Biblio

This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.

Cyber security is turning into a significant angle in each industry like in banking part, force and computerization segments. Servers are basic resources in these enterprises where business basic touch information is put away. These servers frequently join web servers in them through which any business information and tasks are performed remotely. Thus, clearly for a solid activity, security of web servers is extremely basic. This paper gives another testing way to deal with defenselessness appraisal of web applications by methods for breaking down and utilizing a consolidated arrangement of apparatuses to address a wide scope of security issues.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.

As information and communication technologies evolve every day, so does the use of technology in our daily lives. Along with our increasing dependence on digital information assets, security vulnerabilities are becoming more and more apparent. Passwords are a critical component of secure access to digital systems and applications. They not only prevent unauthorized access to these systems, but also distinguish the users of such systems. Research on password predictability often relies on surveys or leaked data. Therefore, there is a gap in the literature for studies that consider real data in this regard. This study investigates the password security awareness of 161 computer engineering students enrolled in a Linux-based undergraduate course at Ataturk University. The study is conducted in two phases, and in the first phase, 12 dictionaries containing also real student data are formed. In the second phase of the study, a dictionary-based brute-force attack is utilized by means of a serial and parallel version of a Bash script to crack the students’ passwords. In this respect, the /etc/shadow file of the Linux system is used as a basis to compare the hashed versions of the guessed passwords. As a result, the passwords of 23 students, accounting for 14% of the entire student group, were cracked. We believe that this is an unacceptably high prediction rate for such a group with high digital literacy. Therefore, due to this important finding of the study, we took immediate action and shared the results of the study with the instructor responsible for administering the information security course that is included in our curriculum and offered in one of the following semesters.

Android controls the majority of the global OS market. Android Open Source Project (AOSP) is a very complex system with many layers including the apps, the Application Framework, the middle-ware, the customized Linux kernel, and the trusted components. Although security is implemented in every layer, the Application Framework forms an important of the attack surface due to managing the user interface and permissions. Android security has evolved over the years. The security flaws that have been found in the Application Framework led to a redesign of Android permissions. Part of this evolution includes fixes to the vulnerabilities that are publicly released in the monthly Android security bulletins. In this study, we analyze the CVEs listed in the Android security bulletin within the last 6 years. We focus on the Android application framework and investigate several research questions relating to 1) the security relevant components, 2) the type and amount of testing information for the security patches, and 3) the adequacy of the tests designed to test these patches. Our findings indicate that Android security testing practices can be further improved by designing security bulletin update specific tests, and by improving code coverage of patched files.

Exploring the efficient vulnerability scanning and detection technology of various tools is one fundamental aim of network security. This network security technique ameliorates the tremendous number of IoT security challenges and the threats they face daily. However, among various tools, Shodan Eye scanning technology has proven to be very helpful for network administrators and security personnel to scan, detect and analyze vulnerable ports and traffic in organizations' networks. This work presents a simulated network scanning activity and manual vulnerability analysis of an internet-connected industrial equipment of two chosen industrial networks (Industry A and B) by running Shodan on a virtually hosted (Oracle Virtual Box)-Linux-based operating system (Kali Linux). The result shows that the shodan eye is a a promising tool for network security and efficient vulnerability research.

Nowadays, dynamic testing tools have significantly expedited the discovery of bugs in the Linux kernel. When unveiling kernel bugs, they automatically generate reports, specifying the errors the Linux encounters. The error in the report implies the possible exploitability of the corresponding kernel bug. As a result, many security analysts use the manifested error to infer a bug’s exploitability and thus prioritize their exploit development effort. However, using the error in the report, security researchers might underestimate a bug’s exploitability. The error exhibited in the report may depend upon how the bug is triggered. Through different paths or under different contexts, a bug may manifest various error behaviors implying very different exploitation potentials. This work proposes a new kernel fuzzing technique to explore all the possible error behaviors that a kernel bug might bring about. Unlike conventional kernel fuzzing techniques concentrating on kernel code coverage, our fuzzing technique is more directed towards the buggy code fragment. It introduces an object-driven kernel fuzzing technique to explore various contexts and paths to trigger the reported bug, making the bug manifest various error behaviors. With the newly demonstrated errors, security researchers could better infer a bug’s possible exploitability. To evaluate our proposed technique’s effectiveness, efficiency, and impact, we implement our fuzzing technique as a tool GREBE and apply it to 60 real-world Linux kernel bugs. On average, GREBE could manifest 2+ additional error behaviors for each of the kernel bugs. For 26 kernel bugs, GREBE discovers higher exploitation potential. We report to kernel vendors some of the bugs – the exploitability of which was wrongly assessed and the corresponding patch has not yet been carefully applied – resulting in their rapid patch adoption.

