Artificial Intelligence enhanced Security Problems in Real-Time Scenario using Blowfish Algorithm
St. Peter's Engineering College (Autonomous), Hyderabad（圣彼得工程学院（自治），海得拉巴）；Lendi Institute of Engineering and Technology (A), JNTU GV, Vizianagaram, Andhra Pradesh, India（伦迪工程技术学院（A），JNTU GV，维齐亚纳加勒姆，安得拉邦，印度）
In a nutshell, "the cloud" refers to a collection of interconnected computing resources made possible by an extensive, real-time communication network like the internet. Because of its potential to reduce processing costs, the emerging paradigm of cloud computing has recently attracted a large number of academics. The exponential expansion of cloud computing has made the rapid expansion of cloud services very remarkable. Ensuring the security of personal information in today's interconnected world is no easy task. These days, security is really crucial. Models of security that are relevant to cloud computing include confidentiality, authenticity, accessibility, data integrity, and recovery. Using the Hybrid Encryption this study, we cover all the security issues and leaks in cloud infrastructure.
Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption
Sulaimani Polytechnic University（苏莱曼尼理工大学）；Charmo University（查尔莫大学）；Ministry of Education（教育部）；Hawazen Preparatory School for Girls（哈瓦森女子预备学校）；University of Kurdistan Hewler（埃尔比勒库尔德斯坦大学）
Data security is required when communications over untrusted networks takes place. Security tools such as cryptography and steganography are applied to achieve such objectives, but both have limitations and susceptible to attacks if they were used individually. To overcome these limitations, we proposed a powerful and secured system based on the integration of cryptography and steganography. The secret message is encrypted with blowfish cipher and visual cryptography. Finally, the encrypted data is embedded into two innocent cover images for future transmission. An extended analysis was made t…
This volume represents the proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop 2019, held on January 27, 2019 in Honolulu, Hawaii.
Through the years, the flow of Data and its transmission have increased tremendously and so has the security issues to it. Cryptography in recent years with the advancement of VLSI has led to its implementation of Encryption and Decryption techniques, where the process of translating and converting plaintext into cypher text and vice versa is made possible. In this paper, the review of various aspects of VLSI's implementation of encryption and decryption are covered. To systemize the material, the information about topics such as Private Key Encryption, Index Technique, Blowfish Algorithm, DNA…
University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pi…
Rochester Institute of Technology（罗切斯特理工学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；IBM Research（IBM研究院）
Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence to inform them of indicators of compromise, attack techniques, and preventative actions. Security analysts prepare threat analysis reports after investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Collectively known as cyber threat intelligence (CTI), the reports are typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing intrusion d…
The workshop will focus on the application of artificial intelligence to problems in cyber security. AICS 2020 emphasis will be on human-machine teaming within the context of cyber security problems and will specifically explore collaboration between human operators and AI technologies. The workshop will address applicable areas of AI, such as machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning and human machine interactions. Further, cyber security application areas with a particular emphasis on the characterization and deplo…
University of Maryland University College（马里兰大学学院分校）
Attacks to networks are becoming more complex and sophisticated every day. Beyond the so-called script-kiddies and hacking newbies, there is a myriad of professional attackers seeking to make serious profits infiltrating in corporate networks. Either hostile governments, big corporations or mafias are constantly increasing their resources and skills in cybercrime in order to spy, steal or cause damage more effectively. traditional approaches to Network Security seem to start hitting their limits and it is being recognized the need for a smarter approach to threat detections. This paper provide…
University of Florida（佛罗里达大学）；Worcester Polytechnic Institute（伍斯特理工学院）
Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design; specifically the design of digital and analog integrated circuits~(ICs), where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time-to-market, and the growing number of ubiquitous interconne…
This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced an…
Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
Dept. of Information Studies, Trine University（信息研究系，特林大学）；Dept. of Business Information Systems, Central Michigan University（商业信息系统系，中央密歇根大学）；Dept. of CS, Maharishi International University（计算机科学系， Maharishi 国际大学）
RealAI（瑞莱智慧）；Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；China Nanhu Academy of Electronics and Information Technology（中国电子科技南湖研究院）；Xi’an Jiaotong University（西安交通大学）；Tencent YouTu Lab（腾讯优图实验室）；China Construction Bank Fintech（中国建设银行金融科技公司）；RippleInfo（涟漪信息科技）
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competi…
The Cyber Security Cooperative Research Centre (CSCRC)（网络安全合作研究中心（CSCRC））；Penn State（宾夕法尼亚州立大学）；RMIT University（皇家墨尔本理工大学）；The University of Technology Sydney（悉尼科技大学）
The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversaria…
