of detailed logging. Further, such tools are built-in and thus can be compromised or corrupted along with the database itself. In practice, investigators need forensic and security audit tools that work on poorlyconfigured systems and make no assumptions about the extent of damage or malicious hacking in a database.In this paper, we present our database forensics methods, which are capable of examining database content from a storage (disk or RAM) image without using any log or file system metadata. We describe how these methods can be used to detect security breaches in an untrusted environment where the security threat arose from a privileged user (or someone who has obtained such privileges). Finally, we argue that a comprehensive and independent audit framework is necessary in order to detect and counteract threats in an environment where the security breach originates from an administrator (either at database or operating system level).

Nowadays, Vehicular ad hoc network confronts many challenges in terms of security and privacy, due to the fact that data transmitted are diffused in an open access environment. However, highest of drivers want to maintain their information discreet and protected, and they do not want to share their confidential information. So, the private information of drivers who are distributed in this network must be protected against various threats that may damage their privacy. That is why, confidentiality, integrity and availability are the important security requirements in VANET. This paper focus on security threat in vehicle network especially on the availability of this network. Then we regard the rational attacker who decides to lead an attack based on its adversary's strategy to maximize its own attack interests. Our aim is to provide reliability and privacy of VANET system, by preventing attackers from violating and endangering the network. to ensure this objective, we adopt a tree structure called attack tree to model the attacker's potential attack strategies. Also, we join the countermeasures to the attack tree in order to build attack-defense tree for defending these attacks.

Secure routing over VANET is a major issue due to its high mobility environment. Due to dynamic topology, routes are frequently updated and also suffers from link breaks due to the obstacles i.e. buildings, tunnels and bridges etc. Frequent link breaks can cause packet drop and thus result in degradation of network performance. In case of VANETs, it becomes very difficult to identify the reason of the packet drop as it can also occur due to the presence of a security threat. VANET is a type of wireless adhoc network and suffer from common attacks which exist for mobile adhoc network (MANET) i.e. Denial of Services (DoS), Black hole, Gray hole and Sybil attack etc. Researchers have already developed various security mechanisms for secure routing over MANET but these solutions are not fully compatible with unique attributes of VANET i.e. vehicles can communicate with each other (V2V) as well as communication can be initiated with infrastructure based network (V2I). In order to secure the routing for both types of communication, there is need to develop a solution. In this paper, a method for secure routing is introduced which can identify as well as eliminate the existing security threat.

Cloud computing presents unlimited prospects for Information Technology (IT) industry and business enterprises alike. Rapid advancement brings a dark underbelly of new vulnerabilities and challenges unfolding with alarming regularity. Although cloud technology provides a ubiquitous environment facilitating business enterprises to conduct business across disparate locations, security effectiveness of this platform interspersed with threats which can bring everything that subscribes to the cloud, to a halt raises questions. However advantages of cloud platforms far outweighs drawbacks and study of new challenges helps overcome drawbacks of this technology. One such emerging security threat is of ransomware attack on the cloud which threatens to hold systems and data on cloud network to ransom with widespread damaging implications. This provides huge scope for IT security specialists to sharpen their skillset to overcome this new challenge. This paper covers the broad cloud architecture, current inherent cloud threat mechanisms, ransomware vulnerabilities posed and suggested methods to mitigate it.

The QR codes have gained wide popularity in mobile marketing and advertising campaigns. However, the hidden security threat on the involved information system might endanger QR codes' success, and this issue has not been adequately addressed. In this paper we propose to examine the life cycle of a redesigned QR code ecosystem to identify the possible security risks. On top of this examination, we further propose standard changes to enhance security through a digital signature mechanism.

Security decision-making is a critical task in tackling security threats affecting a system or process. It often involves selecting a suitable resolution action to tackle an identified security risk. To support this selection process, decision-makers should be able to evaluate and compare available decision options. This article introduces a modelling language that can be used to represent the effects of resolution actions on the stakeholders' goals, the crime process, and the attacker. In order to reach this aim, we develop a multidisciplinary framework that combines existing knowledge from the fields of software engineering, crime science, risk assessment, and quantitative decision analysis. The framework is illustrated through an application to a case of identity theft.

Security and making trust is the first step toward development in both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, This enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using an receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.

Wireless sensor networks offer benefits in several applications but are vulnerable to various security threats, such as eavesdropping and hardware tampering. In order to reach secure communications among nodes, many approaches employ symmetric encryption. Several key management schemes have been proposed in order to establish symmetric keys. The paper presents an innovative key management scheme called random seed distribution with transitory master key, which adopts the random distribution of secret material and a transitory master key used to generate pairwise keys. The proposed approach addresses the main drawbacks of the previous approaches based on these techniques. Moreover, it overperforms the state-of-the-art protocols by providing always a high security level.

Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model measures the potential impact of a threat on assets given its occurrence probability. The attacks prediction model was integrated with our autonomous cloud intrusion detection framework (ACIDF) to raise early warnings about attacks to the controller so it can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments on DARPA 2000 dataset, the proposed prediction model has successfully fired the early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the auto response controller ample time to take preventive measures.