Operating systems are essential software components for any computer. The goal of computer system manu-facturers is to provide a safe operating system that can resist a range of assaults. APTs (Advanced Persistent Threats) are merely one kind of attack used by hackers to penetrate organisations (APT). Here, we will apply the MITRE ATT&CK approach to analyze the security of Windows and Linux. Using the results of a series of vulnerability tests conducted on Windows 7, 8, 10, and Windows Server 2012, as well as Linux 16.04, 18.04, and its most current version, we can establish which operating system offers the most protection against future assaults. In addition, we have shown adversarial reflection in response to threats. We used ATT &CK framework tools to launch attacks on both platforms.

Still in many countries COVID19 virus is changing its structure and creating damages in terms of economy and education. In India during the period of January 2022 third wave is on its high peak. Many colleges and schools are still forced to teach online. This paper describes how cyber security actionable or practical fundamental were taught by school or college teachers. Various cyber security tools are used to explain the actionable insight of the subject. Main Topics or concepts covered are MITM (Man In the Middle Attack) using ethercap tool in Kali Linux, spoofing methods like ARP (Address Resolution Protocol) spoofing and DNS (Domain Name System) spoofing, network intrusion detection using snort , finding information about packets using wireshark tool and other tools like nmap and netcat for finding the vulnerability. Even brief details were given about how to crack password using wireshark.

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

"Security first" is the most concerned issue of Linux administrators. Security refers to the integrity of data. The authentication security and integrity of data are higher than the privacy security of data. Firewall is used to realize the function of access control under Linux. It is divided into hardware or software firewall. No matter in which network, the firewall must work at the edge of the network. Our task is to define how the firewall works. This is the firewall's policies and rules, so that it can detect the IP and data in and out of the network. At present, there are three or four layers of firewalls on the market, which are called network layer firewalls, and seven layers of firewalls, which are actually the gateway of the agent layer. But for the seven layer firewall, no matter what your source port or target port, source address or target address is, it will check all your things. Therefore, the seven layer firewall is more secure, but it brings lower efficiency. Therefore, the usual firewall schemes on the market are a combination of the two. And because we all need to access from the port controlled by the firewall, the work efficiency of the firewall has become the most important control of how much data users can access. This paper introduces two types of firewalls iptables and TCP\_Wrappers. What are the differences between the use policies, rules and structures of the two firewalls? This is the problem to be discussed in this paper.

We demonstrate an in-house built Endpoint Detection and Response (EDR) for linux systems using open-sourced tools like Osquery and Elastic. The advantage of building an in-house EDR tools against using commercial EDR tools provides both the knowledge and the technical capability to detect and investigate security incidents. We discuss the architecture of the tools and advantages it offers. Specifically, in our method all the endpoint logs are collected at a common server which we leverage to perform correlation between events happening on different endpoints and automatically detect threats like pivoting and lateral movements. We discuss various attacks that can be detected by our tool.

Smart building security systems typically consist of sensors and controllers that monitor power operating systems, alarms, camera monitoring, access controls, and many other important information and security systems. These systems are managed and controlled through online platforms. A successful attack on one of these platforms may result in the failure of one or more critical intelligent systems in the building. In this paper, the security requirements in the application layer of any IoT system were discussed, in particular the role of IoT platforms in dealing with the security problems that smart buildings are exposed to and the extent of their strength to reduce the attacks they are exposed to, where an experimental platform was designed to test the presence of security vulnerabilities and This was done by using the Zed Attack Proxy (ZAP) tool, according to the OWASP standards and security level assessment, and the importance of this paper comes as a contribution to providing information about the most famous IoT platforms and stimulating work to explore security concerns in IoT-based platforms.