Computer security has been a concern for decades and artificial intelligence techniques have been applied to the area for nearly as long. Most of the techniques are being applied to the detection of attacks to running systems, but recent improvements in machine learning (for example, in natural language processing) have enabled the opportunity to process software and specifications to detect vulnerabilities in a system before it is deployed. This paper presents a survey of artificial intelligence techniques (including machine learning) to detect or repair security vulnerabilities before produc…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Open-Source Projects and Libraries are being used in software development while also bearing multiple security vulnerabilities. This use of third party ecosystem creates a new kind of attack surface for a product in development. An intelligent attacker can attack a product by exploiting one of the vulnerabilities present in linked projects and libraries. In this paper, we mine threat intelligence about open source projects and libraries from bugs and issues reported on public code repositories. We also track library and project dependencies for installed software on a client machine. We repres…
Dependable Artificial Intelligence with Reliability and Security (DAIReS): A Unified Syndrome Decoding Approach for Hallucination and Backdoor Trigger Detection
National Institute of Advanced Studies, Indian Institute of Science Campus（印度科学学院校园高等研究院）
Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security
Karolinska Institutet（卡罗林斯卡学院）；Democritus University of Thrace（色雷斯德谟克利特大学）；IntelliSolutions（智解公司）；HEDNO SA（HEDNO SA公司）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mayo Clinic（梅奥诊所）；Auburn University（奥本大学）
As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to red team this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to o…
Widespread interest in the emerging area of predictive analytics is driving industries such as manufacturing to explore new approaches to the collection and management of data provided from Industrial Internet of Things (IIoT) devices. Often, analytics processing for Business Intelligence (BI) is an intensive task, and it also presents both an opportunity for competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual Property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a…
Proof of Useful Intelligence (PoUI): Blockchain Consensus Beyond Energy Waste
School of Science and Technology（科学与技术学院）；Kwansei Gakuin University（片山大学）；School of Engineering & Computer Science（工程与计算机科学学院）；Victoria University of Wellington（威灵顿维多利亚大学）
Laboratory for Physical Sciences（物理科学实验室）；Booz Allen Hamilton（博思艾伦汉密尔顿）；National Security Agency（美国国家安全局）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Singapore Management University（新加坡管理大学）；Harvard University（哈佛大学）；Washington University in Saint Louis（圣路易斯华盛顿大学）
The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Additionally, adversaries continue to develop new attacks. Hence, AI methods are required to understand and protect the cyber domain. These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. In general, AI techniques are still not widely adopted in the real world. Reasons include: (1) a lack of certification of AI for s…
Macquarie University（麦考瑞大学）；King Saud University（沙特国王大学）；Victoria University（维多利亚大学）
Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artificial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automa…
Since the use of applications and communication tools has increased, one of the concerns of the responsible for network security has been to protect information and information systems, as well as to provide trust to end users for the use of information and communication technologies. Nowadays, attacks on the network have increased and undergone modifications, which make the task for traditional security devices difficult, being necessary to add the intelligence to face the new attacks generated in the network. Hence the need to incorporate Cyber Threat Intelligence (CTI) as a new component in…
University of Louisiana at Lafayette（路易斯安那大学拉法叶分校）；University of Regina（里贾纳大学）；Tampere University（坦佩雷大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber Threat Intelligence (CTI) has been presented as one of the proactive and robust mechanisms because of its automated cybersecurity threat prediction. Generally, CTI collects and analyses data from various sources e.g., online security forums, social media where cyber enthusiasts, analysts, even cybercriminals discuss cyber or computer security-related topics and…
Authentication plays a significant part in dealing with security in public and private sectors such as healthcare systems, banking system, transportation system and law and security. Biometric technology has grown quickly recently, especially in the areas of artificial intelligence and identity. Formerly, authentication process has depended on security measures like passcodes, identity fobs, and fingerprints. On the other hand, as just a consequence of these precautions, theft has increased in frequency. In response, biometric security was created, in which the identification of a person is ba…
An increasing number of cybersecurity incidents prompts organizations to explore alternative security solutions, such as threat intelligence programs. For such programs to succeed, data needs to be collected, validated, and recorded in relevant datastores. One potential source supplying these datastores is an organization's security incident response team. However, researchers have argued that these teams focus more on eradication and recovery and less on providing feedback to enhance organizational security. This prompts the idea that data collected during security incident investigations may…
Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack - the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing. Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure. Estab…
Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy providing a stage-by-stage operational understanding…
Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e.g. anomaly detection, to behavioral level cyber security modeling and inference. This contribution is based on a dataset named BRON which is amalgamated from public threat and vulnerability behavioral sources. We demonstrate how BRON can support prediction of related threat techniques and attack patterns. We also discuss other AI and ML uses of BRON to exploit its behavioral knowledge.