The latest generation of IoT systems incorporate machine learning (ML) technologies on edge devices. This introduces new engineering challenges to bring ML onto resource-constrained hardware, and complications for ensuring system security and privacy. Existing research prescribes iterative processes for machine learning enabled IoT products to ease development and increase product success. However, these processes mostly focus on existing practices used in other generic software development areas and are not specialized for the purpose of machine learning or IoT devices. This research seeks to characterize engineering processes and security practices for ML-enabled IoT systems through the lens of the engineering lifecycle. We collected data from practitioners through a survey (N=25) and interviews (N=4). We found that security processes and engineering methods vary by company. Respondents emphasized the engineering cost of security analysis and threat modeling, and trade-offs with business needs. Engineers reduce their security investment if it is not an explicit requirement. The threats of IP theft and reverse engineering were a consistent concern among practitioners when deploying ML for IoT devices. Based on our findings, we recommend further research into understanding engineering cost, compliance, and security trade-offs.

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

IoT technology is finding new applications every day and everywhere in our daily lives. With that, come new use cases with new challenges in terms of device and data security. One of such challenges arises from the fact that many IoT devices/nodes are no longer being deployed on owners' premises, but rather on public or private property other than the owner's. With potential physical access to the IoT node, adversaries can launch many attacks that circumvent conventional protection methods. In this paper, we propose Secure SoC (SecSoC), a secure system-on-chip architecture that mitigates such attacks. This include logical memory dump attacks, bus snooping attacks, and compromised operating systems. SecSoC relies on two main mechanisms, (1) providing security extensions to the compute engine that runs the user application without changing its instruction set, (2) adding a security management unit (SMU) that provide HW security primitives for encryption, hashing, random number generators, and secrets store (keys, certificates, etc.). SecSoC ensures that no secret or sensitive data can leave the SoC IC in plaintext. SecSoC is being implemented in Bluespec System V erilog. The experimental results will reveal the area, power, and cycle time overhead of these security extensions. Overall performance (total execution time) will also be evaluated using IoT benchmarks.

Recently, as the use of Internet of Things (IoT) devices has expanded, security issues have emerged. As a solution to the IoT security problem, PUF (Physical Unclonable Function) technology has been proposed, and research on key generation or device authentication using it has been actively conducted. In this paper, we propose a method to apply PUF-based device authentication technology to the Open Connectivity Foundation (OCF) open platform. The proposed method can greatly improve the security level of IoT open platform by utilizing PUF technology.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

The Domain Name System (DNS) is critical to Internet communications. EDNS Client Subnet (ECS), a DNS extension, allows recursive resolvers to include client subnet information in DNS queries to improve CDN end-user mapping, extending the visibility of client information to a broader range. Major content delivery network (CDN) vendors, content providers (CP), and public DNS service providers (PDNS) are accelerating their IPv6 infrastructure development. With the increasing deployment of IPv6-enabled services and DNS being the most foundational system of the Internet, it becomes important to analyze the behavioral and privacy status of IPv6 resolvers. However, there is a lack of research on ECS for IPv6 DNS resolvers.In this paper, we study the ECS deployment and compliance status of IPv6 resolvers. Our measurement shows that 11.12% IPv6 open resolvers implement ECS. We discuss abnormal noncompliant scenarios that exist in both IPv6 and IPv4 that raise privacy and performance issues. Additionally, we measured if the sacrifice of clients’ privacy can enhance IPv6 CDN performance. We find that in some cases ECS helps end-user mapping but with an unnecessary privacy loss. And even worse, the exposure of client address information can sometimes backfire, which deserves attention from both Internet users and PDNSes.

The spread of the Internet of Things (IoT) and cloud services leads to a request for secure communication between devices, known as zero-trust security. The authors have been developing CYber PHysical Overlay Network over Internet Communication (CYPHONIC) to realize secure end-to-end communication among devices. A device requires installing the client program into the devices to realize secure communication over our overlay network. However, some devices refuse additional installation of external programs due to the limitation of system and hardware resources or the effect on system reliability. We proposed new technology, a CYPHONIC adapter, to support these devices. Currently, the CYPHONIC adapter supports only IPv4 virtual addresses and needs to be compatible with general devices that use IPv6. This paper proposes the dual-stack CYPHONIC adapter supporting IPv4/IPv6 virtual addresses for general devices. The prototype implementation shows that the general device can communicate over our overlay network using both IP versions through the proposed CYPHONIC adapter.

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

Based on the campus wireless IPv6 network system, using WiFi contactless sensing and positioning technology and action recognition technology, this paper designs a new campus security early warning system. The characteristic is that there is no need to add new monitoring equipment. As long as it is the location covered by the wireless IPv6 network, personnel quantity statistics and personnel body action status display can be realized. It plays an effective monitoring supplement to the places that cannot be covered by video surveillance in the past, and can effectively prevent campus violence or other emergencies.